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quan2005

MCP Compact

by quan2005
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Server Quality Checklist

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

  • Latest release: v0.6.0

  • Disambiguation4/5

    The two tools are clearly separated by purpose: invoke handles tool calls and read handles resource access. The descriptions provide explicit examples and modes to guide usage, though the generic proxy nature could occasionally blur the line between a tool and a resource.

    Naming Consistency5/5

    Both tool names are single-word verbs in a consistent imperative style, making them predictable and easy to remember. There is no mixing of conventions or misleading synonyms.

    Tool Count4/5

    With only two tools, the set is intentionally minimal but well-suited for a compact projection surface that forwards to a larger MCP ecosystem. While it feels thin compared to direct-integration servers, its purpose justifies the small count.

    Completeness3/5

    The server covers the core interaction patterns (invoking tools and reading resources) and includes validation/preview modes. However, it lacks an explicit discovery mechanism to list available servers, tools, or resource templates, which could be a significant gap for agents without prior knowledge.

  • Average 3.5/5 across 2 of 2 tools scored. Lowest: 2.9/5.

    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
    • Last stable release on
    • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

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

    Then . Browse examples.

  • Add related servers to improve discoverability.

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 exist, so the description carries full responsibility. It fails to disclose that invoking arbitrary MCP tools could have side effects (destructive or otherwise), and it doesn't explain the 'compact projection surface' or what happens on invocation. The validate mode is a positive safety hint, but overall behavioral risk is understated.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness2/5

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

    The description is short but structurally flawed by the incomplete 'Available tool families:' line. It uses jargon like 'compact projection surface' without elaboration. The flow is disjointed, moving from invocation to identifiers to an example, making it less coherent than it could be.

    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?

    While an output schema exists, the description does not clarify how the output relates to the invoked tool (e.g., passthrough vs. wrap), nor does it explain the 'compact projection surface' or enumerate available tool families. The missing list of tool families is a major completeness gap for a proxy tool.

    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?

    Since schema coverage is 0%, the description adds meaning for `ref` (server/name) and `mode` (validate for inspection). The `arguments` parameter is only exemplified, not fully explained. This is helpful but incomplete for a generic tool with three parameters.

    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 invokes an MCP tool ('Invoke an MCP tool through the compact projection surface') and provides a concrete example (invoke with notes/echo_note). However, it does not explicitly differentiate from the sibling tool 'read', and the dangling 'Available tool families:' line introduces ambiguity.

    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?

    Provides specific guidance on constructing the ref object ('Use `ref.server` and `ref.name`') and using validate mode ('Use `mode="validate"` to inspect input requirements'). It does not explain when to use this tool versus the 'read' sibling, and the incomplete list of tool families offers no exclusionary 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?

    With no annotations provided, the description carries the burden of behavior. It explains the modes and the reference structure, but it confusingly states "Direct resources: none" and "Resource templates: none" while the example uses a URI template, creating ambiguity. It also does not mention error behavior or response details, leaving behaviors unexplained.

    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 well-organized and front-loaded with the core purpose, followed by usage instructions and an example. The "Direct resources: none" and "Resource templates: none" lines add confusion and seem unnecessary, slightly detracting from conciseness.

    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?

    Despite the presence of an output schema, the description fails to resolve the contradiction between the "none" resource lists and the example. It provides sufficient guidance for a basic call, but the contradictory statements leave gaps in understanding the tool's actual capabilities, so it is not fully complete.

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

    Parameters5/5

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

    Since schema coverage is 0%, the description compensates well by explaining the layout of the `ref` object, including `server`, `uri`, `uriTemplate`, and `arguments`, and shows a concrete usage example. The `mode` parameter is also given context (default "read", "preview" for metadata).

    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 identifies the tool as reading MCP resources through a projection surface, with explicit instructions for using ref.uri and ref.uriTemplate. It differentiates from sibling invoke by focusing on reading rather than executing/invoking, giving it a distinct purpose.

    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 description gives concrete usage directions: how to construct ref.server with uri or uriTemplate, and when to use mode="preview" for metadata. However, it does not explicitly state when to avoid this tool or compare it to `invoke`, so it misses the full "when-not" guidance.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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