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karenrebecag

analytics-mcp

by karenrebecag

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools target distinct resources: sites (configured web properties) and sources (adapter credentials). Their descriptions clearly separate these concepts, so an agent is unlikely to confuse them.

    Naming Consistency5/5

    Both tools follow the same 'list_[plural noun]' pattern, making the naming fully consistent and predictable.

    Tool Count3/5

    With only two tools, the server feels very thin for an analytics context, aligning with the borderline category of 1-2 tools. The tools are simple list operations, but the count itself is minimal.

    Completeness2/5

    The server covers only read-only listing of sites and sources, with no create, update, or delete operations, nor any analytics data access. For a server named 'analytics-mcp,' this is a significant functional gap.

  • Average 3.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 5 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.

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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

  • Behavior4/5

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

    The annotations already declare readOnlyHint=true and openWorldHint=true, covering the safety profile. The description adds useful behavioral context beyond that by stating 'Binding values stay server-side', which clarifies that sensitive binding values are not included in the response.

    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, compact sentence that front-loads the action and resource, then adds a valuable behavior note. Every word earns its place with no redundancy or filler.

    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?

    This is a simple, zero-parameter read-only list operation. The description names the output fields and clarifies a key behavior, which is sufficient given the absence of an output schema. Minor details like pagination or ordering are not addressed, but they are not clearly essential for a basic site-listing tool.

    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?

    The tool has zero parameters and the schema coverage is 100%, so the schema fully handles parameter semantics. The description adds no parameter meaning, but none is needed; the baseline for a zero-parameter tool applies.

    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 clearly states the action ('List') and the resource ('configured sites') and even enumerates the returned fields (id, name, bound source keys). However, it does not explicitly differentiate this from its sibling 'list_sources', so the agent must rely on the tool name to distinguish 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?

    The description implies the tool is used to list sites, but it gives no guidance on when to choose this over the sibling tool 'list_sources'. No exclusions, conditions, or alternative-selection guidance is provided.

    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 convey read-only and open-world behavior, so the description only needs to add context. It does add that the output includes credential presence, which is useful, but it does not clarify details such as whether credentials are validated or whether the list is exhaustive. The description is not contradictory to 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 a single, compact sentence that front-loads the core purpose and avoids filler. Every word adds meaning.

    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?

    For a parameterless, read-only listing tool with no output schema, the description provides sufficient context: what is listed and what aspect of those items is reported. The annotations cover the safety profile, so nothing critical is missing.

    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?

    The tool has zero parameters and the schema coverage is 100%, so there is no parameter meaning for the description to add. The baseline of 4 for a no-parameter tool 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 states a specific action ('List') and a specific resource ('registered analytics adapters') with an additional useful detail (credential presence). This clearly distinguishes it from the sibling tool list_sites, which targets a different resource type.

    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 resource name makes the intended use inferable—use this when you need analytics adapters and their credential status—but the description does not explicitly contrast it with list_sites or state when not to use it. Usage guidance is implied rather than stated.

    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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Glama performs regular codebase and documentation scans to:

  • 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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