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henryhawke

Wolfram Alpha MCP Server

by henryhawke
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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: wolfram_query handles general queries, while wolfram_query_with_assumptions is specifically for disambiguating queries when multiple interpretations arise. There is no overlap or confusion between them.

    Naming Consistency5/5

    Both tools follow a consistent snake_case naming pattern with 'wolfram_query' as the base, and the second tool adds a descriptive suffix '_with_assumptions'. This makes the naming predictable and easy to understand.

    Tool Count3/5

    With only 2 tools, the server feels thin for a domain as broad as Wolfram Alpha's capabilities (covering computational, mathematical, scientific, and factual information). While the tools cover basic querying and disambiguation, more specialized operations might be expected.

    Completeness4/5

    The tools provide core query functionality and a mechanism for handling ambiguous queries, which covers essential use cases. However, there are minor gaps, such as lack of tools for structured data retrieval, image generation, or step-by-step solutions, which are common in Wolfram Alpha's offerings.

  • Average 3.7/5 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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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. It mentions that the tool queries Wolfram Alpha and supports natural language queries, but does not disclose behavioral traits such as rate limits, authentication needs, error handling, or response formats. This is a significant gap for a tool with 16 parameters and no output schema.

    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 appropriately sized and front-loaded, starting with the core purpose and then listing supported domains. It uses two sentences efficiently without waste, though it could be slightly more structured by separating usage tips from the purpose statement.

    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 complexity (16 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral aspects, response handling, and error cases, which are crucial for a tool with many optional parameters and no structured output documentation. This leaves 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.

    Parameters3/5

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

    Schema description coverage is 100%, so the schema already documents all 16 parameters thoroughly. The description adds no additional parameter semantics beyond what the schema provides, such as examples or usage tips for the parameters. Baseline 3 is appropriate when the schema does the heavy lifting.

    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 purpose with specific verbs ('Query Wolfram Alpha') and resources ('computational, mathematical, scientific, and factual information'), and distinguishes it from its sibling by specifying the types of queries supported. It explicitly lists domains like chemistry, physics, geography, etc., making the scope unambiguous.

    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 for computational and factual queries across various domains, but does not explicitly state when to use this tool versus its sibling 'wolfram_query_with_assumptions' or other alternatives. It provides context on supported query types but lacks explicit guidance on exclusions or comparisons.

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

  • Behavior3/5

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

    No annotations are provided, so the description carries the full burden. It mentions the tool's behavior in handling ambiguous queries and using assumptions, but lacks details on rate limits, authentication needs, error handling, or response format. For a query tool with no annotation coverage, this leaves gaps in behavioral understanding, though it covers the core operational 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 highly concise and well-structured in two sentences, with the first stating the purpose and the second providing usage guidelines. Every sentence earns its place by adding critical information without redundancy, making it front-loaded and efficient.

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

    Completeness3/5

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

    Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers purpose and usage well, but lacks details on behavioral aspects like response handling or error cases. Without annotations or output schema, more context on what to expect from the tool would enhance completeness, though it meets minimum viability.

    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%, so the schema already documents all parameters thoroughly. The description does not add any parameter-specific details beyond what the schema provides (e.g., it doesn't explain the format of 'assumption' or examples). Baseline 3 is appropriate as the schema handles the heavy lifting, but no extra semantic value is added.

    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 purpose with specific verbs ('Query Wolfram Alpha with specific assumptions') and resource ('Wolfram Alpha'), and explicitly distinguishes it from its sibling tool by specifying it's for when 'the initial query returns multiple interpretations.' This provides clear differentiation and a specific use case.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides explicit usage guidelines: 'Use this when you need to clarify ambiguous queries' and specifies it's for when 'the initial query returns multiple interpretations.' This clearly indicates when to use this tool versus alternatives (like the sibling 'wolfram_query'), offering direct 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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