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xaviviro

Opendata.cat MCP Server

by xaviviro
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JavaScript
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Server Quality Checklist

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

  • Latest release: v0.6.0

  • Disambiguation5/5

    Each tool serves a clearly distinct purpose: listing fields of a specific dataset, finding related datasets from other portals, and searching a radio archive. No overlap in functionality.

    Naming Consistency2/5

    Naming is inconsistent: 'list_dataset_fields' follows verb_noun, 'related_datasets' is a noun phrase without a verb, and 'search_radioteca' uses verb_noun with a proper name. No consistent pattern.

    Tool Count3/5

    With only 3 tools, the server feels very limited for an open data portal. However, the tools cover distinct areas, so the count is not unreasonable but could be expanded.

    Completeness2/5

    The server lacks fundamental open data operations such as searching datasets, listing all datasets, or retrieving dataset metadata. The inclusion of a radio archive tool seems out of scope and does not fill the gaps.

  • Average 4/5 across 3 of 3 tools scored.

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

    • 0 of 2 community issues answered or closed in the last 6 months
    • 4 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • 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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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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  ]
}

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 carries the burden. It implies a read-only operation by stating 'list', but does not explicitly disclose safety or authorization needs. For a simple tool with one parameter, this is minimally adequate.

    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 sentence that front-loads the core purpose and the returned fields. No redundant information.

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

    Completeness4/5

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

    For a low-complexity tool with one parameter and no output schema, the description sufficiently specifies what the tool does and what fields are returned. However, lacking an output schema, the description does not detail the structure (e.g., list of JSON objects), which would be beneficial.

    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?

    Schema coverage is 100% with dataset_id described as 'Dataset identifier'. The description does not add new meaning beyond the schema, so baseline score of 3 is appropriate.

    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 action ('list fields') and the resource ('dataset'), and lists the returned attributes (name, data type, description). This distinguishes it from siblings like get_dataset_info (dataset metadata) and query_dataset (run queries).

    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?

    The description provides no guidance on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or context-specific recommendations.

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

  • Behavior2/5

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

    No annotations are provided, so the description must disclose behavioral traits. It only mentions cross-portal scope but lacks details on authentication, results format, or behavior when no related datasets are found.

    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 sentence with no wasted words, efficiently conveying the tool's core purpose and scope.

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

    Completeness4/5

    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 is mostly complete. However, it lacks detail on what constitutes 'related' and whether results are limited or paginated, leaving minor gaps.

    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?

    Schema covers the parameter with a basic description, but the tool description adds crucial value by clarifying that the dataset_id refers to a dataset and results come from other portals, enhancing understanding beyond the schema.

    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?

    Description uses specific verb 'Find' and resource 'related datasets', and clearly distinguishes from sibling tools by specifying 'from OTHER portals', making the tool's unique purpose clear.

    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 hints at usage context with 'Great for discovering complementary data', but does not explicitly state when to use this tool versus alternatives like search_datasets or get_dataset_info.

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

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden. It discloses that the tool searches across multiple fields, returns three types of results with counts, and requires citation and linking. It does not mention side effects or permissions, but for a read-only search tool this is sufficient.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is 3-4 sentences that convey essential information: source, search scope, result types, and critical usage instructions. It is compact but not overly terse; each sentence serves a purpose. Minor redundancy in listing broadcasters twice could be tightened, but overall it's efficient.

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

    Completeness4/5

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

    Given the tool has 6 parameters (including enums) and no output schema, the description provides a solid overview: it names the fields searched, result categories with counts, and important behavioral notes (citation, URL). It could be more complete by describing the output fields or return structure, but the current level is adequate for an AI agent to use correctly.

    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?

    Schema coverage is 100%, so baseline is 3. The description adds value by explaining the year parameter's limitation (only year-level indexing) and suggesting a workaround using keywords and URL path, which goes beyond what the schema provides. This extra guidance justifies a score of 4.

    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 searches radio shows, episodes, and people from the specific Catalan radio archive radioteca.cat, listing exact broadcasters and document counts. It uniquely identifies the tool's resource and scope, effectively distinguishing it from sibling tools that focus on datasets.

    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 provides clear usage context (source, languages, searchable fields) and critical instructions like citing the source and including URLs for traceability. However, it does not explicitly describe when not to use this tool or mention alternative tools for different contexts.

    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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  • Evaluate tool definition quality.

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