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devops-vesi

DevOps MCP

by devops-vesi
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JavaScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.1

  • Disambiguation5/5

    The two tools have clearly distinct purposes: 'fetch_skill' retrieves a specific skill for generating documentation or applying standards, while 'list_skills' enumerates all available skills to inform selection. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern ('fetch_skill', 'list_skills'), using snake_case throughout. The naming is predictable and aligns with common conventions for tool design.

    Tool Count2/5

    With only 2 tools, the server feels thin for a 'DevOps MCP' scope, which typically involves broader operations like deployment, monitoring, or automation. This limited set may not adequately cover the expected domain.

    Completeness2/5

    The toolset is severely incomplete for DevOps, lacking essential operations such as managing deployments, configuring infrastructure, or handling CI/CD pipelines. It focuses narrowly on skills/templates, leaving significant gaps in core DevOps workflows.

  • 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
    • 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.

    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",
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  ]
}

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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 provided, so description carries full behavioral burden, yet discloses only scope ('Lists all available'). Omits idempotency, side effects, caching behavior, rate limits, or auth requirements. 'Lists' implies read-only but doesn't explicitly confirm safety.

    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?

    Two sentences with zero waste: first establishes purpose and scope, second provides workflow guidance. Front-loaded with critical information and appropriate length for tool complexity.

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

    Completeness3/5

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

    Adequate for simple list operation with single optional parameter. However, lacks output schema and description doesn't compensate by describing return structure (array format, skill object fields), leaving response format ambiguous.

    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% with 'category' parameter fully described as 'Optional category to filter skills'. Description adds no additional parameter semantics, but baseline 3 is appropriate since schema requires no compensation.

    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?

    Description provides specific verb ('Lists') + resource ('DevOps skills') with concrete examples in parentheses (SAP Fiori documentation templates, code standards, best practices). The 'list' vs sibling 'fetch' naming pattern clearly distinguishes catalog retrieval from individual retrieval.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Excellent temporal guidance with 'Use this tool BEFORE generating documentation or reading project content,' establishing clear workflow sequencing. Lacks explicit mention of sibling tool as alternative for specific skill retrieval, but provides strong positive guidance.

    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?

    No annotations provided, so description carries full burden. Adds valuable context about external source (GitHub) and content structure. However, lacks disclosure of error behavior (missing skill), caching, return format, or safety profile (read-only vs destructive).

    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?

    Two well-structured sentences with zero waste: first defines operation and content, second specifies usage trigger. Information is front-loaded and appropriately sized for a single-parameter tool.

    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?

    For a simple fetch tool with 100% schema coverage and no output schema, the description adequately covers purpose, source, and usage context. Minor gap: could explicitly mention relationship to list_skills for skill discovery.

    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 has 100% description coverage ('The name of the skill to fetch'), establishing baseline 3. Description does not add syntax details, examples, or constraints (e.g., case sensitivity, naming conventions) beyond what the schema provides.

    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?

    States specific verb ('Fetches'), resource ('DevOps skill'), source ('GitHub'), and content types ('documentation templates, code standards'). The action clearly contrasts with sibling 'list_skills' (retrieve content vs enumerate available items).

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Provides explicit positive guidance ('Use when the user requests to generate documentation, apply standards, or use a template'). Lacks explicit negative constraints or mention of when to use sibling 'list_skills' first to discover available skill names.

    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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  • 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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