search_frontend_knowledge

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description adds valuable context beyond these by disclosing the underlying retrieval backends (SQLite BM25 and PostgreSQL hybrid with pgvector) and the return structure (scores, per-branch ranks, retrieval mode, citations, provenance). This gives the agent a richer understanding of the tool's behavior without contradicting the annotations.

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 three sentences long, front-loaded with the main purpose, and each sentence adds meaningful detail. There is no redundancy or filler; it achieves high information density in a compact form.

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?

Given the tool's complexity (7 parameters, conditional backend behavior, and a rich return payload), the description covers the essential aspects: retrieval modes, filters, detail_level options, and output contents. The presence of an output schema covers the exact return structure, so the description need not list every field. It does not mention the default limit or specific prerequisites for the hybrid backend, but these are minor gaps.

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?

With schema description coverage at 0%, the description carries the burden of parameter explanation. It lists all parameters (query, kind, framework, tags, source_id, limit, detail_level) and groups the optional ones as filters, and it defines the allowed values for detail_level (brief|standard|full). However, it does not elaborate on the meaning of each filter (e.g., what values 'kind' or 'framework' accept), so the semantics remain somewhat shallow beyond the parameter names.

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's function with a specific verb and resource: 'Search frontend knowledge chunks.' It further distinguishes the tool by detailing its retrieval mechanisms (BM25, hybrid pgvector) and return values (scores, citations, provenance), which sets it apart from sibling tools like find_components or discover_frontend_resources.

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 clear context on how to use the tool: 'Pass query plus optional filters (kind, framework, tags, source_id), limit, and detail_level.' However, it does not explicitly state when to use this tool versus the alternatives, nor does it mention any exclusions or alternative tool recommendations. Usage guidance is implied but not explicitly differentiated.

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