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nicolasmosquerar

k8s-mcp-assistant

by nicolasmosquerar
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one checks pod status for an application, while the other retrieves logs for a specific pod. There is no overlap or ambiguity between these functions, making it easy for an agent to select the correct tool.

    Naming Consistency5/5

    Both tools follow a consistent naming pattern using 'k8s.' prefix and snake_case (app_status, pod_logs). This uniformity enhances readability and predictability across the tool set.

    Tool Count2/5

    With only two tools, the server feels thin for a Kubernetes assistant domain, which typically involves more operations like scaling, deployment, or resource management. This limited scope may hinder agent workflows.

    Completeness2/5

    The tool set is severely incomplete for Kubernetes operations, lacking essential CRUD actions such as creating, updating, or deleting resources. Agents will face significant gaps when trying to perform common tasks in this domain.

  • 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
    • 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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

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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.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    No annotations are provided, so the description carries full burden. It states the tool searches for pod status but doesn't disclose behavioral traits like whether it's read-only, what format the status returns, error conditions, or any rate limits. This leaves significant gaps in understanding how the tool behaves.

    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 that directly states the tool's purpose. It's appropriately sized and front-loaded with no wasted words, though it could benefit from slightly more detail given the lack of annotations.

    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 no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., status format, possible values) or address behavioral aspects like error handling. For a tool with 2 required parameters and no structured output documentation, this leaves the agent with insufficient context.

    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%, so the schema already documents both parameters ('namespace' and 'app'). The description mentions 'nombre de aplicacion' (application name) which aligns with the 'app' parameter but doesn't add meaningful semantics beyond what the schema provides. Baseline 3 is appropriate when schema does the heavy lifting.

    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's purpose: 'busca el estado de los pods' (searches for pod status) given an application name. It specifies both the action (search) and resource (pod status), though it doesn't explicitly differentiate from the sibling tool 'k8s.pod_logs' which likely retrieves logs rather than status.

    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. It doesn't mention the sibling tool 'k8s.pod_logs' or any other context for choosing between them, leaving the agent without explicit usage instructions.

    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 full burden. While it mentions the tool is useful for debugging/troubleshooting, it doesn't disclose important behavioral traits like authentication requirements, rate limits, whether it's read-only or destructive, or what format/logs are returned. For a log retrieval tool with no annotation coverage, this is insufficient.

    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 appropriately concise with two sentences. The first sentence states the core purpose, and the second provides usage context. There's no unnecessary verbosity, though it could be slightly more structured.

    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's complexity (7 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain what the tool returns (log format, structure), doesn't mention authentication or permission requirements, and provides minimal behavioral context. For a Kubernetes log retrieval tool, this leaves significant gaps.

    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?

    The schema description coverage is 100%, so all parameters are documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema. According to guidelines, when schema coverage is high (>80%), the baseline score is 3 even without parameter info in the description.

    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's purpose: 'Obtiene los logs de un pod específico en Kubernetes' (Gets the logs of a specific pod in Kubernetes). It specifies the verb ('obtiene' - gets) and resource ('logs de un pod'), but doesn't distinguish it from the sibling tool 'k8s.app_status' which likely serves a different purpose.

    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 some usage context with 'Útil para debugging y troubleshooting' (Useful for debugging and troubleshooting), which implies when to use it. However, it doesn't explicitly state when NOT to use it or mention alternatives like the sibling tool 'k8s.app_status' for different monitoring needs.

    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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