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brain-v42

brain-v42

by hawkixs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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brain_find_tool

Search for tools using natural language. Returns up to 5 relevant matches with summaries and parameter details, or full schema for exact tool names.

Instructions

Search for tools using natural language.

Returns up to 5 matching tools ranked by relevance, each as a summary: name, one-line description, required parameters with types, optional parameter names. Pass an exact tool name to get that tool's full input schema.

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": "brain_find_tool"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesNatural language query to search for tools

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.1

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the hard result cap (up to 5), relevance ranking, the summary fields returned, and the exact-name mode that escalates to a full schema. It does not address auth or rate limits, but for a read-only search those are minor.

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?

Purpose is front-loaded in the first sentence, and the following sentences add return-format and mode details with no redundancy. Every sentence earns its place for a 1-param tool.

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?

An output schema exists, so return values need not be re-explained, and the single parameter is fully documented. The description is complete enough to invoke the tool correctly, with only the downstream relationship to brain_call_tool left implicit.

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?

Schema coverage is 100% for the single query param, so the schema baseline is 3. The description adds real meaning by explaining that the same field drives two behaviors (natural language vs. exact tool name), which the schema alone does not convey.

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 (search) and resource (tools) with the query modality (natural language), plus the result shape. It is trivially distinguishable from the brain_session_* siblings and brain_call_tool, which handle sessions and invocation rather than discovery.

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?

The description conveys two usage modes clearly: a natural-language query returns ranked summaries, while an exact tool name returns the full input schema. It does not explicitly name brain_call_tool as the downstream step or state when to prefer discovery over direct invocation, but the context is clear.

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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