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Filmystar

LightRAG Code Brain MCP

by Filmystar

rag_index_repo

Index code repositories into LightRAG to build a durable memory layer, enabling RAG queries for coding agents.

Instructions

Index a repository into LightRAG. Multi-repo support is provided by prefixing every source with repo_id:relative/path.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rootNoRepository root to index./app
repo_idNoStable repository identifier used as source prefix.repo1
include_extensionsNo
exclude_dirsNo
exclude_file_namesNo
exclude_suffixesNo
max_file_bytesNo
limitNo
dry_runNo
redact_secretsNo
Behavior2/5

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

With no annotations provided, the description bears full responsibility for disclosing behavioral traits. It only hints at the prefixing mechanism but omits critical details like the index's persistence, idempotency, side effects on previous indexes, or performance impact. The mutability is implicit but not explicitly stated.

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 consists of two tightly written sentences that front-load the core purpose and immediately follow with a key design detail (multi-repo support). Every word contributes value, with no redundancy or fluff.

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?

Given the tool's complexity—10 parameters, no output schema, and many sibling tools—the description is severely under-informative. It lacks any mention of prerequisites (e.g., LightRAG status), error scenarios, return behavior, or how this index coexists with other 'rag_' and 'brain_' tools. The description answers only the most basic 'what' question.

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

Parameters2/5

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

Schema description coverage is low (20%), and while the schema itself contains descriptions for each parameter, the tool's description adds minimal meaning beyond the schema. The only added insight is linking 'repo_id' to the prefixing strategy, which the schema already mentions. For the remaining 8 parameters, the description provides no additional context.

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 verb 'index' and the resource 'a repository into LightRAG,' making the tool's purpose specific and distinct from sibling tools like 'rag_ask' or 'brain_remember.' The mention of multi-repo support further clarifies its scope.

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 explicit guidance on when to use this tool versus alternatives (e.g., 'brain_reindex' or 'rag_clear'), nor does it mention prerequisites or exclusion criteria. The usage context is entirely implied.

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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