LiLBrain

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lilbrain_semantic

Find functions by meaning, not just by name. Describe a task like 'handle authentication' and get relevant functions such as verify_token or check_session.

Instructions

Semantic search: find functions by meaning, not just name. "handle authentication" finds verify_token, check_session, etc.

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": "lilbrain_semantic"
  }
}

Input Schema

Name	Required	Description	Default
`n`	No	Number of results (default 20)
`query`	Yes	Natural language query

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Aug 30, 2026

TDQS

A4.2/5.0

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 the core behavior: approximate matching by meaning rather than exact name, with a concrete example showing how a query maps to multiple functions. It doesn't describe output format or matching limits, but the behavior is adequately conveyed for a search tool.

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?

Two concise sentences, front-loaded with the core purpose, followed by a clarifying example. No wasted words or redundant restating of schema fields.

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?

The tool is simple: two parameters, no nested objects, no annotations, no output schema. The description communicates what results look like ('finds functions') and gives a concrete example. It could mention output shape or result limitation, but overall it is sufficient for an agent to call this correctly.

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 coverage is 100%, so the schema already documents both parameters and their descriptions. The description adds a useful example of how the query parameter is interpreted semantically, but does not need to explain 'n' since the schema covers it.

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 performs semantic search to find functions by meaning, which is a specific verb and resource. It also distinguishes itself from name-based search tools like lilbrain_search by explicitly saying 'not just by name.'

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 makes the intended usage clear: natural-language meaning-based queries for function lookup. The example strongly implies when this tool should be chosen over keyword/name search, though it does not explicitly name an alternative or state exclusions.

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