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tatn

MCP Server Diff Python

by tatn
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap with other tools, making it perfectly clear and distinct in purpose.

    Naming Consistency5/5

    The single tool name 'get-unified-diff' follows a consistent verb_noun pattern, and with no other tools to compare, there is no inconsistency.

    Tool Count2/5

    One tool is too few for a server named 'MCP Server Diff Python', which suggests a broader scope for diff operations, such as handling multiple diff formats or additional text comparison features.

    Completeness2/5

    The server is severely incomplete; it only provides a single diff tool, lacking obvious operations like applying diffs, comparing multiple texts, or supporting other diff formats, which are typical for diff-related domains.

  • Average 3/5 across 1 of 1 tools scored.

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

    • No issues in the last 6 months
    • No commit activity data available
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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 the output format ('Unified diff format') but fails to describe critical behaviors such as error handling, performance characteristics, or any side effects. For a tool with two inputs and no annotation coverage, this leaves significant gaps in understanding how it operates beyond its basic function.

    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, consisting of two sentences that efficiently state the tool's purpose and usage context without any redundant information. Every sentence earns its place, making it easy to parse and understand quickly.

    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 tool's complexity (comparing two texts) and the lack of annotations, output schema, and parameter descriptions, the description is incomplete. It does not explain what the Unified diff format entails, how differences are computed, or what the return values look like. This leaves too many unknowns for effective tool selection and invocation.

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

    Parameters2/5

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

    Schema description coverage is 0%, meaning the input schema provides no descriptions for 'string_a' and 'string_b'. The description does not add any semantic details about these parameters, such as what they represent (e.g., original vs. modified text) or any constraints. This lack of compensation for the schema gap results in a low score, as users must guess parameter meanings.

    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: 'Get the difference between two text articles in Unified diff format.' It specifies the verb ('Get'), resource ('difference'), and output format ('Unified diff format'), which is specific and actionable. However, since there are no sibling tools, the description cannot demonstrate differentiation from alternatives, preventing 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 Guidelines3/5

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

    The description provides some guidance with 'Use this when you want to extract the difference between texts,' which implies the context for usage. However, it lacks explicit when-not-to-use scenarios or comparisons to alternatives (though none exist here). This makes it adequate but not comprehensive, fitting a baseline score.

    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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