Tool Search Tool ↗

anthropic-platform guide intermediate tool-use agents
yes

Editorial Notes

The tool search tool enables deferred tool loading, allowing agents with large tool catalogs to discover and activate tools on demand rather than loading everything into context upfront. This is critical for agents that integrate many MCP servers or have hundreds of available tools, since stuffing all tool definitions into the system prompt degrades model performance and wastes tokens. Pay attention to the query semantics: keyword search returns ranked matches while direct selection loads a specific tool by name. Architect your agent so that commonly used tools are always loaded and niche tools are deferred behind search to keep context windows lean.

Original Documentation

The tool search tool enables Claude to work with hundreds or thousands of tools by dynamically discovering and loading them on-demand. Instead of loading all tool definitions into the context window upfront, Claude searches your tool catalog (including tool names, descriptions, argument names, and argument descriptions) and loads only the tools it needs.

This approach solves two problems that compound quickly as tool libraries scale:

  • Context bloat: Tool definitions eat into your context budget fast. A typical multi-server setup (GitHub, Slack, Sentry, Grafana, Splunk) can consume ~55K tokens in definitions before Claude does any actual work. Tool search typically reduces this by over 85%, loading only the 3–5 tools Claude actually needs for a given request.
  • Tool selection accuracy: Claude’s ability to correctly pick the right tool degrades significantly once you exceed 30–50 available tools. By surfacing a focused set of relevant tools on demand, tool search keeps selection accuracy high even across thousands of tools.

For background on the scaling challenges that tool search solves, see Advanced tool use. Tool search’s on-demand loading is also an instance of the broader just-in-time retrieval principle described in Effective context engineering.

Although this is provided as a server-side tool, you can also implement your own client-side tool search functionality. See Custom tool search implementation for details.

Please reach out through our feedback form to share your feedback on this feature.

Server-side tool search is not covered by Zero Data Retention (ZDR) arrangements. Data is retained according to the feature’s standard retention policy. Custom client-side tool search implementations use the standard Messages API and are ZDR-eligible.

On Amazon Bedrock, server-side tool search is available only via the invoke API, not the converse API.

You can also implement client-side tool search by returning tool_reference blocks from your own search implementation.

How tool search works#

There are two tool search variants:

  • Regex (tool_search_tool_regex_20251119): Claude constructs regex patterns to search for tools
  • BM25 (tool_search_tool_bm25_20251119): Claude uses natural language queries to search for tools

When you enable the tool search tool:

  1. You include a tool search tool (e.g., tool_search_tool_regex_20251119 or tool_search_tool_bm25_20251119) in your tools list
  2. You provide all tool definitions with defer_loading: true for tools that shouldn’t be loaded immediately
  3. Claude sees only the tool search tool and any non-deferred tools initially
  4. When Claude needs additional tools, it searches using a tool search tool
  5. The API returns 3-5 most relevant tool_reference blocks
  6. These references are automatically expanded into full tool definitions
  7. Claude selects from the discovered tools and invokes them

This keeps your context window efficient while maintaining high tool selection accuracy.

Quick start#

Here’s a simple example with deferred tools:

curl https://api.anthropic.com/v1/messages \
    --header "x-api-key: $ANTHROPIC_API_KEY" \
    --header "anthropic-version: 2023-06-01" \
    --header "content-type: application/json" \
    --data '{
        "model": "claude-opus-4-6",
        "max_tokens": 2048,
        "messages": [
            {
                "role": "user",
                "content": "What is the weather in San Francisco?"
            }
        ],
        "tools": [
            {
                "type": "tool_search_tool_regex_20251119",
                "name": "tool_search_tool_regex"
            },
            {
                "name": "get_weather",
                "description": "Get the weather at a specific location",
                "input_schema": {
                    "type": "object",
                    "properties": {
                        "location": {"type": "string"},
                        "unit": {
                            "type": "string",
                            "enum": ["celsius", "fahrenheit"]
                        }
                    },
                    "required": ["location"]
                },
                "defer_loading": true
            },
            {
                "name": "search_files",
                "description": "Search through files in the workspace",
                "input_schema": {
                    "type": "object",
                    "properties": {
                        "query": {"type": "string"},
                        "file_types": {
                            "type": "array",
                            "items": {"type": "string"}
                        }
                    },
                    "required": ["query"]
                },
                "defer_loading": true
            }
        ]
    }'
import anthropic

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=2048,
    messages=[{"role": "user", "content": "What is the weather in San Francisco?"}],
    tools=[
        {"type": "tool_search_tool_regex_20251119", "name": "tool_search_tool_regex"},
        {
            "name": "get_weather",
            "description": "Get the weather at a specific location",
            "input_schema": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"},
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["location"],
            },
            "defer_loading": True,
        },
        {
            "name": "search_files",
            "description": "Search through files in the workspace",
            "input_schema": {
                "type": "object",
                "properties": {
                    "query": {"type": "string"},
                    "file_types": {"type": "array", "items": {"type": "string"}},
                },
                "required": ["query"],
            },
            "defer_loading": True,
        },
    ],
)

print(response)

const client = new Anthropic();

async function main() {
  const response = await client.messages.create({
    model: "claude-opus-4-6",
    max_tokens: 2048,
    messages: [
      {
        role: "user",
        content: "What is the weather in San Francisco?"
      }
    ],
    tools: [
      {
        type: "tool_search_tool_regex_20251119",
        name: "tool_search_tool_regex"
      },
      {
        name: "get_weather",
        description: "Get the weather at a specific location",
        input_schema: {
          type: "object",
          properties: {
            location: { type: "string" },
            unit: {
              type: "string",
              enum: ["celsius", "fahrenheit"]
            }
          },
          required: ["location"]
        },
        defer_loading: true
      },
      {
        name: "search_files",
        description: "Search through files in the workspace",
        input_schema: {
          type: "object",
          properties: {
            query: { type: "string" },
            file_types: {
              type: "array",
              items: { type: "string" }
            }
          },
          required: ["query"]
        },
        defer_loading: true
      }
    ]
  });

  console.log(JSON.stringify(response, null, 2));
}

main();

Tool definition#

The tool search tool has two variants:

{
  "type": "tool_search_tool_regex_20251119",
  "name": "tool_search_tool_regex"
}
{
  "type": "tool_search_tool_bm25_20251119",
  "name": "tool_search_tool_bm25"
}

Regex variant query format: Python regex, NOT natural language

When using tool_search_tool_regex_20251119, Claude constructs regex patterns using Python’s re.search() syntax, not natural language queries. Common patterns:

  • "weather" - matches tool names/descriptions containing “weather”
  • "get_.*_data" - matches tools like get_user_data, get_weather_data
  • "database.*query|query.*database" - OR patterns for flexibility
  • "(?i)slack" - case-insensitive search

Maximum query length: 200 characters

BM25 variant query format: Natural language

When using tool_search_tool_bm25_20251119, Claude uses natural language queries to search for tools.

Deferred tool loading#

Mark tools for on-demand loading by adding defer_loading: true:

{
  "name": "get_weather",
  "description": "Get current weather for a location",
  "input_schema": {
    "type": "object",
    "properties": {
      "location": { "type": "string" },
      "unit": { "type": "string", "enum": ["celsius", "fahrenheit"] }
    },
    "required": ["location"]
  },
  "defer_loading": true
}

Key points:

  • Tools without defer_loading are loaded into context immediately
  • Tools with defer_loading: true are only loaded when Claude discovers them via search
  • The tool search tool itself should never have defer_loading: true
  • Keep your 3-5 most frequently used tools as non-deferred for optimal performance

Both tool search variants (regex and bm25) search tool names, descriptions, argument names, and argument descriptions.

Response format#

When Claude uses the tool search tool, the response includes new block types:

{
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "I'll search for tools to help with the weather information."
    },
    {
      "type": "server_tool_use",
      "id": "srvtoolu_01ABC123",
      "name": "tool_search_tool_regex",
      "input": {
        "query": "weather"
      }
    },
    {
      "type": "tool_search_tool_result",
      "tool_use_id": "srvtoolu_01ABC123",
      "content": {
        "type": "tool_search_tool_search_result",
        "tool_references": [{ "type": "tool_reference", "tool_name": "get_weather" }]
      }
    },
    {
      "type": "text",
      "text": "I found a weather tool. Let me get the weather for San Francisco."
    },
    {
      "type": "tool_use",
      "id": "toolu_01XYZ789",
      "name": "get_weather",
      "input": { "location": "San Francisco", "unit": "fahrenheit" }
    }
  ],
  "stop_reason": "tool_use"
}

Understanding the response#

  • server_tool_use: Indicates Claude is invoking the tool search tool
  • tool_search_tool_result: Contains the search results with a nested tool_search_tool_search_result object
  • tool_references: Array of tool_reference objects pointing to discovered tools
  • tool_use: Claude invoking the discovered tool

The tool_reference blocks are automatically expanded into full tool definitions before being shown to Claude. You don’t need to handle this expansion yourself. It happens automatically in the API as long as you provide all matching tool definitions in the tools parameter.

MCP integration#

The tool search tool works with MCP servers. Add the "mcp-client-2025-11-20" beta header to your API request, and then use mcp_toolset with default_config to defer loading MCP tools:

curl https://api.anthropic.com/v1/messages \
  --header "x-api-key: $ANTHROPIC_API_KEY" \
  --header "anthropic-version: 2023-06-01" \
  --header "anthropic-beta: mcp-client-2025-11-20" \
  --header "content-type: application/json" \
  --data '{
    "model": "claude-opus-4-6",
    "max_tokens": 2048,
    "mcp_servers": [
      {
        "type": "url",
        "name": "database-server",
        "url": "https://mcp-db.example.com"
      }
    ],
    "tools": [
      {
        "type": "tool_search_tool_regex_20251119",
        "name": "tool_search_tool_regex"
      },
      {
        "type": "mcp_toolset",
        "mcp_server_name": "database-server",
        "default_config": {
          "defer_loading": true
        },
        "configs": {
          "search_events": {
            "defer_loading": false
          }
        }
      }
    ],
    "messages": [
      {
        "role": "user",
        "content": "What events are in my database?"
      }
    ]
  }'
import anthropic

client = anthropic.Anthropic()

response = client.beta.messages.create(
    model="claude-opus-4-6",
    betas=["mcp-client-2025-11-20"],
    max_tokens=2048,
    mcp_servers=[
        {"type": "url", "name": "database-server", "url": "https://mcp-db.example.com"}
    ],
    tools=[
        {"type": "tool_search_tool_regex_20251119", "name": "tool_search_tool_regex"},
        {
            "type": "mcp_toolset",
            "mcp_server_name": "database-server",
            "default_config": {"defer_loading": True},
            "configs": {"search_events": {"defer_loading": False}},
        },
    ],
    messages=[{"role": "user", "content": "What events are in my database?"}],
)

print(response)

const client = new Anthropic();

async function main() {
  const response = await client.beta.messages.create({
    model: "claude-opus-4-6",
    betas: ["mcp-client-2025-11-20"],
    max_tokens: 2048,
    mcp_servers: [
      {
        type: "url",
        name: "database-server",
        url: "https://mcp-db.example.com"
      }
    ],
    tools: [
      {
        type: "tool_search_tool_regex_20251119",
        name: "tool_search_tool_regex"
      },
      {
        type: "mcp_toolset",
        mcp_server_name: "database-server",
        default_config: {
          defer_loading: true
        },
        configs: {
          search_events: {
            defer_loading: false
          }
        }
      }
    ],
    messages: [
      {
        role: "user",
        content: "What events are in my database?"
      }
    ]
  });

  console.log(JSON.stringify(response, null, 2));
}

main();

MCP configuration options:

  • default_config.defer_loading: Set default for all tools from the MCP server
  • configs: Override defaults for specific tools by name
  • Combine multiple MCP servers with tool search for massive tool libraries

Custom tool search implementation#

You can implement your own tool search logic (e.g., using embeddings or semantic search) by returning tool_reference blocks from a custom tool. When Claude calls your custom search tool, return a standard tool_result with tool_reference blocks in the content array:

{
  "type": "tool_result",
  "tool_use_id": "toolu_your_tool_id",
  "content": [{ "type": "tool_reference", "tool_name": "discovered_tool_name" }]
}

Every tool referenced must have a corresponding tool definition in the top-level tools parameter with defer_loading: true. This approach lets you use more sophisticated search algorithms while maintaining compatibility with the tool search system.

The tool_search_tool_result format shown in the Response format section is the server-side format used internally by Anthropic’s built-in tool search. For custom client-side implementations, always use the standard tool_result format with tool_reference content blocks as shown above.

For a complete example using embeddings, see our tool search with embeddings cookbook.

Error handling#

The tool search tool is not compatible with tool use examples. If you need to provide examples of tool usage, use standard tool calling without tool search.

HTTP errors (400 status)#

These errors prevent the request from being processed:

All tools deferred:

{
  "type": "error",
  "error": {
    "type": "invalid_request_error",
    "message": "All tools have defer_loading set. At least one tool must be non-deferred."
  }
}

Missing tool definition:

{
  "type": "error",
  "error": {
    "type": "invalid_request_error",
    "message": "Tool reference 'unknown_tool' has no corresponding tool definition"
  }
}

Tool result errors (200 status)#

Errors during tool execution return a 200 response with error information in the body:

{
  "type": "tool_result",
  "tool_use_id": "srvtoolu_01ABC123",
  "content": {
    "type": "tool_search_tool_result_error",
    "error_code": "invalid_pattern"
  }
}

Error codes:

  • too_many_requests: Rate limit exceeded for tool search operations
  • invalid_pattern: Malformed regex pattern
  • pattern_too_long: Pattern exceeds 200 character limit
  • unavailable: Tool search service temporarily unavailable

Common mistakes#

400 Error: All tools are deferred

Cause: You set defer_loading: true on ALL tools including the search tool

Fix: Remove defer_loading from the tool search tool:

{
  "type": "tool_search_tool_regex_20251119", // No defer_loading here
  "name": "tool_search_tool_regex"
}
400 Error: Missing tool definition

Cause: A tool_reference points to a tool not in your tools array

Fix: Ensure every tool that could be discovered has a complete definition:

{
  "name": "my_tool",
  "description": "Full description here",
  "input_schema": {
    // complete schema
  },
  "defer_loading": true
}
Claude doesn't find expected tools

Cause: Tool names or descriptions don’t match the regex pattern

Debugging steps:

  1. Check tool name and description. Claude searches BOTH fields
  2. Test your pattern: import re; re.search(r"your_pattern", "tool_name")
  3. Remember searches are case-sensitive by default (use (?i) for case-insensitive)
  4. Claude uses broad patterns like ".*weather.*" not exact matches

Tip: Add common keywords to tool descriptions to improve discoverability

Prompt caching#

Tool search works with prompt caching. Add cache_control breakpoints to optimize multi-turn conversations:

import anthropic

client = anthropic.Anthropic()

# First request with tool search
messages = [{"role": "user", "content": "What's the weather in Seattle?"}]

response1 = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=2048,
    messages=messages,
    tools=[
        {"type": "tool_search_tool_regex_20251119", "name": "tool_search_tool_regex"},
        {
            "name": "get_weather",
            "description": "Get weather for a location",
            "input_schema": {
                "type": "object",
                "properties": {"location": {"type": "string"}},
                "required": ["location"],
            },
            "defer_loading": True,
        },
    ],
)

# Add Claude's response to conversation
messages.append({"role": "assistant", "content": response1.content})

# Second request with cache breakpoint
messages.append(
    {
        "role": "user",
        "content": "What about New York?",
        "cache_control": {"type": "ephemeral"},
    }
)

response2 = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=2048,
    messages=messages,
    tools=[
        {"type": "tool_search_tool_regex_20251119", "name": "tool_search_tool_regex"},
        {
            "name": "get_weather",
            "description": "Get weather for a location",
            "input_schema": {
                "type": "object",
                "properties": {"location": {"type": "string"}},
                "required": ["location"],
            },
            "defer_loading": True,
        },
    ],
)

print(f"Cache read tokens: {response2.usage.get('cache_read_input_tokens', 0)}")

The system automatically expands tool_reference blocks throughout the entire conversation history, so Claude can reuse discovered tools in subsequent turns without re-searching.

Streaming#

With streaming enabled, you’ll receive tool search events as part of the stream:

event: content_block_start
data: {"type": "content_block_start", "index": 1, "content_block": {"type": "server_tool_use", "id": "srvtoolu_xyz789", "name": "tool_search_tool_regex"}}

// Search query streamed
event: content_block_delta
data: {"type": "content_block_delta", "index": 1, "delta": {"type": "input_json_delta", "partial_json": "{\"query\":\"weather\"}"}}

// Pause while search executes

// Search results streamed
event: content_block_start
data: {"type": "content_block_start", "index": 2, "content_block": {"type": "tool_search_tool_result", "tool_use_id": "srvtoolu_xyz789", "content": {"type": "tool_search_tool_search_result", "tool_references": [{"type": "tool_reference", "tool_name": "get_weather"}]}}}

// Claude continues with discovered tools

Batch requests#

You can include the tool search tool in the Messages Batches API. Tool search operations through the Messages Batches API are priced the same as those in regular Messages API requests.

Limits and best practices#

Limits#

  • Maximum tools: 10,000 tools in your catalog
  • Search results: Returns 3-5 most relevant tools per search
  • Pattern length: Maximum 200 characters for regex patterns
  • Model support: Sonnet 4.0+, Opus 4.0+ only (no Haiku)

Good use cases:

  • 10+ tools available in your system
  • Tool definitions consuming >10K tokens
  • Experiencing tool selection accuracy issues with large tool sets
  • Building MCP-powered systems with multiple servers (200+ tools)
  • Tool library growing over time

When traditional tool calling might be better:

  • Less than 10 tools total
  • All tools are frequently used in every request
  • Very small tool definitions (<100 tokens total)

Optimization tips#

  • Keep 3-5 most frequently used tools as non-deferred
  • Write clear, descriptive tool names and descriptions
  • Use consistent namespacing in tool names: prefix by service or resource (e.g., github_, slack_) so that search queries naturally surface the right tool group
  • Use semantic keywords in descriptions that match how users describe tasks
  • Add a system prompt section describing available tool categories: “You can search for tools to interact with Slack, GitHub, and Jira”
  • Monitor which tools Claude discovers to refine descriptions

Usage#

Tool search tool usage is tracked in the response usage object:

{
  "usage": {
    "input_tokens": 1024,
    "output_tokens": 256,
    "server_tool_use": {
      "tool_search_requests": 2
    }
  }
}
Link last verified June 7, 2026. View original ↗
Source: Anthropic Platform Docs
Link last verified: 2026-02-26
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