OpenPapers

Extract training parameters

extract_training_parameters

Fetch explicitly labeled training parameters from scholarly papers, including source locators for provenance and verification.

Instructions

Extract only explicitly labeled training values with source locators.

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_training_parameters"
  }
}

Input Schema

Name	Required	Description	Default
`url`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 2, 2026

TDQS

B3/5.0

Behavior3/5

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

With no annotations provided, the description carries the full behavioral burden. It usefully discloses that only explicitly labeled values are extracted and that source locators are included in output. However, it does not describe behavior when no labeled values exist, whether the URL is fetched, or any side effects, though 'extract' implies a read-only operation.

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 a single, tight sentence with no filler. It front-loads the action ('Extract') and packs the key constraints ('only explicitly labeled', 'with source locators') into a compact structure.

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?

This is a simple one-parameter tool, but with no annotations and no output schema, the description should do more to explain what counts as a training value, what a source locator looks like, and how the URL relates to the source. The tool is probably callable, but an agent would be guessing about return structure and exact input expectations.

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%, so the description needs to clarify the url parameter. It does not state that the URL points to the paper or document from which training values should be extracted, nor does it explain what kind of URL is expected. The property name and format uri make this somewhat inferable, but the description adds no direct parameter meaning.

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 states a specific action—extract—and a specific resource: only explicitly labeled training values, with source locators delivered. It is distinguishable from sibling tools like extract_training_recipe or extract_paper_claims because it emphasizes labeled values and locators, but 'training values' is somewhat vague and the source is not named explicitly.

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 alternatives such as extract_training_recipe, extract_training_recipe_from_url, or extract_paper_facts. The description only states what the tool does, not when it should be preferred or when it should be avoided.

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