OpenPapers

Extract training recipe from paper

extract_training_recipe_from_url

Populate training recipe fields from a paper URL, converting explicitly reported details into structured data for reproducible research.

Instructions

Populate only explicitly reported training recipe fields from a paper URL.

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_recipe_from_url"
  }
}

Input Schema

Name	Required	Description	Default
`url`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 2, 2026

TDQS

B3.3/5.0

Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It adds one meaningful behavioral constraint: 'only explicitly reported' fields are populated, which tells the agent not to infer missing values. However, it does not mention side effects, fetching behavior, failure modes, or return format.

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 concise sentence with no filler. The key behavioral qualifier 'only explicitly reported' is front-loaded, followed immediately by the input source. Every word earns its place.

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?

The tool is simple, with one parameter and no output schema, so a short description is acceptable. However, it omits any indication of what fields constitute a training recipe, what the return value looks like, and how it relates to sibling extraction tools. It is adequate for basic selection but not fully enough for confident invocation in all cases.

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?

The schema provides the 'url' parameter with format 'uri' and maxLength, but no description. The tool description adds the important semantic qualifier that it is a 'paper URL'. This is useful but minimal; it does not fully compensate for the 0% schema description coverage.

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 names a specific action ('populate') and resource ('training recipe fields') and clarifies the input source ('a paper URL'). It is clear and non-tautological, but it does not explicitly distinguish itself from the sibling tools 'extract_training_recipe' or 'extract_training_parameters' beyond the URL-based name.

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 explicit guidance about when to use this tool versus alternatives like 'extract_training_recipe' or 'extract_training_parameters'. The phrase 'from a paper URL' implies the input context, but the description does not state exclusions, prerequisites, or decision boundaries.

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