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Abanoub-Rodolf

mcp-server-starter-demo

by Abanoub-Rodolf
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Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    Echo returns input unchanged, while text_stats computes word and character counts. Their purposes are entirely distinct with no overlap or possibility of confusion.

    Naming Consistency3/5

    Echo is a bare verb, while text_stats is a compound noun with an underscore. There's no consistent verb_noun pattern, but both are short and readable.

    Tool Count4/5

    Two tools is below the typical 3-15 range, but for a starter demo server this minimal scope is intentionally appropriate and each tool earns its place.

    Completeness5/5

    The domain appears to be simple in-memory text operations. Echo and text_stats cover returning text and analyzing it, with no filesystem or network operations claimed, so no obvious gaps exist.

  • 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
    • No commit activity data available
    • 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.

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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

  • Behavior3/5

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

    With no annotations provided, the description carries the transparency burden. It discloses the key behavioral boundary ('No filesystem, network, or subprocess access') but does not explain counting conventions, edge cases, or the exact return format.

    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 short sentences, front-loading the core purpose and then stating constraints. Every word earns its place with no redundancy or filler.

    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?

    For a simple single-parameter tool with no output schema, the description adequately communicates purpose, scope, and safety boundaries. The only minor gap is the lack of explicit mention of the return shape, but it is reasonably inferred from the stated behavior.

    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 100% because the only parameter 'text' already has a clear description ('Text to measure'). The tool description adds useful context that the text is counted, but it does not provide additional parameter-level syntax or usage details beyond the schema.

    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 ('Count') and names the exact resources ('words and characters'), while clarifying the in-memory scope. This clearly distinguishes it from the sibling tool 'echo', which is about outputting text, not computing statistics.

    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 usage context: this is a safe, memory-only text measurement tool, and it explicitly rules out filesystem, network, or subprocess access. However, it does not explicitly state when to prefer this over alternatives like 'echo' or provide concrete use-case guidance.

    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?

    With no annotations provided, the description carries the burden of disclosing behavior. It explicitly notes 'No filesystem, network, or subprocess access,' which is a useful transparency statement about side effects. However, it does not describe validation details or error behavior, which is a 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?

    The description is a single sentence that is perfectly front-loaded. Every word adds value, and the safety clarification is concise. No wasted space or 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 simple tool with one fully documented parameter and an obvious return value, the description is complete. The safety constraint is included, making the context clear. No output schema is needed because the output is simply the input text.

    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 coverage is 100% and the parameter description 'Text to return unchanged' is clear. The description adds the word 'validated' but does not elaborate on what validation entails. Overall, the schema does most of the heavy lifting, so a baseline of 3 is appropriate.

    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's function: 'Return a validated text value unchanged.' It uses a specific verb ('return') and resource ('text value'), and distinguishes itself from the sibling tool by specifying it has no side effects or external access.

    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?

    Usage is implied by the description and name, but there is no explicit guidance on when to use this tool versus the sibling 'text_stats.' The description mentions safety constraints but does not provide context for when it is appropriate to choose echo over 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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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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