Wikexa Knowledge

papers

Academic paper metadata from 57M+ works — title, abstract, authors, citations, DOI and open access URL. Covers every field: CS, medicine, physics, economics, biology, and more. Search by keywords (e.g. "transformer attention"), by topic name (e.g. "machine learning"), or by OpenAlex topic ID. Returns papers sorted by citation count. Source: OpenAlex (CC0 metadata). Use this when the user needs scholarly references, citation counts, or research context that Wikipedia does not cover.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "papers"
  }
}

Input Schema

Name	Required	Description
`year`	No	Publication year to filter on, e.g. 2023.
`limit`	No	Maximum papers to return, 1-20 (default 5).
`query`	No	Keywords to search for. Matches topic names to find the right research area, then filters by title and abstract. Example: "transformer attention", "CRISPR gene editing".
`topic`	No	Topic name or OpenAlex topic ID. Names are resolved automatically (e.g. "machine learning" → T10135). Use an ID like "T10135" if you already know it. Optional — query alone works.

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedOct 7, 2026

changedInput schema / properties / query / description

Previous value: -"Keywords to match in title and abstract (all terms must appear). Combines with topic to narrow results."New value: +"Keywords to search for. Matches topic names to find the right research area, then filters by title and abstract. Example: \"transformer attention\", \"CRISPR gene editing\"."

changedInput schema / properties / topic / description

Previous value: -"OpenAlex topic ID, e.g. \"T10135\" (Machine Learning), \"T10461\" (Quantum Computing). Required unless query is very specific."New value: +"Topic name or OpenAlex topic ID. Names are resolved automatically (e.g. \"machine learning\" → T10135). Use an ID like \"T10135\" if you already know it. Optional — query alone works."

AddedAug 27, 2026

TDQS

A3.9/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the data source (OpenAlex, CC0), the ordering behavior (sorted by citation count), and the scale (57M+ works), which is real behavioral context. However, it doesn't address rate limits, pagination, or what happens when a topic name cannot be resolved — significant gaps for a tool with no annotations.

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?

Well-structured and front-loaded: source and scope first, then search modes, then a when-to-use cue. It's a bit list-heavy and the 'Covers every field' sentence is slightly promotional, but there is no wasted filler.

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?

Despite no output schema and no annotations, the description covers the return fields, sort order, data source, and three input modes. It's missing explicit pagination/rate-limit behavior and any error handling for unresolved topics, which for a 57M-record search tool would be the remaining pieces an agent might need.

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 each parameter already has documentation. The description adds value by giving concrete query examples ('transformer attention') and explaining the topic name→ID resolution flow ('machine learning' → T10135), but the schema already documents all four parameters. Baseline 3 is appropriate when the schema does the heavy lifting.

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+resource ('Academic paper metadata from 57M+ works'), lists the returned fields (title, abstract, authors, citations, DOI, open access URL), and enumerates the search modes (keywords, topic name, OpenAlex topic ID). Its scale and scholarly-focus make it clearly distinct from siblings like article, define, and lookup.

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 description explains three search modes and ends with an explicit context cue ('Use this when the user needs scholarly references, citation counts, or research context that Wikipedia does not cover'), which is a strong differentiator. It lacks a 'do not use when' clause or alternative sibling routing, so it doesn't reach 5.

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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