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Garoth

WolframAlpha LLM MCP Server

by Garoth
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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation2/5

    The tools 'ask_llm' and 'get_simple_answer' have overlapping purposes—both process WolframAlpha queries to return answers, with only subtle differences in output format. An agent could easily confuse them, as the descriptions don't clearly delineate distinct use cases, leading to potential misselection.

    Naming Consistency4/5

    The naming follows a consistent snake_case pattern across all tools, with clear verb_noun structures (e.g., ask_llm, get_simple_answer). However, 'validate_key' deviates slightly by not directly involving query processing, though it maintains the same stylistic convention.

    Tool Count3/5

    With only 3 tools, the count feels thin for a server named 'WolframAlpha LLM MCP Server', which suggests broader functionality. While the tools cover core querying and key validation, the limited number may restrict agent capabilities in handling diverse WolframAlpha tasks.

    Completeness2/5

    The tool surface is significantly incomplete for interacting with WolframAlpha's capabilities. It lacks operations for different query types (e.g., computational, visual, step-by-step), error handling, or advanced features, leaving obvious gaps that could cause agent failures in complex scenarios.

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

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

    • No community issues in the last 6 months
    • 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 of behavioral disclosure. It mentions 'LLM-optimized structured response with multiple formats,' which hints at output behavior, but fails to cover critical aspects like rate limits, authentication needs, error handling, or whether it's read-only or destructive. For a tool with no annotations, this 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.

    Conciseness5/5

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

    The description is a single, efficient sentence that front-loads key information: the action, target, and outcome. There is no wasted text, and it directly communicates the tool's core functionality without redundancy.

    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 of querying an external service (WolframAlpha) and the lack of annotations and output schema, the description is incomplete. It doesn't explain the response formats, error cases, or operational constraints, leaving significant gaps for an AI agent to understand tool behavior fully.

    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 'query' parameter clearly documented. The description adds no additional parameter semantics beyond what the schema provides, such as query format examples or constraints. With high schema coverage, the baseline is 3, as the description doesn't compensate but doesn't need to.

    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: 'Ask WolframAlpha a query and get LLM-optimized structured response with multiple formats.' It specifies the action (ask), target (WolframAlpha), and outcome (structured response with multiple formats). However, it doesn't explicitly distinguish this from sibling tools like 'get_simple_answer' or 'validate_key', which would require a 5.

    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 sibling tools like 'get_simple_answer' or 'validate_key', nor does it specify contexts, prerequisites, or exclusions for usage. This lack of comparative guidance limits its effectiveness for an AI agent.

    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 mentions the output is 'simplified' and 'LLM-friendly,' but doesn't cover critical aspects like rate limits, authentication needs, error handling, or what 'simplified' entails (e.g., formatting, length). This leaves significant gaps for an AI agent to understand the tool's behavior.

    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 clearly states the tool's purpose without unnecessary words. It is front-loaded with the core function ('Get a simplified, LLM-friendly answer'), making it easy to parse. Every part of the sentence contributes to understanding the tool.

    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 low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavior, usage guidelines, and output specifics. Without annotations or an output schema, more context on what the answer includes would improve completeness, but it meets a bare minimum.

    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 'query' parameter documented as 'The query to ask WolframAlpha.' The description adds no additional meaning beyond this, such as query format examples or constraints. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to heavily.

    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 function: 'Get a simplified, LLM-friendly answer focusing on the most relevant information.' It specifies the action ('Get'), the output type ('simplified, LLM-friendly answer'), and the focus ('most relevant information'). However, it doesn't explicitly differentiate from sibling tools like 'ask_llm' or 'validate_key', which prevents a perfect score.

    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 sibling tools like 'ask_llm' or 'validate_key', nor does it specify contexts or exclusions for usage. The phrase 'LLM-friendly answer' implies a target audience but lacks explicit usage rules.

    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 action ('validate') but doesn't explain what validation entails, such as checking key format, connectivity, permissions, or returning success/failure status. This leaves significant gaps in understanding the tool's behavior.

    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 without any unnecessary words. It is appropriately sized and front-loaded, making it easy to understand at a glance.

    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. It doesn't explain what validation means, what the output might be (e.g., success/failure, error messages), or how it integrates with sibling tools. For a validation tool with no structured context, more detail is needed.

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

    Parameters4/5

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

    The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate, earning a baseline score of 4 for tools with no parameters.

    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 ('validate') and the target resource ('WolframAlpha LLM API key'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'ask_llm' or 'get_simple_answer', which prevents a score of 5.

    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?

    No guidance is provided on when to use this tool versus the sibling tools. The description lacks context about prerequisites, such as whether validation is needed before using other tools, or any explicit when/when-not scenarios.

    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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