Skip to main content
Glama

execute_tool

Send an HTTP request to execute an OpenAPI tool based on its method and path. Requires tool name and arguments, with optional base URL and auth token.

Instructions

Execute an OpenAPI tool via HTTP.

    Sends the actual HTTP request based on the tool's method and path
    from the OpenAPI spec. Use after search_tools() + get_tool_schema()
    to call the API.

    Args:
        tool_name: Exact tool name (as returned by search_tools)
        arguments: JSON string of parameter values (e.g. '{"owner":"me","repo":"test"}')
        base_url: API base URL (e.g. https://api.github.com). Required if not inferrable.
        auth_token: Bearer token for authentication (optional)
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tool_nameYes
argumentsYes
base_urlNo
auth_tokenNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries the burden. It discloses it sends an HTTP request (a write operation) and mentions authentication via auth_token. However, it lacks details on side effects, error behavior, or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with a brief intro and a clear Args list. It is front-loaded with purpose. Slightly verbose but still efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema details, the tool is an execution wrapper. The description covers parameters, workflow, and authentication. It could mention error handling but is largely complete for its purpose.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All four parameters are explained in the description with examples and usage details (e.g., 'tool_name: Exact tool name as returned by search_tools'). The input schema has 0% coverage, so the description fully compensates, adding critical meaning beyond types.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'execute' and the resource 'OpenAPI tool via HTTP'. It differentiates from siblings by specifying the workflow: use after search_tools() and get_tool_schema().

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Use after search_tools() + get_tool_schema() to call the API.' It provides clear context on when to use the tool, though it does not mention when not to use or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/SonAIengine/graph-tool-call'

If you have feedback or need assistance with the MCP directory API, please join our Discord server