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Filmystar

LightRAG Code Brain MCP

by Filmystar

brain_search

Search durable project memories and LightRAG semantic memory for prior fixes, failures, decisions, and setup outcomes. Reduce rediscovery for coding agents by retrieving relevant context.

Instructions

Search durable project memories and LightRAG semantic memory for prior fixes, failures, decisions, and setup outcomes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
repo_idNo
kindsNo
limitNo
modeNohybrid
top_kNo
chunk_top_kNo
Behavior2/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It says 'Search', implying a read-only operation, but does not confirm non-destructiveness, required permissions, or any side effects such as rate limiting or data freshness. Lack of transparency may lead to misuse.

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?

The description is a single sentence of 14 words, which is concise. It leads with the action verb 'Search'. However, it lacks structural elements like examples or parameter grouping. Given the tool's complexity (7 params), the brevity may sacrifice clarity.

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

Completeness2/5

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

With 7 parameters, required query, enums for kinds and mode, no output schema, and no annotations, the description fails to explain critical details like the meaning of mode (local vs hybrid), the role of repo_id, or the difference between limit and top_k. It is insufficient for complete understanding.

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

Parameters1/5

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

Schema description coverage is 0% (no descriptions in schema), and the tool description adds no explanation for any of the 7 parameters (query, repo_id, kinds, limit, mode, top_k, chunk_top_k). An agent cannot infer meaning or proper values beyond the type/enum constraints, leaving significant uncertainty in invocation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Search' and specifies the resources: 'durable project memories and LightRAG semantic memory'. It also lists example content types (fixes, failures, decisions, setup outcomes), making the purpose recognizable. However, it does not differentiate from sibling search tools like rag_query_data or profile_search, which could cause confusion.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool vs alternatives, no prerequisites, and no mention of when not to use it. With many sibling tools offering search functionality (e.g., brain_recent, rag_query_data), this omission makes it hard for an agent to decide correctly.

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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