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cessda

OpenAIRE MCP Server

by cessda
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Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: find_related_research discovers citations for a DOI, while get_metadata retrieves detailed metadata using an OpenAIRE dedup ID. The workflow described eliminates any ambiguity.

    Naming Consistency5/5

    Both tools use a consistent verb_noun pattern in snake_case: find_related_research and get_metadata. The naming is clear and predictable.

    Tool Count3/5

    With only two tools, the server feels thin for a domain like OpenAIRE, which could encompass searching by various criteria. The tools cover a specific workflow but the low count borders on inadequate for broader usage.

    Completeness3/5

    The tools handle citation discovery and metadata retrieval, but lack direct search by other attributes (e.g., author, title). The surface is limited to a narrow workflow, missing common functions like direct querying.

  • Average 4.5/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under Apache 2.0.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior3/5

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

    No annotations are provided, so the description is the sole source of behavioral info. It mentions data sources (OpenCitations, Crossref, DataCite) and coverage (publications, datasets, software) but omits details like rate limits, DOI-not-found handling, or whether full records are returned. The output schema fills some gaps but the description itself lacks full transparency.

    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 concise, well-structured with bullet points, and front-loaded with the core purpose. Every sentence adds value without redundancy.

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

    Completeness5/5

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

    Given the tool has only 2 parameters, no annotations, and an existing output schema, the description covers purpose, usage scenarios, data sources, and param behavior. It is complete for an agent to decide when and how to invoke the tool.

    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?

    With 100% schema coverage, the baseline is 3. The description adds value with examples for 'doi' and explains 'limit' default (200) and automatic pagination. This goes beyond the schema's minimal documentation.

    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 clearly states the tool finds publications, datasets, and software that cite a given DOI, using a specific verb ('find') and resource. It distinguishes itself from sibling tool 'get_metadata' by focusing on citation links rather than metadata retrieval.

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

    Usage Guidelines4/5

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

    The description explicitly lists four use cases under 'Use this tool when you want to:' (e.g., find what publications cite a dataset). It provides clear context but does not explicitly state when not to use it or compare with alternatives.

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

  • Behavior3/5

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

    No annotations provided. The description details what data is retrieved but does not explicitly state that the tool is read-only or mention any behavioral traits like rate limits, authentication, or side effects. It lacks disclosure about whether the operation is safe or destructive.

    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 well-structured with clear sections: summary, important note, workflow, and use cases. Every sentence adds value, no redundancy. It is front-loaded with the core purpose.

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

    Completeness5/5

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

    Given the presence of an output schema, the description appropriately omits return value details but covers all needed information for tool selection: what it does, how to use it, and prerequisites. It completes the picture for the user.

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

    Parameters5/5

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

    Schema coverage is 100% with only one parameter 'openaire_id'. The description adds significant value beyond schema by explaining the parameter format and giving an extraction workflow from find_related_research results, including an example.

    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 clearly states the tool retrieves comprehensive metadata from OpenAIRE Research Graph, listing specific use cases like getting abstracts, author information, access rights, etc. It distinguishes itself from the sibling tool 'find_related_research' by noting that it requires an OpenAIRE ID, not a DOI.

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

    Usage Guidelines5/5

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

    The description provides explicit workflow steps: first use find_related_research to get citations, extract OpenAIRE ID, then use this tool. It also lists when to use this tool (for abstracts, authors, etc.), implying when not to use it (when only need citations).

    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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