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MCP Tool-Retrieval Gateway

MCP Tool-Retrieval Gateway

by sneha4175
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

find_tools

Searches all connected MCP servers to find tools matching a natural-language query, returning their definitions as JSON.

Instructions

Search all connected MCP servers for the tools most relevant to a natural-language query. Returns up to k tool definitions (name, description, input schema) as JSON. Call this first to discover which tool to use, then invoke it with call_tool.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "find_tools"
  }
}

Input Schema

NameRequiredDescriptionDefault
kNo
queryYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.7/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses that the tool searches across all connected MCP servers and returns a bounded number of tool definitions, which covers the core behavioral traits. However, it does not mention potential latency, network dependency, or failure modes, so it is not fully exhaustive.

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

Conciseness5/5

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

The description is two sentences long, front-loaded with the core function, and contains no fluff. Every clause adds value: what it does, what it returns, and how to use it.

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

Completeness5/5

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

Given the tool's modest complexity (2 parameters, no annotations), the description fully covers the essential context: it explains the output schema content (name, description, input schema), the role in the workflow, and the parameters. It is a complete description for a discovery tool.

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

Parameters4/5

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

The schema has 0% description coverage, so the description must compensate. It does so implicitly: 'natural-language query' explains the query parameter, and 'up to k' explains the k parameter. This provides meaningful semantics beyond the raw schema, though it lacks detailed constraints or edge cases.

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 tool's purpose: 'Search all connected MCP servers for the tools most relevant to a natural-language query.' It also specifies the return format ('Returns up to k tool definitions... as JSON') and distinguishes itself from the sibling tool call_tool by positioning itself as the discovery step.

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?

The description gives explicit usage guidance: 'Call this first to discover which tool to use, then invoke it with call_tool.' This clearly states when to use this tool and how it relates to the alternative, call_tool.

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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