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farena-ai

Sara

by farena-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Search her library

search_library
Read-onlyIdempotent

Search the owner's stored documents by words or meaning to find passages in PDFs, Word files, spreadsheets, books, and saved web pages that answer questions.

Instructions

Search the documents the owner keeps in Sara's library (PDFs, Word files, spreadsheets, books, saved web pages) by words and meaning. Use it for questions answered by the owner's documents; use search_memory for what Sara remembers from conversations. Returns up to 6 passages (each up to 1,400 characters), each headed by "[document title, place]" (page or section), or a line saying nothing was found. Read-only; documents shared in a single chat only are left out.

Input Schema

NameRequiredDescriptionDefault
queryYesWhat to look for, as words or a question. Up to 300 characters.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv0.1.1
    • changedInput schema / properties / query / description
      Previous value: -"What to look for, e.g. \"when does the lease end\"."New value: +"What to look for, as words or a question. Up to 300 characters."
    • addedInput schema / properties / query / examples
      Added value: +[
+  "when does the lease end",
+  "warranty period"
+]
    • addedInput schema / properties / query / maxLength
      Added value: +300
    • addedInput schema / properties / query / minLength
      Added value: +1
  2. First observedv0.1.0

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare the read-only, idempotent, closed-world profile, and the description adds behavior beyond that: the result cap (6 passages), per-passage length (1,400 chars), the '[document title, place]' header format, the explicit 'nothing found' line, and the chat-only-document exclusion. These are exactly the facts an agent needs to interpret results correctly.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four dense sentences with no filler, front-loaded with what the tool searches before routing to the alternative. The parenthetical file-type list and nested parentheticals in the last two sentences add bulk, though each still carries useful routing or result-shape 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 one-parameter retrieval tool with no output schema, the description fully covers what would otherwise be missing: result count, result size, result formatting, the empty-result signal, and coverage exclusions. Nothing an agent needs in order to call it and read its output correctly is absent.

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 coverage is 100% with a single, well-exampled 'query' parameter, so the schema does most of the work. The description adds one useful semantic point beyond the schema — matching happens on both words and meaning — which tells the agent that natural-language questions (not just keywords) are valid queries.

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?

States a specific verb (search) and resource (the owner's documents in Sara's library), enumerates the document types covered, and notes it works 'by words and meaning' (hybrid keyword + semantic). It is immediately distinguishable from search_memory, which covers conversational memory rather than 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 names the selection rule: use this for questions answered by the owner's documents, use search_memory for what Sara remembers from conversations. It also states a scoping exclusion (documents shared in a single chat only are left out), which prevents false-negative interpretations.

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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