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ssh00n

intent-diff-mcp

by ssh00n
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TypeScript
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Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools serve distinct purposes: start_task captures the initial state, while get_intent_diff compares changes against that state. No overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun snake_case pattern (start_task, get_intent_diff), making their actions clear and predictable.

    Tool Count3/5

    With only 2 tools, the server is thin, but it is focused on a narrow workflow (snapshot and diff). For its limited scope, the count is borderline but acceptable.

    Completeness5/5

    The tool set covers the full lifecycle needed: capturing the original intent (start_task) and reporting drift (get_intent_diff). No obvious gaps for its stated purpose.

  • Average 4.2/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 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.

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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

  • Behavior3/5

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

    With no annotations, the description must convey behavioral traits. It explains the snapshot mechanism and drift measurement, but omits details on side effects, success/failure responses, or what the tool actually returns. This is adequate but not fully transparent.

    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 with three sentences, each providing essential information. Purpose is stated upfront, and there is 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 simplicity of the tool (single parameter, no annotations, no output schema), the description covers core purpose and usage. However, it lacks information about return values and error handling, leaving some contextual 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 coverage is 100% with a clear description for task_description ('verbatim and complete'). The tool description adds no additional semantic value beyond what the schema provides, so a baseline score of 3 is appropriate.

    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: 'Lock in the developer's original intent before coding starts.' It specifies a concrete action (capturing git HEAD snapshot) and distinguishes from sibling tool get_intent_diff, which measures drift after starting.

    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 explicit guidance: 'Call this the moment a new feature/refactor is requested.' It implies the context for use and indirectly contrasts with the sibling tool, but does not explicitly state when not to use.

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

  • Behavior4/5

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

    With no annotations provided, the description carries the burden. It explains the tool performs a comparison and report, implying no destructive side effects. However, it does not explicitly state that it is read-only or disclose authentication or rate limits, which would improve transparency.

    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 with two sentences: one for purpose and one for usage. No redundant information, every word adds value.

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

    Completeness4/5

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

    Despite lacking an output schema, the description adequately implies the tool returns a 'report' of context drift. It covers purpose, timing, and input parameter. Minor gap: no details on output format or structure, but sufficient for an AI agent to understand the tool's behavior.

    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 sole parameter 'target_dir' is fully described in the input schema (100% coverage). The description adds no further meaning beyond what the schema provides, so baseline 3 is appropriate.

    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: comparing saved original intent against code changes to detect context drift. It uses specific verbs like 'compare' and 'report', and distinguishes from the sibling tool 'start_task' by implying this tool is used after task start.

    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 explicitly instructs when to call the tool: 'right before telling the developer the work is done.' This provides clear usage guidance with no ambiguity, and the sibling tool context reinforces that this follows start_task.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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