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vinvuk

Apiverket MCP Server

by vinvuk
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Server Quality Checklist

75%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools serve clearly distinct purposes: govdata_discover for exploring available endpoints and govdata_query for retrieving data. No functional overlap exists.

    Naming Consistency5/5

    Both tools follow a consistent govdata_verb pattern using snake_case, making their purpose predictable.

    Tool Count3/5

    With only 2 tools for a domain covering 139 endpoints across 16 categories, the count is minimal but appropriate for the discover-query pattern. However, it may feel thin for the broad scope.

    Completeness4/5

    The pair covers the complete lifecycle: discovery then retrieval. For a read-only government data API, this is sufficient. Minor gaps like built-in pagination handling are mitigated by error messages.

  • Average 4.6/5 across 2 of 2 tools scored.

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

    • 0 of 1 community issues answered or closed in the last 6 months
    • 11 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 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.

  • This repository includes a glama.json configuration file.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

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

  • Behavior4/5

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

    Annotations already mark the tool as read-only and idempotent. The description adds detail about return values (paths, parameters, descriptions) and behavior with no arguments (returns categories). This supplements the annotations without contradicting them.

    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 (Args, Examples, Returns). It is concise, uses bullet points for readability, and every sentence serves a purpose. No unnecessary repetition or fluff.

    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's simplicity (2 optional parameters, no output schema, simple behavior), the description is fully complete. It covers all inputs, explains behavior comprehensively, and describes return values. No gaps remain.

    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 parameter usage (e.g., 'Use without query to browse a category'), providing examples, and clarifying constraints (maxLength hinted in schema). This extra context justifies a score above baseline.

    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's purpose: 'Search and browse available Swedish government data endpoints'. It specifies the verb (search and browse), resource (endpoints), and distinctively positions it as a discovery tool complementary to the sibling govdata_query.

    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 states 'Use this tool to find the right endpoint before calling govdata_query', providing clear context. It includes examples for different use cases and explains behavior with no arguments. However, it does not explicitly mention scenarios where this tool should not be used, leaving a minor gap.

    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?

    Annotations declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds value by explaining the return format ('meta envelope with data'), error handling (clear messages, automatic truncation with pagination guidance), and that large responses are truncated. No contradiction with annotations.

    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 (Args, Examples, Returns, Error handling). It is appropriately sized—every sentence provides necessary information without redundancy. Examples are concrete and diverse.

    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?

    Despite no output schema, the description explains the return structure and error behavior. It covers all 3 parameters thoroughly, references the sibling tool, and gives ample examples. The tool's complexity is well-addressed.

    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%. The description enhances understanding with examples showing how path_params substitute in the URL and query_params are for query strings. It also explains what types of parameters are expected ('string' for path_params; 'string, number, boolean' for query_params), adding meaning 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?

    The description clearly states 'Call any Apiverket API endpoint to retrieve Swedish government data.' It specifies the verb 'Call' and the resource 'any endpoint'. It distinguishes from the sibling tool 'govdata_discover' by directing users to use discover first for finding endpoints, then query for calling them.

    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 explicitly says 'Use govdata_discover first to find the right endpoint path and required parameters. Then call this tool...' This provides clear when-to-use guidance and names the alternative tool.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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