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gogouravr

FastMCP Demo

by gogouravr
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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: 'calculate' handles arithmetic operations, while 'hello' provides a greeting. There is no overlap or ambiguity between these functions, making it easy for an agent to select the correct tool.

    Naming Consistency5/5

    Both tool names follow a consistent pattern of using simple, descriptive verbs ('calculate', 'hello') without any mixing of conventions like camelCase or snake_case. The naming is straightforward and predictable.

    Tool Count2/5

    With only two tools, the server feels thin and under-scoped for a general-purpose demo. While the tools are distinct, the count is too low to meaningfully cover a domain or provide a coherent set of operations, limiting utility.

    Completeness1/5

    The server lacks a clear domain, making it impossible to assess coverage meaningfully. The tools are trivial and unrelated (arithmetic and greeting), offering no cohesive workflow or lifecycle, which severely limits agent functionality.

  • Average 2.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 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

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  • This repository includes a README.md file.

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

    With no annotations provided, the description carries the full burden of behavioral disclosure but only states the basic function. It doesn't mention error handling (e.g., division by zero), performance characteristics, or any side effects, leaving significant gaps in understanding how the tool behaves beyond its core operation.

    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 extremely concise with a single, clear sentence that directly states the tool's purpose without any unnecessary words or structural fluff. It's front-loaded and wastes no space.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the lack of annotations and output schema, the description is incomplete for a tool with 3 parameters and mathematical operations. It doesn't explain return values, error conditions, or behavioral nuances, making it inadequate for full contextual understanding despite the simple nature of the tool.

    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 description coverage is 100%, with clear descriptions for all three parameters, so the description doesn't need to add parameter details. The description mentions 'basic arithmetic calculations,' which aligns with the parameters but doesn't provide additional semantic context beyond what the schema already covers.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose with a specific verb ('perform') and resource ('basic arithmetic calculations'), making it immediately understandable. However, it doesn't differentiate from the only sibling tool 'hello', which appears unrelated, so it doesn't fully address sibling distinction.

    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 provides no guidance on when to use this tool versus alternatives or any contextual prerequisites. It simply states what the tool does without indicating appropriate use cases or limitations.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

    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 states the tool 'says hello', which implies a read-only output operation, but doesn't disclose any behavioral traits like whether it has side effects, requires authentication, has rate limits, or what format the greeting takes. The description is too minimal to provide meaningful behavioral context.

    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 extremely concise at just 7 words in a single sentence. It's front-loaded with the core purpose and contains zero wasted words. Every element of the description earns its place by communicating the essential function of the tool.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given that there are no annotations and no output schema, the description is incomplete for helping an agent understand how to properly use this tool. While the tool is simple (one parameter), the description doesn't explain what the tool returns, how the greeting is formatted, or any behavioral characteristics. For a tool with zero annotation coverage, this minimal description leaves significant 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 100% description coverage, with the 'name' parameter clearly documented as 'The name to greet'. The description doesn't add any parameter semantics beyond what the schema already provides. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose as 'A simple greeting tool that says hello', which specifies the verb ('says hello') and resource (greeting). It distinguishes from the sibling 'calculate' tool by focusing on greetings rather than calculations. However, it doesn't explicitly differentiate from potential other greeting tools beyond the sibling context.

    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 provides no guidance on when to use this tool versus alternatives. It doesn't mention any specific contexts, prerequisites, or exclusions. The only implied usage is for greeting purposes, but this is too vague to help an agent make informed decisions about tool selection.

    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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  • Evaluate tool definition quality.

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