API Reference

Tools

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Search Tools

tools.search(ToolSearchParams**kwargs) -> ToolSearchResponse

post/v1/tools/search

Search tools using semantic search.

Requires tool embedding to be enabled (embed_tools=True). Uses vector search, full-text search, or hybrid mode to find tools matching the query.

Returns tools ranked by relevance with their search scores.

ParametersExpand Collapse

limit: Optional[int]

Maximum number of results to return.

maximum100

minimum1

query: Optional[str]

Text query for semantic search.

search_mode: Optional[Literal["vector", "fts", "hybrid"]]

Search mode: vector, fts, or hybrid.

Accepts one of the following:

"vector"

"fts"

"hybrid"

tags: Optional[SequenceNotStr[str]]

Filter by tags (match any).

tool_types: Optional[SequenceNotStr[str]]

Filter by tool types (e.g., 'custom', 'letta_core').

ReturnsExpand Collapse

ToolSearchResponse = List[ToolSearchResult]

combined_score: float

Combined relevance score (RRF for hybrid mode).

tool: Tool

The matched tool.

id: str

The human-friendly ID of the Tool

args_json_schema: Optional[Dict[str, object]]

The args JSON schema of the function.

created_by_id: Optional[str]

The id of the user that made this Tool.

default_requires_approval: Optional[bool]

Default value for whether or not executing this tool requires approval.

description: Optional[str]

The description of the tool.

enable_parallel_execution: Optional[bool]

If set to True, then this tool will potentially be executed concurrently with other tools. Default False.

json_schema: Optional[Dict[str, object]]

The JSON schema of the function.

last_updated_by_id: Optional[str]

The id of the user that made this Tool.

metadata: Optional[Dict[str, object]]

A dictionary of additional metadata for the tool.

name: Optional[str]

The name of the function.

npm_requirements: Optional[List[NpmRequirement]]

Optional list of npm packages required by this tool.

name: str

Name of the npm package.

minLength1

version: Optional[str]

Optional version of the package, following semantic versioning.

pip_requirements: Optional[List[PipRequirement]]

Optional list of pip packages required by this tool.

name: str

Name of the pip package.

minLength1

version: Optional[str]

Optional version of the package, following semantic versioning.

project_id: Optional[str]

The project id of the tool.

return_char_limit: Optional[int]

The maximum number of characters in the response.

maximum1000000

minimum1

source_code: Optional[str]

The source code of the function.

source_type: Optional[str]

The type of the source code.

tags: Optional[List[str]]

Metadata tags.

tool_type: Optional[ToolType]

The type of the tool.

Accepts one of the following:

"custom"

"letta_core"

"letta_memory_core"

"letta_multi_agent_core"

"letta_sleeptime_core"

"letta_voice_sleeptime_core"

"letta_builtin"

"letta_files_core"

"external_langchain"

"external_composio"

"external_mcp"

embedded_text: Optional[str]

The embedded text content used for matching.

fts_rank: Optional[int]

Full-text search rank position.

vector_rank: Optional[int]

Vector search rank position.

Search Tools

Python

• HTTP
• TypeScript
• Python

from letta_client import Letta

client = Letta(
    api_key="My API Key",
)
tool_search_results = client.tools.search()
print(tool_search_results)

200 example

[
  {
    "combined_score": 0,
    "tool": {
      "id": "tool-123e4567-e89b-12d3-a456-426614174000",
      "args_json_schema": {
        "foo": "bar"
      },
      "created_by_id": "created_by_id",
      "default_requires_approval": true,
      "description": "description",
      "enable_parallel_execution": true,
      "json_schema": {
        "foo": "bar"
      },
      "last_updated_by_id": "last_updated_by_id",
      "metadata_": {
        "foo": "bar"
      },
      "name": "name",
      "npm_requirements": [
        {
          "name": "x",
          "version": "version"
        }
      ],
      "pip_requirements": [
        {
          "name": "x",
          "version": "version"
        }
      ],
      "project_id": "project_id",
      "return_char_limit": 1,
      "source_code": "source_code",
      "source_type": "source_type",
      "tags": [
        "string"
      ],
      "tool_type": "custom"
    },
    "embedded_text": "embedded_text",
    "fts_rank": 0,
    "vector_rank": 0
  }
]

Returns Examples

200 example

[
  {
    "combined_score": 0,
    "tool": {
      "id": "tool-123e4567-e89b-12d3-a456-426614174000",
      "args_json_schema": {
        "foo": "bar"
      },
      "created_by_id": "created_by_id",
      "default_requires_approval": true,
      "description": "description",
      "enable_parallel_execution": true,
      "json_schema": {
        "foo": "bar"
      },
      "last_updated_by_id": "last_updated_by_id",
      "metadata_": {
        "foo": "bar"
      },
      "name": "name",
      "npm_requirements": [
        {
          "name": "x",
          "version": "version"
        }
      ],
      "pip_requirements": [
        {
          "name": "x",
          "version": "version"
        }
      ],
      "project_id": "project_id",
      "return_char_limit": 1,
      "source_code": "source_code",
      "source_type": "source_type",
      "tags": [
        "string"
      ],
      "tool_type": "custom"
    },
    "embedded_text": "embedded_text",
    "fts_rank": 0,
    "vector_rank": 0
  }
]