BRAINS MCP Server

search_knowledge

Query the internal knowledge base to retrieve relevant playbook and policy context. Submit a natural-language question and get matching knowledge chunks via semantic similarity.

Instructions

Search the internal knowledge base for playbook and policy context.

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_text": "<query_text>"
    },
    "name": "search_knowledge"
  }
}

Input Schema

Name	Required	Description	Default
`query_text`	Yes	A natural-language question, e.g. "what makes a lead a good fit?". Embedded and matched by cosine similarity against stored chunks.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 6, 2026

TDQS

A3.8/5.0

Behavior3/5

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

No annotations are provided, so the description must carry the behavioral burden. It states the operation and scope, which implies a read-only search, but it does not disclose result behavior, ranking, limitations, or side effects. The cosine-similarity mechanism appears in the parameter schema, not in the tool description.

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?

A single, front-loaded sentence with no filler. It states the action, resource, and content scope efficiently, making it easy for an agent to parse quickly.

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 simple one-parametere search tool, the definition is largely complete: the input schema is fully documented and an output schema exists, so return-value details need not be spelled out. The main gap is the lack of explicit usage guidance, but the resource/scope phrasing partially compensates.

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 the schema already documents query_text as a natural-language question embedded and matched by cosine similarity. The tool description adds no parameter-level meaning beyond that, but none is needed given the schema covers it fully.

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 uses a specific verb ('Search'), identifies the resource ('internal knowledge base'), and narrows the scope ('playbook and policy context'). This makes the tool's purpose clear and distinct from siblings like lookup_lead, check_crm, and score_lead, which target lead/CRM/scoring data rather than knowledge-base content.

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

Usage Guidelines3/5

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

The intended context is implied: use when you need internal playbook or policy context. There is no explicit when-to-use/when-not-to-use guidance and no named alternatives, so an agent must infer selection from the resource type and sibling names.

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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