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Handigraphs

handigraphs-stats-api-mcp

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by Handigraphs

Server Quality Checklist

67%
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  • Latest release: v0.2.4

  • Disambiguation5/5

    Each tool serves a distinct function: discovery (list), introspection (describe), and data access (query). There is no overlap in their purposes, and the descriptions make it clear when to use each.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (list_resources, describe_resource, query_stats). The naming is predictable and reinforces the workflow.

    Tool Count5/5

    Three tools is an ideal size for this server's scope: discover, understand, and query. Each tool is necessary and none feel redundant or missing.

    Completeness5/5

    The tool set provides a complete workflow for a stats API client: listing available resources, getting detailed descriptions, and querying with pagination support. There are no obvious gaps for the stated purpose.

  • Average 3.4/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 15 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
  • This repository is licensed under Apache 2.0.

  • This repository includes a README.md file.

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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?

    Annotations already declare readOnlyHint=true and openWorldHint=true, which the description does not contradict. The description adds that the output includes live resource, metric, split, unit, and filter discovery, but it does not disclose error behaviors, output format, or additional side effects. This is basic context beyond annotations but not rich.

    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 a single sentence with no filler, front-loaded with 'Return.' It is efficient but arguably too terse to be fully useful; however, conciseness is about economy, and this achieves that. It could earn a 5 if it packed more meaning, but it leaves too much to inference.

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

    Completeness2/5

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

    Given two required parameters with zero schema descriptions, no output schema, and siblings that could be confused with this tool, the description is incomplete. It fails to explain target parameters, output structure, or the precise scope of 'discovery,' so the agent may not know how to invoke the tool correctly.

    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 the description must compensate. It mentions 'resource' as part of the discovery output but does not explain the parameters 'sport' or 'resource' (e.g., expected format or values). The agent has no guidance on how to fill the required fields, making this a significant gap.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description uses the verb 'Return' and identifies what is returned: 'resource, metric, split, unit, and filter discovery.' This clearly indicates the tool provides discovery information. However, the term 'live' is ambiguous (could mean real-time data vs. metadata), and the description does not explicitly contrast with siblings, though the title and tool name suggest it describes a specific API resource.

    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 word 'discovery' implies use when exploring available metrics/filters before querying, but there is no explicit when-to-use or what-not-to-use instruction. No alternatives are named, leaving the agent to infer that this complements 'list_resources' and 'query_stats'.

    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?

    Annotations already declare readOnlyHint=true and openWorldHint=true, so the read-only and open-ended nature is covered. The description adds the context that resources are 'live' and 'public', which slightly expands on the annotations, but it does not disclose behavior like pagination, response format, or what happens when no sport filter is provided.

    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?

    Single sentence with no redundancy. It front-loads the verb and object, and every word adds meaning. Very concise and well-structured.

    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 list tool with annotations, the description is adequate but has clear gaps. It lacks explanation of the optional parameter's behavior and does not describe the response shape (no output schema). The openWorldHint and readOnlyHint provide some context, but the missing parameter semantics and output details keep it from being fully complete.

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

    Parameters1/5

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

    Schema description coverage is 0%, and the description does not mention the 'sport' parameter at all. With no compensation, the agent has no idea how to use the optional sport parameter, what values to pass, or whether it filters the list. This is a significant gap since the schema provides only the parameter name and length constraints.

    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 states a specific action 'List' on a specific resource type ('live sports and stats resources') and scopes it to 'public Handigraphs discovery'. This clearly distinguishes from siblings 'describe_resource' and 'query_stats', which describe or query resources rather than list them.

    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?

    No explicit guidance on when to use this tool versus the alternatives. The description only states what it does, leaving implications that you use it to list resources, but it does not mention when to prefer it over describe_resource or query_stats, nor any prerequisites or exclusions.

    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?

    The annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds that it queries 'live' data, that pagination remains in the tool, and that describe_resource should be used first, offering behavior beyond the 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 two sentences, front-loaded with the core purpose, and includes a key usage directive. Every word adds value without redundancy.

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

    Completeness2/5

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

    With 17 parameters, no output schema, and no parameter explanations, the description is far too minimal. It does not cover how to construct queries, interpret responses, or handle the complex filter and metrics structures, making it inadequate for the tool's complexity.

    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?

    The schema has 17 parameters with no descriptions, and the description only mentions cursor for pagination. It doesn't explain the meaning of sport, resource, metrics, filters, or other parameters, leaving the agent to infer from names and schema types.

    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 it queries one live Handigraphs Stats API resource, using a specific verb and resource. It distinguishes from siblings by indicating it's for querying data, while list_resources and describe_resource serve other purposes.

    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 instruction to 'Use describe_resource first' provides a clear prerequisite and workflow guidance. Pagination via cursor is noted, implying this tool handles subsequent pages. While alternatives aren't explicitly named, the sibling names imply their distinct roles.

    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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