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search_semantic

Search documents by meaning similarity to user intent, not exact keywords. Returns results ranked by semantic relevance.

Instructions

Search documents using semantic embedding-based matching. Returns results ranked by meaning similarity rather than exact keyword match. Use this when the user's intent matters more than exact wording. For exact keyword matching, use search_documents instead. Demo: simplified mock (no real embeddings API); no auth; same rate limit/timeout as other tools. Read-only; does not modify documents.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results to return. Use smaller values for quick lookups and larger values for comprehensive searches.
queryYesThe search query string. Supports keywords and phrases to match against document titles and content.
Behavior5/5

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

No annotations provided, but the description fully covers behavioral traits: read-only, mock embeddings (demo), no auth, same rate limit/timeout, and does not modify documents.

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?

Well-structured and concise. First sentence states core purpose, then usage guidance, then demo caveats. Every sentence adds value with no redundancy.

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

Completeness4/5

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

Covers purpose, usage, behavioral aspects, and schema. However, without an output schema, the description does not explain return values or result format, which could be helpful for a search tool.

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

Parameters4/5

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

Schema covers both parameters with descriptions (100% coverage). The description adds value by explaining the semantic nature, but does not add new parameter-level details beyond schema.

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?

Clearly states semantic embedding-based matching, ranking by meaning similarity, and explicitly distinguishes from exact keyword search, naming the sibling tool search_documents.

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 says when to use (user intent matters more than exact wording) and when not (use search_documents instead). Also includes demo context and limitations.

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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