OpenPapers

Extract paper facts

extract_paper_facts

Extract verified compatibility and canonical facts from a paper URL, each with source locators, without inferring unsupported values.

Instructions

Extract compatibility heuristic facts plus canonical v5 facts with source locators; no unsupported values are inferred.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>"
    },
    "name": "extract_paper_facts"
  }
}

Input Schema

Name	Required	Description	Default
`url`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 2, 2026

TDQS

C2.9/5.0

Behavior3/5

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 does add a meaningful constraint ('no unsupported values are inferred') and states that results include source locators, which helps set expectations. But it omits error behavior, side effects, access requirements, and how unsupported values are actually handled.

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?

The description is a single focused sentence with no filler and a clear action verb. The phrase 'compatibility heuristic facts plus canonical v5 facts' is dense and jargon-heavy, which slightly hurts readability, but it remains appropriately concise.

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

Completeness2/5

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

There is no output schema and no annotations, so the description must explain what 'facts' means and what the result looks like; it does not. It also fails to provide enough context for an agent to confidently choose this tool among the many extraction-oriented sibling tools.

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

Parameters2/5

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

Schema description coverage is 0%, and the description never mentions the url parameter. The schema only provides format:uri, while the description does not clarify whether the URL should point to a paper page, PDF, or repository, or how the URL relates to the extraction. The single parameter is self-explanatory in name but the description adds no semantic value.

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?

The description uses a specific verb ('extract') and a clear resource ('paper facts'), and goes beyond the title by distinguishing compatibility heuristic facts from canonical v5 facts, noting source locators, and stating an inference policy. However, the terms 'compatibility heuristic facts' and 'canonical v5 facts' are unexplained jargon, leaving the exact scope somewhat ambiguous.

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 guidance about when to use this tool versus siblings like extract_paper_claims, extract_training_parameters, or read_paper. The description gives no prerequisites, no exclusion criteria, and no mention of what kinds of URLs or papers are appropriate inputs.

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