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Glama
Brandcode-Studio

brandsystem-mcp

by Brandcode-Studio
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.14.1

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion with other tools.

    Naming Consistency5/5

    The single tool uses a clear verb_noun pattern ('brand_clarify'), and consistency is unproblematic with only one tool.

    Tool Count3/5

    A single tool for a narrow clarification task is borderline; the server would benefit from including the complementary 'brand_compile' tool to provide a complete workflow.

    Completeness2/5

    The tool assumes a prior 'brand_compile' step that is absent from the server, leaving a significant gap in the tool surface. Users cannot independently use the server without an external compilation tool.

  • Average 4.4/5 across 1 of 1 tools scored.

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

    • 1 of 1 community issues answered or closed in the last 6 months
    • 72 commits in the last 12 weeks
    • Last stable release on
    • 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.

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

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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

  • Behavior4/5

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

    The description discloses that the tool modifies state (returns updated identity) and supports natural language input, adding value beyond the annotations which only indicate non-read-only and non-destructive. It does not contradict annotations.

    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 concise, front-loaded with the primary action, and every sentence adds value. No redundant 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 presence of an output schema, the description adequately covers the tool's function and return (updated identity, count). It lacks explicit mention of error handling or prerequisites beyond 'after brand_compile', but overall 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?

    Schema coverage is 100%, so baseline is 3. The description enriches parameter meaning by explaining the ID's source (needs-clarification.yaml) and detailing valid answer formats (hex color, role names, natural language), which goes beyond the schema descriptions.

    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 resolves ambiguous brand values interactively, specifies it is used after brand_compile, and lists the types of items needing confirmation. It distinguishes itself from any potential siblings by detailing its specific role.

    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 provides clear context on when to use this tool (after brand_compile when there are ambiguous values) but does not explicitly state when not to use it or mention alternatives. The guidance is sufficient but lacks exclusions.

    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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