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JWTseng

grok-mcp

by JWTseng
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: 'grok' handles AI analysis and search operations, while 'grok_budget' manages API budget and spending. There is no overlap in functionality, making it impossible to confuse them.

    Naming Consistency5/5

    Both tools follow a consistent naming pattern with the 'grok' prefix and descriptive suffixes ('grok' and 'grok_budget'), using snake_case consistently. This creates a predictable and readable naming scheme.

    Tool Count2/5

    With only two tools, the server feels severely under-scoped for a Grok AI integration. Key operations like managing conversation history, configuring modes, or handling errors are missing, making the set too thin for practical agent use.

    Completeness2/5

    The toolset is highly incomplete for an AI assistant domain. It lacks core functionalities such as conversation management, response customization, error handling, or user preference settings, leaving significant gaps that will hinder agent workflows.

  • Average 3.3/5 across 2 of 2 tools scored.

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

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  • This repository includes a README.md file.

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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 actions like 'adjust' and 'modify limits,' which imply mutation capabilities, but it doesn't specify permissions required, whether changes are reversible, rate limits, or error handling. This leaves significant gaps for a tool that can modify budget settings.

    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 concise and front-loaded in a single sentence, efficiently covering the tool's purpose and key actions without unnecessary details. Every part earns its place, though it could be slightly more structured for clarity.

    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 budget management (with mutation actions) and no annotations or output schema, the description is incomplete. It lacks details on behavioral aspects like safety, permissions, and response format, which are crucial for proper tool invocation in this context.

    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 both parameters ('action' with enum values and 'value' as a number). The description adds marginal value by hinting at usage ('check spending, remaining balance, or modify limits'), but it doesn't provide additional syntax, format details, or constraints beyond what the schema specifies.

    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 specific verbs ('view or adjust') and resource ('Grok API budget'), and distinguishes its scope by mentioning spending, balance, and limits. However, it doesn't explicitly differentiate from the sibling 'grok' tool, which might handle other aspects of Grok API management.

    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 context by listing actions like checking spending or modifying limits, but it doesn't provide explicit guidance on when to use this tool versus alternatives (e.g., the sibling 'grok' tool) or any prerequisites. The implied context is adequate but lacks detailed 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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals important behavioral traits: default mode ('free browser mode'), automatic switching behavior ('auto-switches to API for structured output'), and the tool's scope (analyzing links, searching, answering questions). However, it doesn't cover important aspects like rate limits, authentication requirements, error conditions, or response format expectations.

    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 and well-structured in just two sentences. The first sentence states the core purpose, and the second sentence provides important behavioral context about modes. Every word earns its place with zero waste or redundancy.

    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?

    For a tool with 4 parameters, no annotations, and no output schema, the description is somewhat incomplete. While it covers the basic purpose and mode behavior, it doesn't address important contextual aspects: what the response looks like, error conditions, authentication requirements, or how it differs from the sibling tool. The description is adequate for basic understanding but leaves significant gaps for effective tool invocation.

    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 4 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions modes ('free browser mode', 'API') which relate to the 'track' parameter's enum values, but this doesn't provide additional semantic value beyond the schema's enum description. Baseline 3 is appropriate when schema coverage is complete.

    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 Grok AI to analyze X/Twitter links, search X, or answer questions.' This specifies the verb ('ask Grok AI') and resources/actions (analyze links, search, answer questions). However, it doesn't explicitly differentiate from the sibling tool 'grok_budget' - we can infer this tool is for general queries while grok_budget might be cost-related, but the description doesn't make this distinction clear.

    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 provides some usage context: 'Defaults to free browser mode; auto-switches to API for structured output.' This gives guidance on mode selection behavior. However, it doesn't explicitly state when to use this tool versus the sibling 'grok_budget' or provide clear when/when-not scenarios. The guidance is implied rather than explicit.

    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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