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krollchristensen

Sample Server

by krollchristensen
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Python
Local

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a completely different task (mathematical calculation, currency conversion, and weather retrieval), so there is no ambiguity in purpose.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (calculate, convert_currency, get_weather), making them predictable and easy to understand.

    Tool Count4/5

    Three tools is a reasonable number for a utility server, though the set feels slightly sparse for a general-purpose toolkit.

    Completeness2/5

    The tools form a disjoint set with no clear domain; they cover only a few random utilities, leaving obvious gaps for a general-purpose server.

  • Average 2.8/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 2 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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

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  • 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": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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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

  • Behavior2/5

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

    No annotations are provided, so the description must convey behavioral traits. It only states 'get the current weather' with no disclosure about data freshness, API limits, caching, or required authentication. A 2 indicates minimal transparency.

    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, efficient sentence that directly states the tool's purpose. No extraneous content, perfectly front-loaded.

    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 tool's simplicity (1 parameter) and presence of an output schema, the description should at least hint at what the output contains. It does not, nor does it address any other contextual signals like rate limits or location validation. A 2 reflects significant incompleteness.

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

    Parameters1/5

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

    The sole parameter 'location' has a 0% schema description coverage, and the tool description only says 'specified location' without any added detail about format, accepted values, or examples. The description fails to compensate for the missing schema documentation.

    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 action ('get') and the resource ('current weather') with a location parameter. However, it does not provide any differentiation from sibling tools (calculate, convert_currency), though these are unrelated, so no strong need for discrimination. A 4 reflects clear but basic purpose.

    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 offers no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. With sibling tools in different domains, the lack of usage context is acceptable but still missing explicit advice.

    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, the description carries full burden. It only says 'calculate the result,' missing critical details like error handling, allowed complexity, security implications, or return format. The presence of an output schema (not shown) mitigates this slightly, but the description alone is insufficient.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single sentence, making it concise, but it may be too minimal. Front-loading is fine, but the lack of structure (e.g., no separation of purpose, usage, behavior) hurts readability for the agent.

    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 tool has one parameter, no annotations, and an output schema (unseen), the description should provide more context about return values, error cases, or supported math. It is incomplete for a tool that likely evaluates arbitrary expressions, which has safety and formatting implications.

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

    Parameters2/5

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

    Schema description coverage is 0%, so the description must compensate. It fails to explain the format of the 'expression' string (e.g., operators, functions, precedence). The agent has no guidance on how to construct valid expressions beyond the type 'string'.

    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 calculates a mathematical expression, which differentiates it from sibling tools like convert_currency or get_weather. However, it does not specify the scope of supported operations (e.g., basic arithmetic vs. advanced functions), leaving some 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?

    No explicit guidance is given on when to use this tool versus alternatives, but the sibling tools are domain-specific (currency, weather), so the intended use case is reasonably implied. No exclusions or prerequisites are mentioned.

    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 full burden for behavioral disclosure. It does not mention whether the conversion uses real-time rates, historical data, or any required authentication. No behavioral traits beyond the basic function are disclosed.

    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 a single concise sentence, front-loaded with the core purpose. It wastes no words, though it could include more detail without losing conciseness. The structure is efficient for the information it conveys.

    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 three required parameters, absence of parameter descriptions, and no annotations, the description is inadequate. It does not address parameter format, behavior, or error handling. The existence of an output schema partially compensates for return values, but the description remains incomplete.

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

    Parameters1/5

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

    The input schema has three parameters with 0% description coverage, and the description adds no meaning beyond the parameter names. It does not explain formats (e.g., currency codes), constraints, or examples, leaving the agent without guidance on valid values.

    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 'Convert amount from one currency to another,' specifying the verb 'convert' and the resource 'amount' with source and target currencies. It distinguishes from sibling tools 'calculate' (arithmetic) and 'get_weather' (weather data).

    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, nor any constraints or prerequisites. It only states the basic function, leaving the agent without context for 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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  • 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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