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rocnubie

makebracket-mcp

by rocnubie

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 are clearly distinct: one retrieves editor scope and limits, the other returns official links. There is no overlap in functionality or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent 'get_ + noun_phrase' pattern (get_editor_scope, get_official_links), making naming predictable and clear.

    Tool Count3/5

    With only 2 tools, the set is borderline for an MCP server. While they cover specific informational needs, the count feels thin and limits the server's usefulness.

    Completeness3/5

    The tools cover only retrieval of two pieces of information about Make Bracket Online. Notable gaps include any operational tools or broader service capabilities, making the surface incomplete.

  • Average 3.7/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • No commit activity data available
    • 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.

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

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How to sync the server with GitHub?

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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

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden. It only states what is returned without disclosing any behavioral traits such as read-only nature, authentication needs, or rate limits. The description lacks sufficient behavioral context for a tool with zero annotations.

    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 one sentence with a parenthetical note, making it very concise. It is front-loaded with the main information. However, the parenthetical '(Make Bracket Online)' is slightly unclear and may be extraneous.

    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 zero parameters and no output schema, the description is adequate in stating what is returned. However, it lacks details on the format or structure of the output, which would improve completeness. It is minimally sufficient but not thorough.

    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?

    There are zero parameters, so schema coverage is 100%. The description does not need to add parameter information, and by baseline for zero parameters, a score of 4 is appropriate. It adds no extra parameter semantics, which is acceptable.

    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 the specific verb 'Return' and identifies the resource as 'editor workflow' and 'documented limits on local processing or export'. This clearly distinguishes from the sibling tool 'get_official_links', which likely returns links.

    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 implies the tool is used to retrieve editor workflow and limits but does not explicitly state when to use it versus alternatives, nor does it provide any exclusions or prerequisites.

    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?

    No annotations are provided, so the description must cover behavioral traits. It states that the tool returns a canonical list but does not disclose whether the operation is read-only, if there are side effects, or how the list is generated. For a simple read operation with no parameters, this is adequate but lacks depth.

    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 a single, concise sentence containing 15 words. It is front-loaded with the key action and resource, with no superfluous information.

    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's low complexity (no parameters, no output schema), the description is fairly complete. It explains what is returned and what 'official links' includes. However, it could be slightly more explicit about the read-only nature or the dynamicity of the list.

    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?

    The input schema has zero parameters, so the description does not need to add parameter information. Baseline for 0 parameters is 4, and the description does not detract from this.

    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 'Return' and resource 'canonical list of official links for Make Bracket Online', which is distinct from the sibling tool 'get_editor_scope'. It specifies the types of links (website, support, docs) making the purpose very clear.

    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?

    The description does not provide any guidance on when to use this tool versus the alternative sibling tool 'get_editor_scope'. There is no mention of context, prerequisites, or exclusions, leaving the agent to infer usage.

    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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