tool_use/05_tool_choice.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tool choice"
   ]
  },
  {
   "attachments": {
    "tool_choice.png": {
     "image/png": 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52Vn43kXybq7Fw8aNW7B27Tphu6GhFr+d9yAKCoZqS5SXleLfb74e9fW1+P0fnhFu7ry0aAnOO3catNrQyyEfo+/sQu2katxyyw2YPu1M2cG+0diDlStXC9sZGel45OF7ceYZU4XndDodLpg5AyeeUI9H5y0Iu/yc2Ej3fSjRXnuislptKCkpxrNPPyap01NXV4O6uhqcccap+NV/PSQs57Rs2Tu48opZ0IlqeGg0moB/B01NrZIE8NSpJ0v6OZy3//aeZFnBWZf8G+695w7JLJ+GhsGb+BMmVOD1JW8BGKyh85eX38D8eb8exidARETHK1ZxHQAsXfaOMLo8LS0NTy54NGDZ2rS0VNzxi9ugVqvxt+XvAxiM17Zu3YmpU08W9isqKgj4vbJarZIE8EUXnh+2NEQ8xTOuy8nJxs03XYvLvn8xUlLCL7UntsSv72+/bS7m3Dhb2FapVJgyZTKeWPAb/O/TL2DNmrUA5JXJGMm+D+V4rj0RjURcBwDTzjkj4NyLFr0OCwYTwJWVE2T/H4hxHRHR2KLXd8ElrhWbnYVJNVWSpGlmZgbqMybhcFMLjMYe2TNN2zv0wuxPlUqF+rpa6HQpkn1UKhUmTqiAQqGAXj84SMvU1w+z2YxM0dK5Wq0W+XnS322PxyNJAOfn5yXMPRu32y35PdSo1Zg0qRqZmUN17zUaNQoK8pGRkYHGxkMYODqjs8vQjYLCfFnLax9LCJaUFKGkuGhYca1/eYeK8jKUlhYL2wqFApkZGaivn4SmphYYe3ohd47xSPZ9KA6nExq1enCiRkG+7EGHierYv6v6uknIyhq6frVaBZ1Oh+zsLOzddwADA4PfE5OpD2aLBZkZGZLzBJug0traJiSAU1NTkS8zMdvX14/eXpOwnZ6ehtpJNZL/c6SkpKCsrARp6Wk4JFpJoKW1DTk52WO+X4iixW8+jRu7du3F3r0HhO1TpkzG7bfNDRqUFRTk4f777pQdsG3atBXNza3C9l2//GnIYEelUuFXd/2HsG22WPDpZ2vlXkZCevf9j4THCoUCv77/LslNQrFp55yBG0SzXo3GHqxZ83+y3icrKxO/nf8gZkw/a1jB9Mf//Fy44QQA1137o5BJwaKiAtx79x2yzx2vvo/22hPZLTdfJ7lJKFZfNwmXff9iYdtssWCP6N/vSLPb7fho1afCdllZCe6955cha8rdfNO1OOvMoe/Q1998i9bWtpi1j4iIpGIZ15ktFqz5fOj3ec6NV4esWQoAt/7kRsnv2YerPpH1PokqXnEdANzzXz/HVT+6fFgJ0O07dqOxcWh28plnTJUkf8W0Wg3uvfsOFBbmyzp3PPs+mmtPZIzriIgoWl6vV5JAValUqKmuDDpjVqFQoLpqYsQarce4PR70GIfKNJSWFAckAMUqysskSaWuCMvgJjqDwSiZhVleXiZJ/orpdCmorJLO+O3qNATdN5iS4iJMqCgfVvLXbLZIZnVmZ2VKkr9iSqUSVVUTZQ00BOLb95VVE1FUVJg0ScZjKw4Go1arA2rrRqqLfbw6/WaIV1dVhvwe5GRnobSkSNh2uVxJU6aHKBrJ8VeJSIat23ZKtufMuSbs/jU1lTj/vGmyzr3pu63C45KSYpx00gkRz11TM7Qs7dZtO2S9TyKy2QawefM2YfuCmTMiLhFz5ZWXSkaGbZBRXw4ArvrR5SguLhx2G8V9n5WViR/+8Pth96+rq8F5554j69zx6vtorz1RpaSk4OKLLwi7z0V+MzzEifaRtmPHHkkdkdlXXwG1OvzyTNddd5Xw2OfzDbu2JBERRS+Wcd32bbuEEe0AMPP86WH312g0kuWBt2zZEVXts0QQz7jutKlTcK7MeEtsm1/f33BD+HIaKSlaXDM7cLnhYOLV99Fee6JiXEdERMfDarXBLUpSFhUVhBy0A0BYmlYOs9kCj6iWbW5uYDko/3Pn5AztY45xYivW+vr6hMdarSbkoL5jMjMykCVKEJtk1FsFAKVCEXbQXCj+dXNLSoInf4X3USoTru+zMjORK1pSOhkUFIQfPJmXlytJdtsHYlcH2OPxSBLMebk5SA2ylLhYYVEh1KIBJHLqBhMlKy4BTeOGeJZIWloqzjzj1IjHTJ92JtZ+tS7ifnv3NQqPlUoFnnr6zxGP8XiGgpA9e2I3Aj7W9uzZL9RIA4Bp086MeExmRgZOPfUk/OvrDQCAHTt2y3qvysqKqNt4zJQpkwOWJQlm2rQzhfaFE6++j/baE1VFeWnEkZH+oz79/2Mwko7V1z5mxvSzQ+w55PTTTkFGRrpQl27Xrr2YffUVMWkfERFJxSuu02o1ePtv70U8pqOjU3hsMvXhyJF2TJhQHvG4RBPfuE5eTTl/4v5JT0/DaVOnRDxmxvSz8OJLrw3r3LHs+2ivPVExriMiouPhX/9dTjItJycbLa1HZJzbKjxWKhTQ6zvD7D3I4XAKj11uN+x2e0DZgrHA6/XCIvpss7OyZK2Ik5OTjf6jv9Nutxs220DE+rU6nS6q2a/i/lGpVCFnnfq3r0XGSh3x6vtgdY3HupSU0DOlh/bRCgMn3R55pVaiYbXaJAMsc2T8fVCrVMjMzECvaXAARCzjTqJExwQwjRu9vUOj3srKSmQFPaGWHfFnNPYIj9vb9Whv1w+rbT09vfD5fGNyad9uo3RJFPEyG+GUiPYz9fVjYMAecQRXNDNgrVabZCaH3PaVJVjfJ9PsXwAoLAq+RKBYXm4OtFqNsHy3y+WOcET0xP2o1WpkLxVZXlaCffsHa0oausf20lBERGNJbOO6oSXCnE4XVq3+bPjtM/WNyQRwPOO6IhmxQDAm01DfV8icaVJcXAi1Wi3UgAslXn0f7bUnKsZ1RER0PFxul2RbTvJJq9VAqVDAG2HlDZeoHJjX54vq77vL5cYYzP/C5XJLEmdyPlcA0Prt53S5kIbwCWBtlCUtxHWfdXLbp9VCoVBEXHUlXn2fInNJ6rFCpVRCo4mcMtJqhxLAPm/sVj9yOp2SbbnlU8Tfd7fbDa/XmzRLdBMNB7/1NG6YLUOjfYqL5CXTioexrMjx8Hg8ASMexwqLRdruApk3WAr9lhOR8xkGq/8SybFR/KHeNxS5Cdd49X00157I1DKuR6FQxO26xd+TwsIC2YMxCkS17jiikIgofmIZ14lnC0RrrP4mJHpcB0h/swtC1JwN9l5yvifx6nvGdbHFuI6IaGzxiFYfUalUYZd/PkahUMhKOnpGYGbiSJxjNHg80sFWGpmJSq1fDV9PhAF0ABDtdBbxZyu3tq9CoUCKjBrQ8er7sTiZJyyZ1xOv6/bvA41GXgLY//seaSAoUbLiDGAaN3S6oZE/fTLrOMitEZCenibcaJg160LcfuvcYbcvPT1t2MckAv/ZHX19Zln1OPz7IDUtNsMpA9ons+97RbNLwhnPfZ9MxH8fTDL7HpD+jUhLwmV/iIgSVSzjOnHsMHFCORY+OX94jQOQmRm53EQiSvS4DpC2UW6f+nw+9JpMwzr3eOv7ZMK4johobFGqhuYneTwe2TP13DJWk1CKBh/pdDo01NcOu32R6sgnKqVS2m65CTD//ZQxHMAl7ufhJOhcLlfEfcZz3ycT/++f2+2WNQs4nt9jokTGBDCNG+I6Eu3tHbKOEdf0Cic3NwednQYAQGenAQUFecNv4BiVnZ0l2e7s7EJD/aSIx+n1XcLjlBQtMtLTR7xtwOBNOJVKJYwY65BR8wMA2tvZ9+NJtqiGiNVqQ19ff8B3O5gO0ZLfcuqQEBHRyIhlXCf+e97eoUdmZobspcbGukSP64Do+t5o7IXNNhBxv/Hc98mEcR0R0diiVklvTzsczoilJNxut6y6oxrRbGKHwwG1WjVuloH1X8bXfyndUBx++2lkzMiOlni2t93hkHWM0+mCx+uNuN947vtk4v/9czqdsibSOEX1nJVKpawVa4iS0f8DAAD//+zde3xcZZ3H8e/cMpPJpE2b+625N4WWS5HKpe5aYKFlKQKlgAgWZV+yKl5AXJSrosAiIrquAq6uwlIEykV00YIUkFUEWiill9A2UNqmySTNpWkuk0wyk9k/Yk9zMmlmIjOTZObzfr36es1zOPPkIb8k5zfn95zn4a8eUkZlRZnxuqOjUw0NjRHfU/fujqj6HjmL7L33dskfZdLy9xqdjE2mebXmGXTb6iJ/z4LBoLbvqDfa84+eF7elQywWi6qrK4z29u31CgQif0jYtm17VP0nOvaIj3mjZoJujSL+DQ2Nau84vFfgvHk1MR8XAGBs8czr5tUe/nseCJhzlnjx+6dGbjfV8zpJqhgV+71RxH7r1nej6nsyYo/YI68DgOlldLF39FZeY4nmHMm84looFErI9mtDURQnE8Fms5m+t9F+z0aeZ7Va5XaPv//vh+FOP9z34GBA/f39Ed/T0xPdNg2TEXvE3uhibzQ/x6FQSD0j4u3xxG9yKjDVUQBGyjjmmKNN7cfXPDPu+QcPdukPa9dF1fcJJxxrvO7t9empp5+N+J5QKKShoSHjXyRZWYdnrbe1tkc1rkTIzp6tmppKo7127bqIe2a99PJf1DRihv3ChcfEbXySdMyCo4zXXm+L1r34yrjnt7a267nnX4qq70TEHvF3/PELTDNPn3zydxHf88SocxYeH9+fYwDAYfHM6447br7pmvDoY09H9b4J5XUzzU8XtrV1RPU14m065HXHjsjrJOnxx38z7vmhUEiPrRn/nEMSEXvEH3kdAEwvmZke0+Sx5pb9CoVC475n5Ooj4/edaeo72lXhQqGQ8S+S8CcUIy9PnCgjV07p7fWpK8LWKX6/Xx0jJkRlejxxfWrWM2rrDG8UcY02homIPeLP4XCYJiG0trVHfLCno+OA6QGdGWzRghRGARgp46SPnmBanveFdX/Sxrc3j3nu0NCQfv6Lh6OeHXbKySequKjQaK9Z81vt2dNwxPMDgaBuufUunbl0pc5culLfuOE7Eb9GYWGB8bq944DeWL8xqrElwrnLlxqve3t9euBnDxpLLo/W3LJfqx95wmg7nU6dveyMuI7vrLNOM7V//eunjGWbRxscHNT9D/wq6id5ExF7xN+sWVla8vHFRnvzlrpxCwUb396sF9b9yWjX1laHFSMAAPETz7xu1qwsnbbk8DVhw4a3I04e+/WjTxnX9vNXrJLPN/7XKiouMLVHXlMm21TP60466SPKzc0x2utefEUbN44de2m4sFdfvyuqvhMRe8QfeR0ATC92u9209H5/f/+4W3e0trWrO8qnQB0Ou2bPnmW0Dx7sUnv7+BPvvN5mvfnWJr351ia9vWnLEfOgQ5xOp6kdqf9EyhuRM0nSvn1NR9xrd2hoSHsbGk2Fz7y8nDHPjZWsmTOUluYw2u3tHeMWqZub90e1rYeUmNgjMfJyc43XwWBQDfsaj1ig9/sH1OQ9PDnVarUqJye+P8fAVEYBGCnD4XDowhXnGu1AIKibbr5Dv//DC6bZ+l1d3frOd+/R2udejLpvm82mK1ZdYrS7e3r0lWtu1Msv/yUssaqr26EbbvyuXnt9g3FsxQXnRPwaVZVlpvYdd96rh1ev0fr1G1Vfv0vv79qt93ftnpQnSM78pyWqrjq8zPJzz7+kb3377rDEatOmrbrm2pu0b1+TcWzlhedq1qysuI6vqrJc//Cxk412Y5NXX732Rm3ZUmc6r7W1TTfceLte+b+/yhbl3hCJiD0S4/LLVppmFf7g3vv0qwcfNS1BFAgE9eyzf9RNN99hmtX72c98KqFjBYBUF8+8TpIuv+wiud2Hlxu763s/1oMPPRpWRG5padUP/+MB/fcvHzGOLT/nLNN7x1JSXGhazuzxNc/onnvv06uvvqHt2+uNvC6apa1jbarndXa7XRet/ITRDgSCuumW8Nj39/frp/f9Uj/7r4ckKercLt6xR2KQ1wHA9FJQkGd6WrOxyas9expM1/ZQKKQmb7N27947ob6LCgtMecCuD/aosdEbVtwbGBjQ7j0N2tfoNY7l5mZHzCFcLqfpHG9zi3bv3qsDnQfV2+uTz9cnn68vquWNY83lcik3N9to9/p8end7fVhe4/f7tXPn++rsPGgcy8z0mArz8WCxWFSQn2e0Q6GQ6uvfV2tru6nAN1yc3qeGfY3G+6IR79gjMbKzZ5nyura2dr33/gcaHDQ/bd/d3a3tO3aqv//wQz0F+Xlh+2EDqYSffqSUlReeq9ffeEvvvLNV0vCyLPf+8H49vHqNyuaUamBgQDt2vmfsw7Z48Ul69dU3our7jDP+UW+/s1Vr/za7vKenV7ffea+czjTVVFfKYrWqqdFr2ltKGi4AnnLyooj9H3fcAp16yiL99bXh4mFvr08PPvRY2HkXrliuL37hyqjGHCtpaQ5df/2Xdd11txqzMF97fYPWX7ZRlZVl8mRkqLllf9gMzmOPOVqrPn3JWF3G3Fe+fJXq3t1p3LxsbW3XNV+7WWVlpcrPz1VPd4927HzfSAKXn3Omfvu756LqO96xR2KUlhbri1+4Uvf84KfGsdWPPKGnf/N7VVTMkc1q1d6GRtMHIkm65OLztejE4xM9XABIefHM60pKivSlq6/U3d//iaThm1EPr35Cqx95UhUVc5SVNVPNzcO5zcibU0fNm6vPfubSiP27XC5dftlFRnEyFApp7dp1Ri5xSE1NpR64756oxhwr0yGvW3HBOXr9jTeNJ39Hxz4QDKi+fpdxc3PJksXasrkuLBcbS7xjj8QgrwOA6cWTkaGCgjxTfrG/tU3tHQeU4U6XLBb19fVpcHB4or3HkyG/fyCsADQWl8upOaXF+mBE4bjJ26wmb7Pc6emyO+zy+wfCVoLLyHCrpLgoYv9Wq1VFhQVGcVIafkq5tc28fZvbna75R8+L2F+szSktMQrR0vAkubp3d8jlciotLU2BwYB8feanah0OhyrKy8bqLuby8/PUebDLePJ3KBTS7j171eT1Kt3lGt6/19dn3K+bPXuWurt7pkTskRhWq1UV5WXasaNegb/9HHR2HtQ7B7uUnu6S3WaXf8BvfO47JNPjUVFRwVhdAimDJ4CRUqxWq2771vVaMN+ccLW2tuvNtzZp85Y642Jx6imL9NkrPjmh/r92zed13ieWmY75/QPaum27tmwJv+l02mkf01WfWxV1/1df/S+qnVs1oTElSlVlub53162m5RiDwaDq63fp7U1bwm4SLlq0UHfcfpPs9sTMpps9O0t33/Ut08xCSdqzp0Hr129U3bs7jWRy2dLTTcsfRiPesUdinL3sDH39uqtNSxD5fD5t27Zdm7fUhd0k/NSlFxJHAJgk8c7rlp51ur5+3dVyOtOMY6FQSLt27dHGjZvV1NRsKgBWV1folpuvk8PhGKu7MCsuWK4lI5Ybnkqmel5nsVj07Vuv1wkLjzUdPxT7TZu2GsXfmppKXfvVz0+o/3jHHolBXgcA00tJcVHYksXBYFBd3T3q6uo2ir8up1NVlRWK8iFQSVJOTrYqyueE7Wfr6+tTV1d3WAHQ7U5XdVVF1E+a5ufnmpYbnkqsVqtq51Yr05NhOt7f71dXV3dY8dfldKq2ttqUB8VbdVWFab9iaXiC38GubnV19xj369zudJWXlU6o73jHHonhdqdr7txqpY3It0OhkHy+PnV1d4cVf2fOnKGamkriiJTHE8BIOZmZHn3/7tu05oln9NTTz4btLeF2p+vST67QxRedp8YRS39IUqRLhtVq1Ve+fJUWn3qSVj/yhLZsfXfMPQlq51bp4ovOn/BNv4L8PP3kP7+n3//hBa1fv3F41lpTs2nJsslUW1utX/z8R1r9yJNa98Kf1HmwK+ycivI5WrnyE1p61mkJvwiXl5fq/vu+r/95+HE9/8eXw/YNycqaqStWXaLl55ylvXsntuRivGOPxDl72RmaP3+eHnzwUb32+oaw3y+LxaKFxx+jK1ZdogULjpqkUQIApPjmddLwNeGYBUfpVw89qtde2xB2Y0EavuF37vKlWnHBOWF7wI3Hbrfplpuu09nLztDa516Ut6lZ+xq9Ue9VHG9TPa/LyHDr3++8Rb/93VqteeKZsG1Q7Ha7zj/vbH368ovlGXXDMxrxjD0Sh7wOAKaXsrJSZWZ61NjoVf+owpw0XMwrKS6Sw2HXEbYAPaKcnGx5PB41NnnV2XnQtLz0IWlpacrLzVF+fm5YwXA8FotFVZXlysmZrba2Dvn7/er3+6fMHrJ2u121tTVqbW1Ty/5W0xK5hzgcDuXmZKuwMH9C/++xYLPZNLemSvv3t6q5eb8GBsOv1/l5uSoqKvi7lmWOZ+yROBkZbs1fcJS8Tc1qa+8Ycz/r9HSXCvLzlJOTPUYPQOqxdB5oneDlEhPR1rL3iJuSJ6uQ36e+Df8b8TxbTqmc806Nut+h0JD2Ne77MEMLMzg4qE2btqqxyauhoZAqKuaoprrSuEn01lvv6Ppv3mac/+Mf3an586NfrqW1tU0763fpQEenLFaLsmbOUE1NlfLykn/z+UAgqG1127V/f5t8Pp+ysmaqbE6pyssnNlMvXvr7+7Vl63a1trbJ4XCorKxEZXNKYzbDMZVjn0x6enpVV7dD7R0HFAwENTt7lmrnVik7e3bkN0/QnJI5Ec/pe/NZhfp7I57nXnyxJjQdOkllzsiWy+2Z7GEgiXR1tskfxe9gMgo01Wtg18aI5znnnSJbTuS/Z5K0d9/E9m+LJN55nc/n07a6nWpra9fAwKCysmaopKRIlRVlST+zfKrndaFQSNvqdqip0avBQEClpcWqqJijTE9srgGpHPtkksi8rrS4NKqfjaGeA+rf9MeI59nzypU296RYDG3acroyNCOLz1KInX5ft7q7OiKfiKgMDA6MWVT7MHp6etXX169gMCiXyym3222s6hAKhfTmW5uMc0uKi1RYmB9138FgUD09vRocHNTQUEh2h10up9O0z2gyO7QncSAQkM1mk9PpVEaGe8rkNT09ver3+xUaCsnlcirdnS57jPbjTfXYJ4tQKKSenl4NDAwoGAzK7nAo3eVSeror5l/LmeacMr8bycJisSonf2p8lkx2PAGMlOZwOLRo0UIt0sIx/3tzy35TO2vWzAn1n5ubo9zc1PyQarfbdNyx8yd7GEfkcrniur9XKsc+mXg8GfroR0+Y7GEAAKIQ77zO7Xan7N6gUz2vs1gsWjB/Xthy4LGSyrFPJuR1ADC9eDwZR1zFY/SyvQ7HxG5x22w2zZw54+8e23TndqdP6YLneLH/sFI99snCYrEoM5MJ/0AkFICBcWzY8LbxOitrpooKp9/G8QcOdCoYjN0sTLfbJbfbHbP+ED/EHgCAw8jrwnFtnz6IPQAAhx0ctTXFdLymBYPBmF7brVaL7HZu9U8HxB5AovCXASnj0L65h1z1uVUqLi484vnbtm3Xn//yutE+8SPHT8vlHm646XbV1++KWX+rPn2Jrlh1Scz6Q/wQewBAsiKviw2u7dMHsQcAJKuenl41N7cY7eycbM3KOvJKLcFgUN7mwyu7OJ1pcVn2Nd4am7xqaWmNWX8zMjNVW1sds/4QP8QeQKJQAEbKKCkp0r0/vN9o79j5vq7+4pX62OKTwm4AvvTyn/XT+35pOnbeeWcnZJwAAAAYH3kdAABAckhPd6m7u0eBYFDS8NO9xcVFysvLkdVqNZ3b2+vT7t17NTg4aBzLzc2ZlhP7AACINwrASBnHHTtfy5aerueef0mS1Nrapm/fdrcKCvJVXlainJxstbV3aM+eBnm9Lab3nn/eP+voo+ZOxrA/tKrKcnkyYrdvRl5udsz6QnwRewBAsiKviw2u7dMHsQcAJCubzabS0mJ9sHuvJGkoFFLDvkZ5m1vkdqfLmZamYDCovv5+9ff7FQqFjPdmZLhVkJ83WUP/UJxpTs3IzIxZf9PxKehURewBJAoFYKSUa6/5vILBoF5Y94pxrLm5xbTUzGinn/YP+terrkjE8OLi377+pckeAiYJsQcAJDPyOqQSYg8ASGY5OdkKBofUsK/RKPA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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Claude API supports a parameter called `tool_choice` that allows you to specify how you want Claude to call tools. In this notebook, we'll take a look at how it works and when to use it.\n",
    "\n",
    "When working with the `tool_choice` parameter, we have three possible options: \n",
    "\n",
    "* `auto` allows Claude to decide whether to call any provided tools or not.\n",
    "* `any` tells Claude that it must use one of the provided tools, but doesn't force a particular tool.\n",
    "* `tool` allows us to force Claude to always use a particular tool.\n",
    "\n",
    "\n",
    "This diagram illustrates how each option works: \n",
    "\n",
    "![tool_choice.png](attachment:tool_choice.png)\n",
    "\n",
    "Let's take a look at each option in detail. We'll start by importing the Anthropic SDK:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from anthropic import Anthropic\n",
    "client = Anthropic()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Auto\n",
    "\n",
    "Setting `tool_choice` to `auto` allows the model to automatically decide whether to use tools or not.  This is the default behavior when working with tools if you don't use the `tool_choice` parameter at all.\n",
    "\n",
    "To demonstrate this, we're going to provide Claude with a fake web search tool. We will ask Claude questions, some of which would require calling the web search tool and others which Claude should be able to answer on its own.\n",
    "\n",
    "Let's start by defining a tool called `web_search`.  Please note, to keep this demo simple, we're not actually searching the web here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "def web_search(topic):\n",
    "    print(f\"pretending to search the web for {topic}\")\n",
    "\n",
    "web_search_tool = {\n",
    "    \"name\": \"web_search\",\n",
    "    \"description\": \"A tool to retrieve up to date information on a given topic by searching the web\",\n",
    "    \"input_schema\": {\n",
    "        \"type\": \"object\",\n",
    "        \"properties\": {\n",
    "            \"topic\": {\n",
    "                \"type\": \"string\",\n",
    "                \"description\": \"The topic to search the web for\"\n",
    "            },\n",
    "        },\n",
    "        \"required\": [\"topic\"]\n",
    "    }\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we write a function that accepts a `user_query` and passes it along to Claude, along with the `web_search_tool`. \n",
    "\n",
    "We also set `tool_choice` to `auto`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tool_choice={\"type\": \"auto\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the complete function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import date\n",
    "\n",
    "def chat_with_web_search(user_query):\n",
    "    messages = [{\"role\": \"user\", \"content\": user_query}]\n",
    "\n",
    "    system_prompt=f\"\"\"\n",
    "    Answer as many questions as you can using your existing knowledge.  \n",
    "    Only search the web for queries that you can not confidently answer.\n",
    "    Today's date is {date.today().strftime(\"%B %d %Y\")}\n",
    "    If you think a user's question involves something in the future that hasn't happened yet, use the search tool.\n",
    "    \"\"\"\n",
    "\n",
    "    response = client.messages.create(\n",
    "        system=system_prompt,\n",
    "        model=\"claude-3-sonnet-20240229\",\n",
    "        messages=messages,\n",
    "        max_tokens=1000,\n",
    "        tool_choice={\"type\": \"auto\"},\n",
    "        tools=[web_search_tool]\n",
    "    )\n",
    "    last_content_block = response.content[-1]\n",
    "    if last_content_block.type == \"text\":\n",
    "        print(\"Claude did NOT call a tool\")\n",
    "        print(f\"Assistant: {last_content_block.text}\")\n",
    "    elif last_content_block.type == \"tool_use\":\n",
    "        print(\"Claude wants to use a tool\")\n",
    "        print(last_content_block)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with a question Claude should be able to answer without using the tool:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Claude did NOT call a tool\n",
      "Assistant: The sky appears blue during the day. This is because the Earth's atmosphere scatters more blue light from the sun than other colors, making the sky look blue.\n"
     ]
    }
   ],
   "source": [
    "chat_with_web_search(\"What color is the sky?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we ask \"What color is the sky?\", Claude does not use the tool.  Let's try asking something that Claude should use the web search tool to answer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Claude wants to use a tool\n",
      "ToolUseBlock(id='toolu_staging_018nwaaRebX33pHqoZZXDaSw', input={'topic': '2024 Miami Grand Prix winner'}, name='web_search', type='tool_use')\n"
     ]
    }
   ],
   "source": [
    "chat_with_web_search(\"Who won the 2024 Miami Grand Prix?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we ask \"Who won the 2024 Miami Grand Prix?\", Claude uses the web search tool! \n",
    "\n",
    "Let's try a few more examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Claude did NOT call a tool\n",
      "Assistant: The Los Angeles Rams won Super Bowl LVI in 2022, defeating the Cincinnati Bengals by a score of 23-20. The game was played on February 13, 2022 at SoFi Stadium in Inglewood, California.\n"
     ]
    }
   ],
   "source": [
    "# Claude should NOT need to use the tool for this:\n",
    "chat_with_web_search(\"Who won the Superbowl in 2022?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Claude wants to use a tool\n",
      "ToolUseBlock(id='toolu_staging_016XPwcprHAgYJBtN7A3jLhb', input={'topic': '2024 Super Bowl winner'}, name='web_search', type='tool_use')\n"
     ]
    }
   ],
   "source": [
    "# Claude SHOULD use the tool for this:\n",
    "chat_with_web_search(\"Who won the Superbowl in 2024?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Your prompt matters!\n",
    "\n",
    "When working with `tool_choice` set to `auto`, it's important that you spend time to write a detailed prompt.  Often, Claude can be over-eager to call tools.  Writing a detailed prompt helps Claude determine when to call a tool and when not to.  In the above example, we included specific instructions in the system prompt:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "system_prompt=f\"\"\"\n",
    "    Answer as many questions as you can using your existing knowledge.  \n",
    "    Only search the web for queries that you can not confidently answer.\n",
    "    Today's date is {date.today().strftime(\"%B %d %Y\")}\n",
    "    If you think a user's question involves something in the future that hasn't happened yet, use the search tool.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forcing a specific tool\n",
    "\n",
    "We can force Claude to use a particular tool using `tool_choice`.  In the example below, we've defined two simple tools: \n",
    "* `print_sentiment_scores` - a tool that \"tricks\" Claude into generating well-structured JSON output containing sentiment analysis data.  For more info on this approach, see [Extracting Structured JSON using Claude and Tool Use](https://github.com/anthropics/anthropic-cookbook/blob/main/tool_use/extracting_structured_json.ipynb) in the Anthropic Cookbook.\n",
    "* `calculator` - a very simple calculator tool that takes two numbers and adds them together .\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "tools = [\n",
    "    {\n",
    "        \"name\": \"print_sentiment_scores\",\n",
    "        \"description\": \"Prints the sentiment scores of a given tweet or piece of text.\",\n",
    "        \"input_schema\": {\n",
    "            \"type\": \"object\",\n",
    "            \"properties\": {\n",
    "                \"positive_score\": {\"type\": \"number\", \"description\": \"The positive sentiment score, ranging from 0.0 to 1.0.\"},\n",
    "                \"negative_score\": {\"type\": \"number\", \"description\": \"The negative sentiment score, ranging from 0.0 to 1.0.\"},\n",
    "                \"neutral_score\": {\"type\": \"number\", \"description\": \"The neutral sentiment score, ranging from 0.0 to 1.0.\"}\n",
    "            },\n",
    "            \"required\": [\"positive_score\", \"negative_score\", \"neutral_score\"]\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"calculator\",\n",
    "        \"description\": \"Adds two number\",\n",
    "        \"input_schema\": {\n",
    "            \"type\": \"object\",\n",
    "            \"properties\": {\n",
    "                \"num1\": {\"type\": \"number\", \"description\": \"first number to add\"},\n",
    "                \"num2\": {\"type\": \"number\", \"description\": \"second number to add\"},\n",
    "            },\n",
    "            \"required\": [\"num1\", \"num2\"]\n",
    "        }\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our goal is to write a function called `analyze_tweet_sentiment` that takes in a tweet and uses Claude to print a basic sentiment analysis of that tweet.  Eventually we will \"force\" Claude to use the `print_sentiment_scores` tool, but we'll start by showing what happens when we **do not** force the tool use. \n",
    "\n",
    "In this first \"bad\" version of the `analyze_tweet_sentiment` function, we provide Claude with both tools. For the sake of comparison, we'll start by setting `tool_choice` to `auto`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tool_choice={\"type\": \"auto\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please note that we are deliberately not providing Claude with a well-written prompt, to make it easier to see the impact of forcing the use of a particular tool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze_tweet_sentiment(query):\n",
    "    response = client.messages.create(\n",
    "        model=\"claude-3-sonnet-20240229\",\n",
    "        max_tokens=4096,\n",
    "        tools=tools,\n",
    "        tool_choice={\"type\": \"auto\"},\n",
    "        messages=[{\"role\": \"user\", \"content\": query}]\n",
    "    )\n",
    "    print(response)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what happens when we call the function with the tweet `Holy cow, I just made the most incredible meal!`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ToolsBetaMessage(id='msg_staging_01ApgXx7W7qsDugdaRWh6p21', content=[TextBlock(text=\"That's great to hear! I don't actually have the capability to assess sentiment from text, but it sounds like you're really excited and proud of the incredible meal you made. Cooking something delicious that you're proud of can definitely give a sense of accomplishment and happiness. Well done on creating such an amazing dish!\", type='text')], model='claude-3-sonnet-20240229', role='assistant', stop_reason='end_turn', stop_sequence=None, type='message', usage=Usage(input_tokens=429, output_tokens=69))\n"
     ]
    }
   ],
   "source": [
    "analyze_tweet_sentiment(\"Holy cow, I just made the most incredible meal!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Claude does not call our `print_sentiment_scores` tool and instead responds directly with:\n",
    "> \"That's great to hear! I don't actually have the capability to assess sentiment from text, but it sounds like you're really excited and proud of the incredible meal you made\n",
    "\n",
    "Next, let's imagine someone tweets this: `I love my cats! I had four and just adopted 2 more! Guess how many I have now?`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ToolsBetaMessage(id='msg_staging_018gTrwrx6YwBR2jjhdPooVg', content=[TextBlock(text=\"That's wonderful that you love your cats and adopted two more! To figure out how many cats you have now, I can use the calculator tool:\", type='text'), ToolUseBlock(id='toolu_staging_01RFker5oMQoY6jErz5prmZg', input={'num1': 4, 'num2': 2}, name='calculator', type='tool_use')], model='claude-3-sonnet-20240229', role='assistant', stop_reason='tool_use', stop_sequence=None, type='message', usage=Usage(input_tokens=442, output_tokens=101))\n"
     ]
    }
   ],
   "source": [
    "analyze_tweet_sentiment(\"I love my cats! I had four and just adopted 2 more! Guess how many I have now?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Claude wants to call the calculator tool:\n",
    "\n",
    "> ToolUseBlock(id='toolu_staging_01RFker5oMQoY6jErz5prmZg', input={'num1': 4, 'num2': 2}, name='calculator', type='tool_use')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly, this current implementation is not doing what we want (mostly because we set it up to fail). \n",
    "\n",
    "So let's force Claude to **always** use the `print_sentiment_scores` tool by updating `tool_choice`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tool_choice={\"type\": \"tool\", \"name\": \"print_sentiment_scores\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to setting `type` to `tool`, we must provide a particular tool name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze_tweet_sentiment(query):\n",
    "    response = client.messages.create(\n",
    "        model=\"claude-3-sonnet-20240229\",\n",
    "        max_tokens=4096,\n",
    "        tools=tools,\n",
    "        tool_choice={\"type\": \"tool\", \"name\": \"print_sentiment_scores\"},\n",
    "        messages=[{\"role\": \"user\", \"content\": query}]\n",
    "    )\n",
    "    print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now if we try prompting Claude with the same prompts from earlier, it's always going to call the `print_sentiment_scores` tool:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ToolsBetaMessage(id='msg_staging_018GtYk8Xvee3w8Eeh6pbgoq', content=[ToolUseBlock(id='toolu_staging_01FMRQ9pZniZqFUGQwTcFU4N', input={'positive_score': 0.9, 'negative_score': 0.0, 'neutral_score': 0.1}, name='print_sentiment_scores', type='tool_use')], model='claude-3-sonnet-20240229', role='assistant', stop_reason='tool_use', stop_sequence=None, type='message', usage=Usage(input_tokens=527, output_tokens=79))\n"
     ]
    }
   ],
   "source": [
    "analyze_tweet_sentiment(\"Holy cow, I just made the most incredible meal!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Claude calls our `print_sentiment_scores` tool:\n",
    "\n",
    "> ToolUseBlock(id='toolu_staging_01FMRQ9pZniZqFUGQwTcFU4N', input={'positive_score': 0.9, 'negative_score': 0.0, 'neutral_score': 0.1}, name='print_sentiment_scores', type='tool_use')\n",
    "\n",
    "Even if we try to trip up Claude with a \"Math-y\" tweet, it still always calls the `print_sentiment_scores` tool:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ToolsBetaMessage(id='msg_staging_01RACamfrHdpvLxWaNwDfZEF', content=[ToolUseBlock(id='toolu_staging_01Wb6ZKSwKvqVSKLDAte9cKU', input={'positive_score': 0.8, 'negative_score': 0.0, 'neutral_score': 0.2}, name='print_sentiment_scores', type='tool_use')], model='claude-3-sonnet-20240229', role='assistant', stop_reason='tool_use', stop_sequence=None, type='message', usage=Usage(input_tokens=540, output_tokens=79))\n"
     ]
    }
   ],
   "source": [
    "analyze_tweet_sentiment(\"I love my cats! I had four and just adopted 2 more! Guess how many I have now?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even though we're forcing Claude to call our `print_sentiment_scores` tool, we should still employ some basic prompt engineering to give Claude better task context:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze_tweet_sentiment(query):\n",
    "\n",
    "    prompt = f\"\"\"\n",
    "    Analyze the sentiment in the following tweet: \n",
    "    <tweet>{query}</tweet>\n",
    "    \"\"\"\n",
    "    \n",
    "    response = client.messages.create(\n",
    "        model=\"claude-3-sonnet-20240229\",\n",
    "        max_tokens=4096,\n",
    "        tools=tools,\n",
    "        tool_choice={\"type\": \"auto\"},\n",
    "        messages=[{\"role\": \"user\", \"content\": prompt}]\n",
    "    )\n",
    "    print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Any\n",
    "\n",
    "The final option for `tool_choice` is `any`, which allows us to tell Claude, \"You must call a tool, but you can pick which one.\"  Imagine we want to create a SMS chatbot using Claude.  The only way for this chatbot to actually \"communicate\" with a user is via SMS text message. \n",
    "\n",
    "In the example below, we make a very simple text-messaging assistant that has access to two tools:\n",
    "* `send_text_to_user` - sends a text message to a user.\n",
    "* `get_customer_info` - looks up customer data based on a username.\n",
    "\n",
    "The idea is to create a chatbot that always calls one of these tools and never responds with a non-tool response.  In all situations, Claude should either respond back by trying to send a text message or calling `get_customer_info` to get more customer information. To ensure this, we set `tool_choice` to `any`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tool_choice={\"type\": \"any\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "def send_text_to_user(text):\n",
    "    # Sends a text to the user\n",
    "    # We'll just print out the text to keep things simple:\n",
    "    print(f\"TEXT MESSAGE SENT: {text}\")\n",
    "\n",
    "def get_customer_info(username):\n",
    "    return {\n",
    "        \"username\": username,\n",
    "        \"email\": f\"{username}@email.com\",\n",
    "        \"purchases\": [\n",
    "            {\"id\": 1, \"product\": \"computer mouse\"},\n",
    "            {\"id\": 2, \"product\": \"screen protector\"},\n",
    "            {\"id\": 3, \"product\": \"usb charging cable\"},\n",
    "        ]\n",
    "    }\n",
    "\n",
    "tools = [\n",
    "    {\n",
    "        \"name\": \"send_text_to_user\",\n",
    "        \"description\": \"Sends a text message to a user\",\n",
    "        \"input_schema\": {\n",
    "            \"type\": \"object\",\n",
    "            \"properties\": {\n",
    "                \"text\": {\"type\": \"string\", \"description\": \"The piece of text to be sent to the user via text message\"},\n",
    "            },\n",
    "            \"required\": [\"text\"]\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"get_customer_info\",\n",
    "        \"description\": \"gets information on a customer based on the customer's username.  Response includes email, username, and previous purchases. Only call this tool once a user has provided you with their username\",\n",
    "        \"input_schema\": {\n",
    "            \"type\": \"object\",\n",
    "            \"properties\": {\n",
    "                \"username\": {\"type\": \"string\", \"description\": \"The username of the user in question. \"},\n",
    "            },\n",
    "            \"required\": [\"username\"]\n",
    "        }\n",
    "    },\n",
    "]\n",
    "\n",
    "system_prompt = \"\"\"\n",
    "All your communication with a user is done via text message.\n",
    "Only call tools when you have enough information to accurately call them.  \n",
    "Do not call the get_customer_info tool until a user has provided you with their username. This is important.\n",
    "If you do not know a user's username, simply ask a user for their username.\n",
    "\"\"\"\n",
    "\n",
    "def sms_chatbot(user_message):\n",
    "    messages = [{\"role\": \"user\", \"content\":user_message}]\n",
    "\n",
    "    response = client.messages.create(\n",
    "        system=system_prompt,\n",
    "        model=\"claude-3-sonnet-20240229\",\n",
    "        max_tokens=4096,\n",
    "        tools=tools,\n",
    "        tool_choice={\"type\": \"any\"},\n",
    "        messages=messages\n",
    "    )\n",
    "    if response.stop_reason == \"tool_use\":\n",
    "        last_content_block = response.content[-1]\n",
    "        if last_content_block.type == 'tool_use':\n",
    "            tool_name = last_content_block.name\n",
    "            tool_inputs = last_content_block.input\n",
    "            print(f\"=======Claude Wants To Call The {tool_name} Tool=======\")\n",
    "            if tool_name == \"send_text_to_user\":\n",
    "                send_text_to_user(tool_inputs[\"text\"])\n",
    "            elif tool_name == \"get_customer_info\":\n",
    "                print(get_customer_info(tool_inputs[\"username\"]))\n",
    "            else:\n",
    "                print(\"Oh dear, that tool doesn't exist!\")\n",
    "            \n",
    "    else:\n",
    "        print(\"No tool was called. This shouldn't happen!\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start simple:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======Claude Wants To Call The send_text_to_user Tool=======\n",
      "TEXT MESSAGE SENT: Hello! I'm doing well, thanks for asking. How can I assist you today?\n"
     ]
    }
   ],
   "source": [
    "sms_chatbot(\"Hey there! How are you?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Claude responds back by calling the `send_text_to_user` tool.\n",
    "\n",
    "Next, we'll ask Claude something a bit trickier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======Claude Wants To Call The send_text_to_user Tool=======\n",
      "TEXT MESSAGE SENT: Hi there, to look up your order details I'll need your username first. Can you please provide me with your username?\n"
     ]
    }
   ],
   "source": [
    "sms_chatbot(\"I need help looking up an order\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Claude wants to send a text message, asking a user to provide their username.\n",
    "\n",
    "Now, let's see what happens when we provide Claude with our username:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======Claude Wants To Call The get_customer_info Tool=======\n",
      "{'username': 'jenny76', 'email': 'jenny76@email.com', 'purchases': [{'id': 1, 'product': 'computer mouse'}, {'id': 2, 'product': 'screen protector'}, {'id': 3, 'product': 'usb charging cable'}]}\n"
     ]
    }
   ],
   "source": [
    "sms_chatbot(\"I need help looking up an order.  My username is jenny76\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Claude calls the `get_customer_info` tool, just as we hoped! \n",
    "\n",
    "Even if we send Claude a gibberish message, it will still call one of our tools:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======Claude Wants To Call The send_text_to_user Tool=======\n",
      "TEXT MESSAGE SENT: I'm afraid I didn't understand your query. Could you please rephrase what you need help with?\n"
     ]
    }
   ],
   "source": [
    "sms_chatbot(\"askdj aksjdh asjkdbhas kjdhas 1+1 ajsdh\")"
   ]
  }
 ],
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