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xsg22

mcp-apollo-config

by xsg22
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.2

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one retrieves a single key, the other retrieves all configs under a namespace. No overlap in functionality.

    Naming Consistency5/5

    Both tools follow a consistent 'apollo_get_<thing>' pattern in snake_case, making it predictable and easy to understand.

    Tool Count4/5

    With only 2 tools, the surface is thin but appropriate for a read-only query interface focused on single key and full namespace config retrieval.

    Completeness2/5

    The server lacks any write operations (create, update, delete) and only provides basic read capabilities. Significant gaps exist for managing configurations.

  • Average 2.8/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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.

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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 full behavioral burden. It merely says 'Query,' implying read-only, but lacks explicit safety info, error cases, or side effects. Minimal disclosure for a tool with 5 parameters.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single sentence, front-loaded with the main action. However, it sacrifices necessary detail for brevity, making it less helpful than a slightly longer but more informative version.

    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 the tool has 5 parameters (1 required) and an output schema, the description is too sparse. It does not provide context on default values, optional parameters, or how the output schema relates to the query.

    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 compensate. It mentions key and namespace/environment/cluster but does not explain the role of appId, env, clusterName, namespaceName, or how they scope the query. Insufficient for correct invocation.

    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 tool queries a single key in Apollo, specifying the hierarchical context (namespace/environment/cluster). It effectively distinguishes from the sibling tool apollo_get_namespace_configs which presumably retrieves all keys.

    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 guidance on when to use this tool versus the sibling or alternatives. The description does not explain prerequisites or scenarios where this tool is preferred.

    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 exist, so the description carries full disclosure burden. It only states 'query', but does not disclose read-only nature, potential side effects, pagination behavior, or response format.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single concise sentence, but it sacrifices completeness. While no word is wasted, the lack of detail makes it only adequate for a simple tool.

    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?

    Despite having an output schema, the description omits crucial usage details: no parameter explanations, no mention of pagination or defaults, and no behavioral context. Incomplete for a tool with 6 optional parameters.

    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?

    With 0% schema description coverage, the description should explain parameters. It does not mention any of the 6 parameters (appId, env, clusterName, namespaceName, pageSize, maxItems) beyond their names in 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 the action ('Query') and resource ('all configs under a namespace/environment/cluster'), and implicitly distinguishes from the sibling 'apollo_get_key' which likely targets a single key.

    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 implies use for querying all configs in a namespace, but provides no explicit guidance on when to use versus when not to, nor does it mention alternatives or prerequisites.

    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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