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ppspps824

cc-plugin-mcp

by ppspps824
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Python
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Server Quality Checklist

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

  • Latest release: v0.1.5

  • Disambiguation5/5

    list_plugins and load_elements have clearly distinct purposes: one for browsing all plugins, the other for retrieving detailed elements of a specific plugin. No overlap or confusion possible.

    Naming Consistency5/5

    Both tool names follow the consistent verb_noun pattern in snake_case (list_plugins, load_elements), making them predictable and readable.

    Tool Count3/5

    With only 2 tools, the server feels thin for a plugin management system. However, the tools are well-scoped to listing and loading, so it is borderline acceptable.

    Completeness2/5

    The tool set covers only read operations (list and load). Missing create, update, delete, or search functionality, which are significant gaps for managing plugins.

  • Average 3.1/5 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",
  "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 provided, so the description must carry full burden. It mentions possible HTTP exceptions (plugin not found, elements cannot be read) but does not disclose side effects, permissions, or whether the operation is destructive. The response example is vague and doesn't clarify the tool's behavior.

    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?

    Description is structured into sections (Args, Returns, Raises, Path Parameters, Responses) which aids readability. However, it is somewhat verbose and includes redundant information (e.g., 'Path Parameters' repeats plugin_name already in input schema). The use of Japanese in descriptions may reduce clarity for English-speaking agents.

    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 complexity (2 parameters, no annotations), the description is incomplete. It does not clarify whether the tool is read-only or mutating, nor does it explain the relationship between path parameters and request body. The sibling tool list_plugins provides minimal context. The response example is vague.

    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 coverage is 50% (plugin_name lacks description in schema). The description adds minimal: it mentions 'request: Request body with elements to load' but does not elaborate on plugin_name's role. The elements parameter is described in the schema (in Japanese) and the description adds little beyond that.

    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 it loads plugin elements (skills, agents, commands) with their content, which is specific. It differentiates from the sibling tool list_plugins which likely only lists plugins. However, 'load' is somewhat ambiguous (could mean retrieve content or load into memory).

    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 explicit guidance on when to use this tool versus alternatives. The sibling tool list_plugins is mentioned but no comparison. The description does not state prerequisites or when not to use this tool.

    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 bears full responsibility for behavioral disclosure. It mentions raising HTTPException on failure, but lacks details on authentication, rate limits, idempotency, or potential side effects, leaving significant gaps for a safe operation.

    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 relatively concise with structured sections for returns and raises. However, the example response includes an odd value 'unknown_type' which may cause confusion, slightly reducing clarity.

    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?

    Given the tool has no parameters and an output schema exists, the description adequately covers return values. It could mention potential pagination or filtering, but for a simple list tool it is sufficiently complete.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    With zero parameters and 100% schema coverage, the baseline is 4. The description does not need to add parameter meaning beyond the schema, and it correctly notes no parameters are required.

    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 tool gets a list of all available plugins from marketplaces, with a specific verb and resource. It mentions the return structure (name, description, categorized elements), distinguishing it from the sibling tool 'load_elements' which likely deals with loading elements rather than listing plugins.

    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, no prerequisites or exclusions provided. The description does not help an agent decide between this and the sibling tool.

    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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