Skip to main content
Glama

raptr-agent-finder

Find existing agents

find_agents
Read-only

Searches the open agent registry (RAR) (RAR, about 1,700 single-file agents) for agents that already do what the user wants, for example when they ask 'is there an AI tool for...' or want a ready-made automation instead of building one. Use it before building from scratch, or when the user asks whether an agent exists for a task.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many results, default 5
queryYesPlain words describing the task, e.g. 'summarize sales calls'

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, and destructiveHint, so the safety profile is covered. The description usefully discloses registry scope and size (open, ~1,700 single-file agents), but says nothing about rate limits, result freshness, or what a match actually returns. Modest added value over the structured hints.

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?

Two compact sentences with the core purpose front-loaded; every clause does work. Minor redundancy in restating 'RAR' twice back-to-back ('(RAR) (RAR, about 1,700...)'), which is slightly sloppy but not costly.

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

Completeness3/5

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

For a discovery tool with no output schema, the description should hint at what matches look like (agent name, description, code reference) since the agent must decide what to do next with results. It covers when to call well but leaves the return shape and follow-up path (e.g., get_agent_code) unstated.

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

Parameters3/5

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

Schema description coverage is 100%, so both parameters are already documented, including the example 'summarize sales calls' for query. The description reinforces the query intent with example user utterances but adds no format or semantics beyond the schema, so baseline 3 is right.

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?

States a specific verb and resource ('Searches the open agent registry (RAR)') and scopes it precisely ('about 1,700 single-file agents'). The framing as finding 'agents that already do what the user wants' lets an agent distinguish it from siblings like get_agent_code or use_agent_here without opening a 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?

Gives explicit triggers: 'Use it before building from scratch, or when the user asks whether an agent exists for a task,' plus sample phrasings like 'is there an AI tool for...'. It does not name alternative tools for when the user already knows an agent by name, so it stops short of full alternative routing.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.