search_hybrid_context

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

No annotations are provided, so the description fully discloses behavior: read-only, dual query against vector and graph databases, no side effects, returns Markdown with file paths, line numbers, code snippets, and dependencies. It also mentions the failure case when semantic search is disabled.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is somewhat long but well-structured with sections, bullet points, and warnings. Every sentence provides value, though it could be slightly more concise. It is front-loaded with the core purpose and usage guidance.

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

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

Despite no output schema, the description explains return format (Markdown with file paths, line numbers, etc.) and mentions supported languages (Java, Kotlin, TypeScript). It covers failure modes, prerequisites, and usage recommendations, making it complete for the tool's complexity.

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

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

Schema description coverage is 100%, but the description adds extra guidance beyond schema: query should be 2-5 words, max_results can be increased for broad discovery, and repo_name should be included to avoid cross-repository pollution. This adds meaningful context for effective use.

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

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

The description clearly states the tool performs 'read-only semantic and structural code search' combining vector embeddings and graph analysis. It specifies that it locates code based on natural language descriptions and returns files, signatures, and documentation. This distinguishes it from siblings like find_callers (reverse dependency lookup) and explore_file (file structure inspection).

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

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

The description provides explicit guidance on when to use this tool ('first step when exploring unfamiliar code') and when not to use it (for finding all usages of a specific function, instead use find_callers). It also details prerequisites: requires initialized vector and graph DBs, and warns about lightweight mode with alternative tools listed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.