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ivan1911

eva-custom-mcp

by ivan1911
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TypeScript

Server Quality Checklist

42%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.1

  • Disambiguation2/5

    The two tools have overlapping purposes—both retrieve a glossary article's content, differing mainly in lookup mechanism (by slug/URL vs. term resolution). An agent could easily be unsure which to pick for a given query, since both ultimately return article content.

    Naming Consistency4/5

    Both tools follow a consistent verb_noun pattern (glossary_article_get, glossary_search), sharing the 'glossary' prefix. The verbs 'get' and 'search' differ slightly in style but remain predictable and readable.

    Tool Count3/5

    At only 2 tools, the surface is thin for a server dedicated to a glossary domain. It's borderline—enough for basic read access but minimal for any meaningful workflow beyond retrieval.

    Completeness2/5

    The surface only supports reading/searching glossary articles. There is no create, update, or delete capability, and no listing endpoint. It's a read-only retrieval server with noticeable gaps relative to typical content management expectations.

  • Average 3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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

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  • This repository includes a README.md file.

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

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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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It does not state return format, whether it performs a fuzzy match or exact match, what 'resolve' means (normalization, synonym expansion, etc.), or behaviors on no-match/multiple-match scenarios. The agent is left blind to critical behavioral characteristics.

    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 efficient sentence with zero waste. It is appropriately brief for a one-parameter tool, though it could have used the brevity to add behavioral specifics without harm.

    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?

    No output schema exists and no annotations are provided, so the description must carry the completeness burden. For a simple single-parameter tool, it covers the basic purpose but fails to disclose match behavior, return shape, or relationship to the sibling tool. A resolution-style tool with no output specification leaves the agent unsure what it will receive.

    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 description coverage is 100%, and the query parameter has a clear description with an example ('API'). The description's use of 'Resolve' hints that the query may undergo normalization beyond exact match, adding marginal value, but the parameter is already well-documented by the schema, so baseline 3 is appropriate.

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

    Purpose3/5

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

    The description states a specific verb (Resolve) and resource (EvaTeam glossary term) with a clear outcome (return matching article content). It distinguishes from the sibling tool glossary_article_get in that this appears to resolve/search a term rather than fetch a specific article, but this differentiation is implicit rather than explicit, muddying what 'Resolve' actually means operationally.

    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?

    There is no guidance on when to use this tool versus glossary_article_get. The description does not clarify whether this is for exploring/resolving unknown terms while the sibling is for retrieving a known article, leaving the agent to infer the distinction from tool names alone.

    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 carries the full burden of behavioral disclosure. It discloses the resource is 'public' (an access level), but does not describe return format, error behavior for invalid slugs/URLs, or content structure. For a read tool with no annotations, this is minimal but not misleading.

    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?

    A single, clear sentence that is appropriately concise with zero wasted words. It reads naturally and front-loads the verb and resource.

    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?

    The tool is simple (1 param, 100% schema coverage, read-only nature), so the description covers the essentials. It could mention what fields the returned article contains or behavior when both slug and URL forms are given, but for a straightforward single-entity GET tool with strong schema coverage, this is reasonably complete.

    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% and the description reinforces the parameter by naming both acceptable forms ('by slug or URL'). The example in the schema additionally illustrates valid values. The description adds marginal value but the schema already handles parameter meaning well.

    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?

    Uses specific verb (Get) + resource (EvaTeam glossary article) and states the input modality (by slug or URL). It is clear about purpose. However, it does not explicitly distinguish itself from the sibling glossary_search tool beyond the read-specific framing, though 'get' vs 'search' implies retrieval-by-identifier vs lookup.

    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 description provides minimal guidance on when to use this tool. The contrast with the sibling glossary_search is only implicit (get by exact slug/URL vs search). There is no explicit when/when-not guidance or alternative naming, though the 'by slug or URL' phrasing implies usage when you know the identifier.

    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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