OpenPapers

Extract training recipe

extract_training_recipe

Extract training parameters from a research paper, returning only source-backed values and never guessing missing details.

Instructions

Return only source-backed training parameters. Missing values are never guessed.

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": {
      "paper_id": "<paper_id>"
    },
    "name": "extract_training_recipe"
  }
}

Input Schema

Name	Required	Description	Default
`paper_id`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 2, 2026

TDQS

B3.1/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It provides a meaningful guarantee against hallucination ('Missing values are never guessed') and clarifies that only source-backed values are returned, but it does not explain what happens when no values exist, whether it returns partial data, or what error/edge-case behavior to expect.

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 extremely concise: two short sentences with no wasted words. It front-loads the core function ('Return only source-backed training parameters') and then adds the key constraint about not guessing missing values.

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?

For a single-parameter tool with no output schema and no annotations, the description defines the basic return contract but leaves gaps. It does not define what counts as a 'training recipe', nor does it distinguish itself from 'extract_training_parameters', making selection among siblings less reliable.

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 should compensate for the parameter documentation. It does not mention 'paper_id' at all or explain how it should be formatted, though the parameter name is reasonably self-explanatory.

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 verb ('Return'), a clear resource ('source-backed training parameters'), and a behavioral constraint ('Missing values are never guessed'). However, it does not differentiate from the sibling tool 'extract_training_parameters', which likely performs a very similar function.

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. The description does not mention prerequisites, exclusions, or the relationship to similar tools like 'extract_training_parameters' or 'extract_training_recipe_from_url'.

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