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Server Quality Checklist

67%
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  • Latest release: v0.2.0

  • Disambiguation5/5

    Each tool targets a completely different interaction: adversarial challenge, one-shot conversation, multi-turn conversation, and code review. There is no overlap in purpose.

    Naming Consistency5/5

    All tool names follow the identical pattern 'grok_<verb>' (challenge, chat, consult, review), providing a clear and predictable naming convention.

    Tool Count4/5

    With four tools, the surface is lean but focused. It covers the main code interaction modes without being too few for a build-oriented MCP server.

    Completeness4/5

    The set covers challenging, chatting, consulting, and reviewing code. Missing a general code explanation or generation tool, but for the advertised purpose it is reasonably complete.

  • Average 3.9/5 across 4 of 4 tools scored.

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

    • 0 of 1 community issues answered or closed in the last 6 months
    • 21 commits in the last 12 weeks
    • Last stable release on
    • 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.

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      ]
    }

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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

  • Behavior4/5

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

    With no annotations, the description effectively communicates the key behavioral trait: stateless server, caller responsible for maintaining state. It clearly indicates that full message history is replayed each call. However, it omits details on response format, error handling, or limits.

    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?

    Two concise sentences with front-loaded purpose. Every word is necessary and informative, capturing the essence 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?

    No output schema exists, yet the description fails to specify what the tool returns (e.g., an assistant message). It also lacks context on error conditions, token limits, or comparisons to sibling tools. This makes it incomplete for a tool with three parameters and no output schema.

    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 provides good descriptions for 'messages' and 'timeout' (67% coverage). The tool description does not add parameter-specific info beyond the schema, and the 'model' parameter lacks any description, leaving a gap.

    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 specifies the tool's purpose: continuing a conversation with Grok by replaying full message history. It distinguishes the tool's stateless approach but does not explicitly differentiate from sibling tools like grok_chat or grok_review.

    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 explicit guidance on when to use this tool versus alternatives. The description mentions statelessness and caller-owned state, but does not compare to siblings or provide when-not-to-use scenarios.

    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, the description bears full responsibility. It states the tool returns severity-ranked issues with reproductions, offering reasonable output transparency. However, it does not disclose side effects, mutation risks, or any operational constraints beyond what the parameters imply.

    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 sentence with a colon, efficiently conveying purpose and output. Every word is meaningful, with no extraneous content.

    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 absence of an output schema and the four-parameter signature, the description provides a reasonable overview of inputs and outputs. However, it lacks details on return format, pagination (if any), and the meaning of the 'model' parameter, leaving some gaps.

    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 schema provides descriptions for three of four parameters (code, context, timeout), covering 75%. The tool description does not add new parameter insights or explain the missing 'model' parameter. Thus it adds minimal value beyond the schema.

    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 identifies the tool as asking Grok to adversarially break code, listing specific types of issues (edge cases, race conditions, security holes, adversarial inputs). This distinguishes it from sibling tools like grok_chat (general chat) and grok_review (code review), giving a clear and specific 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?

    No guidance is provided on when to use this tool versus alternatives like grok_consult or grok_review. The description does not mention prerequisites, limitations, or when not to use it, leaving the agent without decision-making context.

    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, the description carries the burden of disclosing behavioral traits. It explains the default git diff behavior and the effect of format parameter, but does not mention potential side effects like reading local files or the execution environment. The timeout parameter's purpose is only in the schema, not reinforced in the description.

    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 three sentences, each earning its place: purpose, default behavior, and a key usage hint. It is front-loaded and contains no redundant information.

    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?

    Despite having no output schema, the description does not explain what 'per-dimension review' means or what dimensions are reviewed. The 'focus' parameter is mentioned in schema but not elaborated. The 'model' parameter lacks any description, leaving the agent without guidance on model selection. This is incomplete for a tool with 7 parameters.

    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 high (86%), so the baseline is 3. The description adds value by explaining the default behavior when diff is omitted and emphasizing the json format for CI, but does not significantly elaborate on other parameters beyond what the schema provides.

    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: 'Have Grok review a git diff.' It specifies the verb (review), resource (git diff), and mentions the default behavior when diff is omitted. It distinguishes from siblings like grok_chat and grok_consult, which are conversational rather than code-review focused.

    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 explicitly advises when to use format='json' for CI gating, which provides clear usage guidance. However, it does not explicitly state when not to use this tool or compare to alternatives, though the sibling tools are sufficiently different to imply appropriate use.

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

  • Behavior5/5

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

    No annotations provided, but the description fully covers behavioral traits: stateless, one-shot, returns reply. No contradictions; the description is sufficient for 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?

    Two concise sentences with no waste. Every part is necessary and informative, front-loaded with the core purpose.

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

    Completeness5/5

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

    Given the simple stateless nature, 3 parameters, and no output schema, the description covers all needed context: purpose, statelessness, usage guidance, and parameter details.

    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?

    Schema coverage is 100%, baseline 3. The description adds value by explaining model parameter overrides and timeout details ('Defaults to 300. Raise for long grok-4 reasoning'), going beyond the schema.

    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 explicitly states 'Send a one-shot prompt to xAI Grok and return its reply', clearly identifying the action and resource. It distinguishes from sibling tools by noting 'Stateless — for multi-turn use grok_consult'.

    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 clear guidance: use for one-shot stateless queries, and directs to grok_consult for multi-turn interactions, naming the specific alternative.

    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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