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SecretiveShell

MCP-wolfram-alpha

by SecretiveShell
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is clearly defined as querying Wolfram Alpha for complex math or symbolic intelligence, making it distinct by default.

    Naming Consistency5/5

    The single tool name 'query-wolfram-alpha' follows a consistent verb-noun pattern, using kebab-case. Since there is only one tool, naming consistency is inherently perfect with no deviations to assess.

    Tool Count2/5

    One tool is too few for a server named 'MCP-wolfram-alpha', which suggests broader Wolfram Alpha integration. A single query tool feels thin and limits functionality, as it lacks operations like data retrieval, visualization, or specialized computations that might be expected from such a service.

    Completeness2/5

    The tool surface is severely incomplete for a Wolfram Alpha integration. While the query tool handles basic questions, there are obvious gaps such as no support for step-by-step solutions, data analysis, image generation, or API-specific features like units conversion or historical data, which are core to Wolfram Alpha's capabilities.

  • Average 3.2/5 across 1 of 1 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 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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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. The description mentions the tool's purpose but doesn't disclose important behavioral traits such as rate limits, authentication requirements, response format, or error handling. It adds minimal value beyond stating what the tool does.

    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 appropriately sized and front-loaded with the core purpose in the first sentence and usage guidance in the second. Both sentences earn their place by providing essential information without waste.

    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 complexity (querying an external API for complex computations), lack of annotations, no output schema, and low parameter coverage, the description is incomplete. It doesn't address how results are returned, error conditions, or practical constraints, leaving significant gaps for an AI agent.

    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?

    The input schema has 1 parameter with 0% description coverage. The description doesn't add any meaning about the 'query' parameter beyond what's implied by the tool's purpose. It doesn't explain what constitutes a valid query, format expectations, or examples, failing to compensate for the low schema coverage.

    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: 'Use Wolfram Alpha to answer a question.' It specifies the action ('answer a question') and the resource (Wolfram Alpha). However, it doesn't distinguish from siblings since there are none, so it can't achieve a perfect 5.

    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: 'when you need complex math or symbolic intelligence.' This gives explicit guidance on appropriate use cases. However, it doesn't mention when NOT to use it or name alternatives, so it falls short of a 5.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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