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search_semantic

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Find space regulatory filings by meaning using natural language queries. Uses semantic embeddings to match intent, not just keywords.

Instructions

Semantic vector search across filing text using natural language. Uses nomic-embed-text-v1.5 embeddings to find filings by meaning, not just keywords. Note: FCC filings have limited embedding coverage — use search_filings for FCC keyword search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 10, max 50)
queryYesNatural language search query
agencyNoFilter by source agency
filed_afterNoMinimum filed date (YYYY-MM-DD)
filed_beforeNoMaximum filed date (YYYY-MM-DD)
min_similarityNoMinimum cosine similarity threshold (default 0.5)
Behavior5/5

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

The description adds value beyond annotations by specifying the use of nomic-embed-text-v1.5 embeddings and noting limited FCC filing coverage. This complements the readOnlyHint annotation with operational constraints.

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 concise sentences, each serving a clear purpose: the first defines the tool's function and method, the second provides a critical caveat and alternative. No filler or redundant information.

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 tool with 6 parameters and no output schema, the description sufficiently covers its purpose, embedding technique, limitation, and alternative tool, providing a complete mental model for the agent.

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?

With 100% schema coverage, the description adds context about the embedding model and why parameters like limit might matter, but does not repeat schema details. This provides meaningful additional guidance.

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 the tool performs semantic vector search using natural language, distinguishing it from keyword search by explicitly mentioning 'meaning' vs 'keywords'. It also names the sibling tool search_filings for FCC 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?

The description explicitly advises to use search_filings for FCC keyword search due to limited embedding coverage, providing clear when-to-use and when-not-to-use guidance.

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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