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srmcguirt

FastMCP Python Boilerplate

by srmcguirt

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.1.0

  • Disambiguation5/5

    echo and calculate have entirely distinct purposes—one returns text unchanged and the other evaluates arithmetic expressions. There is no overlap or possibility of confusion between them.

    Naming Consistency5/5

    Both tools use simple, lowercase imperative verb names (echo, calculate), creating a consistent naming style. While there is no verb_noun pattern, the convention is uniform across the set.

    Tool Count3/5

    With only two tools, the server feels thin and provides minimal functionality. This is borderline but acceptable for a boilerplate template meant to demonstrate basic MCP tool patterns.

    Completeness4/5

    For the server's apparent purpose as a Python MCP boilerplate, the tool surface covers round-trip testing and simple computation with no obvious gaps. A few more demonstration tools could improve coverage, but nothing essential is missing.

  • Average 4.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 4 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 failing
  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

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

    Then . Browse examples.

  • Add related servers to improve discoverability.

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

  • Behavior4/5

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

    With no annotations, the description carries the full burden of behavioral disclosure. It clearly states the tool returns the input unchanged, implying a pure, side-effect-free operation. It does not discuss error behavior or formatting, but for an echo tool the core behavior is fully disclosed.

    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?

    A single, front-loaded sentence says exactly what the tool does and why it is useful. There is no redundancy or extraneous information.

    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 one-parameter echo tool with an output schema present, the description covers the core behavior and intended use case adequately. No additional context about return values is needed because the behavior is fully implied by 'unchanged' and the schema.

    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 schema has no property descriptions, so the description must compensate. The phrase 'Return *text* unchanged' implicitly references the 'text' parameter and conveys that it is echoed back, but it does not add explicit details like examples, constraints, or formatting. The single, self-explanatory parameter reduces the need for more.

    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 ('text'), clearly stating the behavior is to return the input unchanged. It also names the use case (round-trip testing), making it easy to distinguish from the sibling tool 'calculate'.

    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 phrase 'useful for round-trip testing' gives an implied use case, but it does not explicitly state when to choose this tool over 'calculate' or when not to use it. There is minimal routing guidance, just enough to hint at its purpose.

    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 full burden of behavioral disclosure. It explicitly states the operation is safe, enumerates supported operators, and prohibits function calls, variables, and imports, effectively communicating that no arbitrary code execution occurs. It does not cover error behavior or division-by-zero handling, but the core safety behavior is well disclosed.

    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 compact and front-loaded with the purpose, follows with supported operations and restrictions, and uses examples to clarify expected behavior. Every sentence contributes useful 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 single-parameter calculator with an output schema, the description is sufficiently complete: it defines valid inputs, supported operations, restrictions, and example outputs. No critical information is missing for an agent to select and invoke the tool correctly.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema provides only a string type with no description, so the description must compensate. It does so thoroughly by defining what the 'expression' parameter must be, supported operators, syntax rules, and concrete examples. This is exactly the meaning the schema lacks.

    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 a specific verb ('evaluate') and resource ('simple arithmetic expression') and indicates the result is returned. It differentiates from sibling tool 'echo' by emphasizing evaluation rather than echoing, and lists supported operations to remove ambiguity.

    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 gives clear boundaries for what expressions are valid (arithmetic only, no function calls, variables, or imports), so the intended use is implied. However, it does not explicitly mention when to prefer this tool over the sibling 'echo' or what types of tasks should not use it.

    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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