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search_fulltext

Find exact keywords in the full body text of indexed PDFs, with highlighted snippets showing matched terms for precise research.

Instructions

Full-text keyword search across all indexed PDFs.

Exact KEYWORD search across the full body text of your indexed PDFs. For
meaning-based (semantic/vector) PDF search use search_pdf_knowledge; for
reference metadata only use search_library.

Searches the library_fulltext table for papers containing your keywords.
More powerful than title/abstract search — finds methodological details
in the body of papers.

Args:
    query: Keywords to search for, space-separated (words shorter than three
        letters and common stop-words are ignored).
    max_results: Maximum number of results to return (default 10).

Returns:
    A single TextContent listing each matching paper's title (or filename)
    with a highlighted snippet around the first matched keyword, or a
    "no results" / "no usable keywords" message.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
max_resultsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that it searches the library_fulltext table, ignores short words and stop-words, and returns highlighted snippets. It does not explicitly state read-only nature, but the output format implies no side effects. Still, it provides good behavioral context.

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 well-structured with a clear overview, context with siblings, and a formal Args section. Every sentence adds value, and the length is appropriate for the complexity.

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?

Given the tool's simplicity (2 params, output schema exists), the description is complete. It explains the search scope, return format, and special behavior (stop-word filtering). No gaps are evident.

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

Parameters5/5

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

Schema coverage is 0%, so description must explain parameters. It does so well: query is space-separated keywords with filtering behavior, max_results defaults to 10. This adds significant value beyond the schema fields.

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 it performs full-text keyword search across indexed PDFs. It explicitly distinguishes from sibling tools search_pdf_knowledge (semantic search) and search_library (metadata search), providing clear differentiation.

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 provides explicit guidance on when to use this tool: for exact keyword search, as opposed to semantic search or metadata-only search. It also notes that it is more powerful than title/abstract search for finding methodological details.

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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