Clade Embeddings Search

Anthropic Tool Use (Function Calling)

Overview

Tool use lets Claude call functions you define — query databases, hit APIs, read files, do math. Claude decides when to call a tool, you execute it, and feed the result back. This is how you build Claude-powered agents.

Note: Anthropic does not offer an embeddings API. For embeddings + vector search, pair Claude with a dedicated embedding model (OpenAI, Cohere, or Voyage).

Prerequisites

• Completed clade-model-inference
• Understanding of JSON Schema for tool definitions

Instructions

Step 1: Define Tools

import Anthropic from '@claude-ai/sdk';

const client = new Anthropic();

const tools: Anthropic.Tool[] = [
  {
    name: 'get_weather',
    description: 'Get current weather for a city. Call this when the user asks about weather.',
    input_schema: {
      type: 'object',
      properties: {
        city: { type: 'string', description: 'City name, e.g. "San Francisco"' },
        unit: { type: 'string', enum: ['celsius', 'fahrenheit'], description: 'Temperature unit' },
      },
      required: ['city'],
    },
  },
];

Step 2: Send Message with Tools

const response = await client.messages.create({
  model: 'claude-sonnet-4-20250514',
  max_tokens: 1024,
  tools,
  messages: [{ role: 'user', content: "What's the weather in San Francisco?" }],
});

// Claude responds with stop_reason: 'tool_use'
// response.content includes a tool_use block:
// { type: 'tool_use', id: 'toolu_01...', name: 'get_weather', input: { city: 'San Francisco' } }

Step 3: Execute Tool and Return Result

// Find the tool use block
const toolUse = response.content.find(block => block.type === 'tool_use');

// Execute your function
const weatherData = await fetchWeather(toolUse.input.city);

// Send result back to Claude
const finalResponse = await client.messages.create({
  model: 'claude-sonnet-4-20250514',
  max_tokens: 1024,
  tools,
  messages: [
    { role: 'user', content: "What's the weather in San Francisco?" },
    { role: 'assistant', content: response.content },
    {
      role: 'user',
      content: [{
        type: 'tool_result',
        tool_use_id: toolUse.id,
        content: JSON.stringify(weatherData),
      }],
    },
  ],
});

console.log(finalResponse.content[0].text);
// "The weather in San Francisco is currently 65°F and partly cloudy."

Python Example

import anthropic

client = anthropic.Anthropic()

tools = [{
    "name": "get_weather",
    "description": "Get current weather for a city.",
    "input_schema": {
        "type": "object",
        "properties": {
            "city": {"type": "string"},
        },
        "required": ["city"],
    },
}]

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    tools=tools,
    messages=[{"role": "user", "content": "Weather in Paris?"}],
)

# Process tool_use blocks in response.content
for block in response.content:
    if block.type == "tool_use":
        result = execute_tool(block.name, block.input)
        # Send tool_result back...

Agentic Tool Loop

// Keep calling Claude until it stops requesting tools
let messages = [{ role: 'user', content: userInput }];

while (true) {
  const response = await client.messages.create({
    model: 'claude-sonnet-4-20250514',
    max_tokens: 4096,
    tools,
    messages,
  });

  // Add assistant response to conversation
  messages.push({ role: 'assistant', content: response.content });

  if (response.stop_reason === 'end_turn') {
    // Claude is done — extract final text
    const text = response.content.find(b => b.type === 'text')?.text;
    console.log(text);
    break;
  }

  // Execute all tool calls and send results
  const toolResults = [];
  for (const block of response.content) {
    if (block.type === 'tool_use') {
      const result = await executeTool(block.name, block.input);
      toolResults.push({
        type: 'tool_result',
        tool_use_id: block.id,
        content: JSON.stringify(result),
      });
    }
  }
  messages.push({ role: 'user', content: toolResults });
}

Output

• tool_use content blocks with name and input when Claude wants to call a tool
• stop_reason: "tool_use" indicating Claude is waiting for tool results
• Final text response after all tool results are provided
• Complete agentic loop until stop_reason: "end_turn"

Error Handling

Examples

See Step 1 (tool definition), Step 2 (sending with tools), Step 3 (executing and returning results), and the full agentic tool loop example above.

Resources

• Tool Use Guide
• Tool Use API Reference

Next Steps

See clade-common-errors for error handling patterns.

Source: jeremylongshore/claude-code-plugins-plus-skills → skills/.curated/clade-embeddings-search/SKILL.md

Also appears in: jeremylongshore/claude-code-plugins-plus-skills/plugins/saas-packs/claude-pack/skills/clade-embeddings-search/SKILL.md