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possible055

Relace MCP Server

by possible055

cloud_search

Read-onlyIdempotent

Search codebases with natural language queries to find files, classes, or functions by their behavior and purpose, even without knowing exact names.

Instructions

Search for code by meaning using AI embeddings. Requires cloud_sync first.

Use for conceptual queries where you don't know the exact file, class, or function name. Prefer agentic_search when you know the exact identifier or symbol name.

Prerequisite: cloud_sync must have been run at least once. Fails if RELACE_API_KEY is not set or no sync state exists.

Returns: {results (list), result_count (int), warnings (list[str]), query (str), branch (str), repo_id (str)}. Check warnings[] for stale index alerts (e.g., uncommitted local changes).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language description of the code to find. ✅ 'function that validates JWT and returns user ID' ✅ 'rate limiting middleware for HTTP requests' ❌ 'auth' (too vague — low-relevance results) Be specific about behavior, not just topic.
branchNoBranch to search (null = API default branch).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations already define read-only, idempotent, non-destructive behavior. Description adds failure modes (RELACE_API_KEY, sync state) and return structure with warnings for stale indices.

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?

8 sentences, all value-adding: purpose, prerequisite, usage guidance, failure conditions, return type, warning tip. No redundancy.

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

Completeness5/5

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

For a search tool with 2 params, annotations, and output schema, the description covers purpose, prerequisites, usage, failures, return shape, and warnings. Complete.

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% with detailed descriptions for both parameters. The tool description does not add extra parameter information, so baseline 3 is appropriate.

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 it searches for code by meaning using AI embeddings, distinguishing it from agentic_search for exact identifier searches.

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

Usage Guidelines5/5

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

Explicitly tells when to use (conceptual queries) and when to prefer agentic_search (exact identifiers). Also mentions prerequisite cloud_sync and failure conditions.

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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