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Dmitry-Kaleykin

knowledge-gateway-mcp

by Dmitry-Kaleykin
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TypeScript
Local

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    search_skills handles discovery by returning candidate matches, while load_skill retrieves the full content for a specific skill name. Their purposes are clearly separated and complementary.

    Naming Consistency5/5

    Both tools use the same verb_noun snake_case convention, with search_skills and load_skill being predictable and consistent. The naming clearly indicates the action and target resource.

    Tool Count4/5

    Two tools is slightly below the typical range, but the server is intentionally scoped to a simple search-and-retrieve workflow. Each tool earns its place and there is no redundant surface.

    Completeness5/5

    For a read-only skill library gateway, the discover-then-retrieve flow is complete: search_skills finds relevant candidates and load_skill retrieves the full skill. No create, update, or delete operations are implied by the stated purpose.

  • Average 4.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 8 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.

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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.

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  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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  ]
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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

  • Behavior3/5

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

    Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description does not need to restate safety. It adds a meaningful precondition (exact name) and mentions loading the complete skill, but does not disclose return format or behavior when the name is not found.

    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?

    One front-loaded sentence with zero filler, placing the core action and the key precondition before anything else. It earns its place without redundancy.

    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 load operation with a rich annotation set and fully described schema, the description plus schema cover the essential call context. The only minor gap is the absence of an explicit statement of what 'complete skill' returns, but no output schema was expected to be summarized.

    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% and clearly documents both parameters, including the optional files parameter with an example. The description adds no parameter-level meaning beyond what is already in the schema, so it stays at the baseline.

    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 uses a specific verb ('Load') and resource ('the complete skill'), and scopes it to an exact name returned by search_skills. This clearly distinguishes it from the sibling search_skills, which is for finding skills rather than loading them.

    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?

    The phrase 'exact skill name returned by search_skills' establishes the prerequisite workflow: call search_skills first, then load. It does not explicitly state when not to use the tool or name alternatives, but with only one sibling the routing is clear.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

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

    Annotations already convey readOnly, idempotent, and non-destructive behavior, so the description does not need to restate those. It adds valuable behavioral context by explaining that the tool returns relevant candidates and explicitly cannot list the complete skill library, which is information not present in annotations or schema.

    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?

    Three short sentences, each earning its place: what the tool does, a key limitation, and the routing to the sibling tool. No filler or redundancy.

    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 simple search tool with fully documented parameters, strong annotations, and one clearly identified sibling, the description covers what the tool does, its limitation, and the next step. Nothing essential is missing.

    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 both parameters are already documented meaningfully in the schema. The description does not add parameter-level detail, but it does not need to; the schema carries the semantic load. Baseline 3 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 verb and resource: 'Search within local library of skills.' It clearly distinguishes itself from the sibling load_skill by noting it returns candidates and cannot list the complete catalog, so an agent can tell search apart from load without opening schemas.

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

    Usage Guidelines5/5

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

    It explicitly directs the agent to 'Call load_skill for a relevant match,' establishing when to use this tool versus the sibling. The limitation statement also clarifies that search is not for enumerating the full catalog, which prevents misuse.

    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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