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normalzzz

EKS Metrics MCP Server

by normalzzz
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clear, distinct purpose: one fetches actual metrics data, the other lists available metric names. No overlap or ambiguity.

    Naming Consistency5/5

    Both tools use consistent snake_case verb_noun pattern: get_eks_metrics and list_api_server_metric_names. Naming is predictable and uniform.

    Tool Count4/5

    With 2 tools, the server is minimal but well-scoped for its purpose of EKS metrics retrieval. It slightly edges into 'thin' territory but is still reasonable.

    Completeness3/5

    Core operations (fetch metrics, list names) are present, but there are notable gaps like filtering, aggregation, or time-range queries. CloudWatch support is missing.

  • Average 2.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 4 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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the equivalent kubectl command for api_server, but does not address authentication requirements, error scenarios, rate limits, or idempotence. The read-only nature is implied but not confirmed, and no safety information 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.

    Conciseness3/5

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

    The description is concise at three sentences and starts with the main action. However, it includes a note about CloudWatch being reserved for future implementation, which is somewhat irrelevant. The structure is adequate but could be more organized by front-loading key usage constraints.

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

    Completeness1/5

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

    Given the tool has 10 parameters and an output schema, the description is extremely sparse. It does not explain how to connect to a cluster, specify metrics, or interpret results. The sibling tool is not referenced. For a tool with this complexity, the description fails to provide necessary context for correct usage.

    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?

    The input schema has 10 parameters with 0% description coverage, and the tool description does not mention any parameter, its purpose, or allowed values. For example, the 'source' parameter defaults to 'api_server', but the description only mentions it in passing. This leaves agents completely uninformed about parameter semantics.

    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 fetches EKS metrics from api_server or cloudwatch, with the api_server source explained. However, it does not differentiate from the sibling tool 'list_api_server_metric_names', which likely serves a complementary purpose. The purpose is clear but lacks explicit distinction.

    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 provides no guidance on when to use this tool versus alternatives, such as the sibling tool 'list_api_server_metric_names'. It only notes that CloudWatch is reserved for future use, implying current limitation. No when-to-use or when-not-to-use instructions are given.

    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 must fully disclose behavior. It only states that the tool lists metric names, implying a read-only operation, but does not mention any potential side effects, required permissions, or rate limits. The description lacks detail on what happens on errors or timeouts.

    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 very short (one sentence), but it omits important details. While it avoids verbosity, the brevity sacrifices clarity and completeness, making it less useful than a slightly longer, more informative description.

    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 4 parameters with 0% schema coverage and no annotations, the description is insufficient. It does not mention the output format (though an output schema exists), nor does it provide enough context for an agent to correctly invoke the tool or understand its behavior.

    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 provides no explanation of the parameters (kube_context, kubeconfig_path, timeout_seconds, metric_name_pattern). The description does not clarify how these parameters affect the results, leaving the agent to guess their purpose and valid values.

    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?

    Description clearly states the tool lists metric names from the Kubernetes API server /metrics endpoint. However, it does not differentiate from the sibling tool 'get_eks_metrics', which might retrieve actual metric values or focus on EKS-specific metrics.

    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 alternatives like 'get_eks_metrics'. There is no mention of prerequisites, recommended contexts, or situations where the tool should not be used.

    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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