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mustafa-ahmadov

opendata-az-mcp

by mustafa-ahmadov
OverviewSchemaRelated ServersScoreDiscussions
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

    Each tool has a distinct purpose: searching datasets, getting metadata, and retrieving file download info. They form a clear sequential workflow with no overlap.

    Naming Consistency5/5

    All tools use consistent verb_noun snake_case naming (search_datasets, get_dataset_info, get_resource_info), making the pattern predictable.

    Tool Count5/5

    Three tools are perfectly scoped for a read-only open data portal: search, metadata, and resource details. No unnecessary tools.

    Completeness4/5

    The set covers the core workflow—search, get details, get download URL. A minor gap is the lack of a browse/list all datasets without a keyword, but the search tool effectively addresses discovery.

  • Average 4.2/5 across 3 of 3 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 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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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 present, so the description bears full responsibility. It describes the return format (titles, IDs, descriptions) and suggests a follow-up action (get_dataset_info), but does not disclose any side effects, rate limits, or authentication needs.

    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 three sentences, efficient, and front-loaded with the main purpose. Every sentence adds value without unnecessary detail.

    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 search tool with two parameters and an output schema, the description covers the purpose, usage context, and return content. It lacks parameter details but the output schema likely provides return structure. The suggestion for next steps adds completeness.

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

    Parameters2/5

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

    Schema description coverage is 0%. The description does not explain the 'query' and 'limit' parameters. While 'query' is somewhat implied, 'limit' is not clarified. The description fails to add meaning beyond the schema's basic titles.

    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 action: search the Azerbaijan Open Data Portal for datasets by keyword. It specifies the resource (datasets) and differentiates from sibling tools like get_dataset_info by explaining the return type and suggesting next steps.

    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?

    Explicit guidance is provided: 'Use this when the user asks for datasets on a topic'. It also suggests the next tool to call, but does not explicitly state when not to use or compare to alternatives.

    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?

    No annotations provided, so description carries full burden. It discloses returns (format, size, URL) and warns about large files. It does not mention authentication or rate limits, but these are likely irrelevant for a public open data platform. Overall, behavior is sufficiently transparent for a read-only operation.

    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?

    Description is three sentences, all essential. First sentence states purpose, second gives usage and return list, third adds important caution. No redundant information; front-loaded with key action.

    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's simplicity (one required parameter, output schema exists), the description covers return values, usage, and a caution. It lacks explanation of the parameter's source, which is a minor gap. Overall, it provides nearly complete context for an agent.

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

    Parameters2/5

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

    Schema description coverage is 0%, so description must explain the sole parameter 'resource_id'. However, it only states 'for a specific data file' without clarifying what a resource_id is or how to obtain it (e.g., from get_dataset_info or search_datasets). This lack of parameter context reduces usability.

    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 clearly states verb 'Get' and resource 'file format, size, and direct download URL for a specific data file on opendata.az'. It distinguishes from sibling tools (get_dataset_info, search_datasets) by focusing on file-level details rather than dataset metadata or search.

    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?

    Explicitly states when to use: 'Use this when the user needs to download a file or get the link.' Also provides a critical usage warning: 'Do not load large files into context—instruct the user to download from the URL or use the URL in code to fetch locally.' This gives clear context for appropriate use.

    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?

    No annotations provided, so description must cover behavioral aspects. It lists returned fields (organization, description, tags, resources) and implies read-only behavior. It could mention idempotency or safety more explicitly, but it's clear.

    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?

    Four well-structured sentences with front-loaded purpose, no unnecessary words. Clear flow of information.

    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 output schema exists, description covers key return fields. Tool is simple with one parameter. Workflow guidance compensates for missing schema descriptions. Complete enough for agent to use correctly.

    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 0% (no parameter descriptions). The description mentions dataset_id implicitly by referencing search_datasets output, but does not specify its format or origin explicitly.

    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?

    Clearly states the verb 'Get' and resource 'detailed metadata for a specific dataset'. Distinguishes itself from siblings by placing it in a workflow: after search_datasets and before get_resource_info.

    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?

    Explicitly instructs to use after search_datasets when details are needed, and names get_resource_info as the next step for download URLs.

    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 there are no obvious security issues.
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