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Server Quality Checklist

92%
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  • Latest release: v1.1.0

  • Disambiguation5/5

    The server exposes exactly one tool, so there is no possibility of confusing it with another. All tool selection is unambiguous by definition.

    Naming Consistency4/5

    The lone tool name 'mission_agent' is clear and internally consistent, but the pattern cannot be validated across a set. No naming conflicts or inconsistencies exist.

    Tool Count3/5

    A single tool is borderline for a server that describes a broad range of capabilities. The one mission_agent can be a legitimate monolithic entry point, but the count feels thin given the scope.

    Completeness3/5

    The mission_agent handles open-ended missions and follow-ups via thread_id, but there are no explicit controls for listing, canceling, or monitoring missions. The description hints at individual granular tools that are absent from this server surface.

  • Average 4.7/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 2 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
  • 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.

  • This repository includes a glama.json configuration file.

  • This server has been verified by its author.

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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

  • Behavior5/5

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

    The description goes far beyond the annotations by explaining autonomous planning/execution, the [thread:<id>] result prefix, memory across follow-ups, and the possibility of a clarifying question. It also mentions capabilities like running data work in the code sandbox and delivering into connected apps, which gives an agent a concrete sense of side effects. No contradiction with annotations was mentioned.

    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?

    The description is dense but every sentence adds new information: core functionality, key output format, continuation pattern, clarification behavior, and usage preference. The front-loaded sentence explains scope first, followed by operational details. It is aligned with the best vertex for high-functioning agents.

    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?

    Given the tool is an autonomous agent with no sibling tools and no output schema, the description surprisingly covers everything essential: what the objective should be, what outcomes can be expected, how to start, how to continue, how to handle clarifying questions, and when to prefer it. The emptiness of a formal output schema is compensated by the explicit mention of the thread line at the beginning. This is a high-quality, self-sufficient description.

    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 input schema already covers 100% of parameters with meaningful descriptions and an example for objective. The description reinforces thread_id usage in the continuation context but does not materially add new semantic details beyond the schema. This matches the baseline where the schema carries the parameter-load.

    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 that the tool takes a plain-language objective and autonomously plans/executes a multi-step mission across growth, revenue, and digital operations. It lists concrete actions (research, audits, competitor analysis, email verification) and explicitly contrasts itself with individual tools. This goes well beyond a vague restatement of the name.

    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 says 'Prefer this over the individual tools for anything multi-step', which specifies when to use it and strongly implies when not to use it (single-step operations). It also documents the follow-up workflow using thread_id and how to respond to clarifying questions. This is strong, actionable usage direction.

    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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astrofabric-mcp MCP server

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