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r28ai

Web Research to Docs

by r28ai

firecrawl_research_papers_search

Read-onlyIdempotent

Search a research paper index with natural-language queries, filtering by author, category, and date range to find relevant papers.

Instructions

Search the research paper index with natural-language queries.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNoMaximum number of ranked papers to return. The server applies 40 when this is absent.
toNoInclusive upper bound on created/updated date.
fromNoInclusive lower bound on created/updated date.
queryYesNatural-language paper search query.
authorsNoAuthor substring filter. Repeat or pass a comma-separated value.
categoriesNoPaper category filter. Repeat or pass a comma-separated value.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

Annotations already declare readOnly, idempotent, and openWorld, so the safety profile is covered by structured data. The description adds nothing beyond that — no note on ranking behavior, result caps, pagination, or what happens when the index has no match.

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?

A single front-loaded sentence with zero filler and the core action first. It is efficient, though so terse that it borders on under-specification rather than disciplined conciseness.

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

Completeness3/5

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

All six parameters are documented in the schema and annotations cover the read-only safety profile, so the basics are in place. With no output schema and no description of return shape, ranking semantics, or default result count, an agent still lacks enough to predict what it gets back.

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 description coverage is 100%, so the baseline is 3. The description's phrase 'natural-language queries' merely restates the query parameter's own schema description and adds no meaning for k, from/to, authors, or categories.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Search the research paper index') plus the query mode ('natural-language'). It does not, however, distinguish itself from the sibling firecrawl_research_paper_get or from the broader tavily_search, so an agent must infer which search surface to pick.

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

Usage Guidelines2/5

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

There is no when-to-use, when-not-to-use, or alternative routing guidance. The presence of firecrawl_research_paper_get and tavily_search among siblings makes the absence of any 'use this instead of X when Y' statement a real gap.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.