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search_papers

Find relevant passages in your ingested research papers by asking a natural-language question. Returns ranked excerpts with page numbers and sources.

Instructions

Search the ingested paper collection using a natural-language query. Returns the most relevant passages with page numbers and sources, ranked by a cross-encoder reranker.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of results to return (default 5).
queryYesThe search query in natural language.
Behavior4/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals that results are passages rather than full papers, include page numbers and sources, and are ranked by a cross-encoder reranker, which meaningfully describes behavior beyond the tool name.

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?

The description is two sentences with the main action front-loaded. The second sentence efficiently packs return format and ranking behavior. No unnecessary words or 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?

For a simple two-parameter search tool with no output schema, the description adequately conveys what results include (passages, page numbers, sources, ranked order) and the scope (ingested collection). It does not specify the exact result data structure, but that is minor given the straightforward contract and the clear sibling relationship.

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%, so the baseline is 3. The description reinforces that the query is natural-language but adds no new detail about the limit parameter or query formatting. It does not need to compensate for schema gaps.

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 states a specific verb ('search') and resource ('ingested paper collection'), and clarifies that queries are natural-language. It also distinguishes itself from the sibling 'ingest_paper' by focusing on retrieval of already-ingested content rather than adding papers.

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

Usage Guidelines4/5

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

The phrase 'ingested paper collection' clearly implies this is for searching papers previously added, giving the agent a strong contextual signal about when to use it. However, it does not explicitly name the alternative or state exclusions, so it falls short of full routing 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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