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delorenj

33GOD Pipeline MCP Hub

by delorenj
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: list_domains for discovery and call_domain_tool for invocation. No overlap at all.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun snake_case pattern (list_domains, call_domain_tool), making them predictable and clear.

    Tool Count4/5

    With only 2 tools, the set feels thin, but for a hub server that delegates to domains, this minimal surface is acceptable and focused.

    Completeness2/5

    The hub is missing the list_domain_tools tool, which is advertised in list_domains' description. Without it, users cannot discover and invoke tools within domains, causing a critical gap.

  • Average 4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 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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  • If you are the author, simply .

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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 are provided, so the description carries the full burden. It says 'invoke' which implies potential side effects, but it does not disclose any behavioral traits such as permissions, rate limits, error handling, or what happens if the domain/tool is invalid. The description is too generic to inform the agent about behavioral aspects.

    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 two sentences plus a concise example. Every sentence is informative, with no fluff or repetition. The structure is front-loaded with the core purpose and then provides an illustrative example. It is appropriately sized for the tool's simplicity.

    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?

    Given the tool has 3 parameters, no output schema, and a sibling with a different purpose, the description is moderately complete. It explains the invocation pattern and the argument requirement. However, it does not describe the return value format, possible errors, or how to obtain the input schema for a specific tool. The example helps but does not cover all contextual gaps.

    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 0%, so the description must add meaning. It explains that 'arguments' must conform to the tool's input schema and gives an example. However, it does not describe what 'domain' and 'tool' represent (e.g., they must match values from list_domains/list_domain_tools). The example partially compensates, but the meaning of all parameters is not fully clarified.

    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 it invokes a specific tool within a domain and returns its result. The verb 'invoke' and the resource 'tool within a domain' are specific, and it distinguishes from the sibling 'list_domains', which only lists domains.

    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?

    The description provides an example and notes that arguments must conform to the tool's input schema from list_domain_tools. However, it does not explicitly state when to use this tool vs alternatives, nor does it mention prerequisites (e.g., needing to call list_domain_tools first). Usage context is implied but not fully explicit.

    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?

    No annotations are provided, but the description explains that tools are intentionally not loaded into context until requested, and mentions the return fields. It does not explicitly state read-only nature, but the behavior is implied. Minor gap.

    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, front-loaded with 'START HERE'. Every sentence adds essential information without 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 zero-parameter list tool with an output schema (implied), the description fully covers purpose, usage, and return value composition. No gaps.

    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 has no parameters, so schema coverage is 100%. The description does not add parameter-specific meaning beyond stating the function.

    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 'List the tool domains' with a specific verb and resource. It distinguishes itself from the sibling tool call_domain_tool by explaining this is the starting point for discovering domains before using them.

    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?

    The description explicitly says 'START HERE' and provides a step-by-step guide: list domains, then use list_domain_tools then call_domain_tool. This gives clear when-to-use and alternatives.

    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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