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UnboundCompute

lachesis-mcp

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concept_search

Search code by behavior instead of spelling. Uses a local embedding model to match code concepts offline, so you can find the relevant functions even when the exact names are unknown.

Instructions

Search code by behavior rather than spelling using an optional local embedding model. Search is offline-only and never downloads implicitly; install the concept-search extra and run lachesis concept-model download.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
modelNoBAAI/bge-small-en-v1.5
queryYes
formatNotext (compact, default) | json (structured result page)
offsetNo
min_scoreNo
Behavior4/5

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

No annotations are provided, so the description must carry behavioral disclosure. It meaningfully discloses offline-only behavior and that no implicit downloads happen, plus the explicit installation step. That is valuable non-obvious context beyond the bare fact that this is 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 sentences, front-loaded with the core purpose, and no wasted words. The critical distinction (behavior vs spelling) is stated first, and setup details follow economically.

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?

The tool has six parameters, no output schema, and no annotations, so the description should fill more gaps. It covers offline behavior and setup, but does not explain what results look like, how scoring works, how pagination behaves, or what the model parameter means. This is enough to orient an agent, but not enough to invoke it correctly with confidence.

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 only 17%, so the description must compensate for parameter meaning. It explains the semantics of semantic search generally and refers to the embedding model, but it does not explain query, limit, offset, min_score, model, or format beyond what defaults suggest. This leaves the agent without meaningful guidance for most parameters.

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 and resource: 'Search code by behavior rather than spelling.' This clearly distinguishes concept_search from sibling tools like search by identifying it as semantic/behavior-based rather than lexical. The name and title are not just restated; the description earns its purpose.

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 description conveys when to consider this tool: when you want behavior-based search rather than spelling/lexical search, and it provides important setup context. However, it never explicitly names alternatives or says when not to use this tool, leaving part of the routing decision to the agent rather than stating it.

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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