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Filmystar

LightRAG Code Brain MCP

by Filmystar

brain_remember

Persist project memory including architecture decisions, failures, and session notes to reduce rediscovery and maintain context across coding sessions.

Instructions

Persist a durable project memory into the local memory log and LightRAG. Use this after fixes, failed attempts, setup discoveries, architecture decisions, and session handoff notes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNolesson
titleYes
summaryYes
detailsNo
outcomeNo
repo_idNorepo1
filesNo
tagsNo
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It only states that the tool persists memories into two stores (local memory log and LightRAG) but does not describe side effects, authorization needs, or whether it appends or overwrites. This is insufficient for a write operation.

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, front-loads the action and resource, and contains no extraneous information. Every sentence is purposeful.

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?

Despite a clear purpose and usage context, the description fails to provide enough information for the agent to correctly invoke the tool, especially given the 0% parameter coverage, missing output schema, and no annotations. The agent lacks guidance on how to fill parameters and what to expect.

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?

With 0% schema description coverage and 8 parameters, the description adds no meaning to any parameter. The agent must infer all parameter semantics from names and enums alone, which is inadequate.

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 ('persist') and clearly identifies the resource ('durable project memory', 'local memory log and LightRAG'). It also distinguishes itself from sibling tools like brain_begin and brain_finish by focusing on memory storage after specific events.

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 explicitly lists when to use the tool ('after fixes, failed attempts, setup discoveries, architecture decisions, and session handoff notes'), providing good context. However, it does not specify when not to use it or mention alternatives.

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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