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raptr-agent-finder

Find existing agents

find_agents
Read-only

Searches the open agent registry (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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true and destructiveHint=false, so the safety profile is covered. The description usefully adds that the search targets a public registry of ~1,700 single-file agents, which tells the agent the nature and boundary of what is being searched, though it says nothing about result ordering or relevance behavior.

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?

Three sentences, front-loaded with the core action and scope, then usage triggers. Slight redundancy between 'want a ready-made automation instead of building one' and 'Use it before building from scratch,' but nothing is wasted overall.

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?

For a two-parameter read-only search with no output schema, the description supplies the scope, the trigger conditions, and the intended workflow position. The only gap is that it does not preview the shape of the returned agent results, which is a minor omission for a discovery tool.

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 the 'query' and 'limit' parameters are already documented in structured data. The description reinforces the query style with an example phrase, but adds no syntax or format detail beyond the schema's own 'summarize sales calls' example.

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 ('Searches') and resource ('the open agent registry'), and quantifies scope ('about 1,700 single-file agents'), so an agent immediately knows this is the discovery step rather than a retrieval/execution tool like get_agent_code or use_agent_here.

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

Usage Guidelines5/5

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

Gives explicit trigger conditions ('is there an AI tool for...', 'whether an agent exists for a task') and a clear when-not alternative ('instead of building one', 'before building from scratch'). The agent knows both when to reach for this and what it replaces.

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

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