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mcp-zenodo

mcp-zenodo

by MSKazemi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

detect_data_type

Identify whether a Zenodo record is a dataset, software, or article. Provides a confidence score for the classification.

Instructions

Determine if a Zenodo record is a dataset, software, or article.

Args:
    record_id: The ID of the Zenodo record to analyze
    
Returns:
    Dictionary containing the detected data type and confidence score

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": {
      "record_id": "<record_id>"
    },
    "name": "detect_data_type"
  }
}

Input Schema

NameRequiredDescriptionDefault
record_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

The description discloses the return format (dictionary with data type and confidence score), which is useful. However, without annotations, it does not state whether the operation is read-only, what permissions are required, or how errors are handled. This leaves gaps for a tool that could involve network calls.

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 clear purpose statement followed by a brief Args/Returns section. It is front-loaded with the main action and contains no redundant filler.

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 simple tool with one parameter and no output schema, the description provides the essential information about input and output. However, it does not explain what confidence score means, possible data type values beyond the initial three, or fallback behavior for unclassifiable records.

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

Parameters4/5

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

The input schema only defines record_id as a string with no explanation. The description compensates by specifying that it is 'The ID of the Zenodo record to analyze', giving semantic meaning. However, it does not specify the expected format (e.g., numeric ID or DOI), leaving some ambiguity.

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?

The description uses the specific verb 'Determine' with a clear resource ('a Zenodo record') and identifies the classification categories (dataset, software, article). This distinguishes it from sibling tools like search_records or get_metadata, which perform different operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for classification but does not explicitly state when to use this tool versus siblings, nor does it mention any exclusions. There is no guidance on what to do if the record is not one of the listed types.

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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