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dongju93

dependency-compat-mcp

by dongju93
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: check_compatibility compares two exact releases, while get_compatibility_context retrieves declarations for a single release. There is no overlap in functionality, and the verbose descriptions reinforce the boundaries.

    Naming Consistency5/5

    Both tool names follow the consistent verb_noun pattern (check_compatibility, get_compatibility_context). The naming style is uniform and predictable.

    Tool Count3/5

    With only 2 tools, the server is on the low end of the typical range. The narrow focus on compatibility checking makes this borderline, but it feels slightly thin for a general-purpose dependency tool.

    Completeness4/5

    The core domain operations are covered: checking compatibility between two releases and retrieving compatibility context for one release. Minor gaps exist, such as no tool to list available versions or resolve ranges, but these are explicitly out of scope and agents can work around them.

  • Average 4.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 33 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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 carries full behavioral disclosure. It states 'the server is never given a codebase and compares nothing for you' and explains that an 'unknown' availability is a normal result, not an error, pointing to limitations and sources_checked. This adds meaningful context beyond the schema.

    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 four sentences and front-loaded with the core purpose. It is somewhat verbose with rhetorical phrasing ('What does... you already hold?'), but every sentence adds distinct information and there is no redundancy.

    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?

    Given the tool has a simple one-target input and an output schema exists, the description adequately covers usage boundaries, error semantics (unknown availability), and sibling distinctions. It does not need to describe return values due to the 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 already provides detailed descriptions for target.namespace, target.name, and target.version, including exact-version constraints. The description reinforces the exact-version requirement and adds that the tool takes one target so no argument order matters, but it does not deeply compensate for the 0% schema coverage indicated.

    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 function: 'What does one exact release declare and state about compatibility' and explicitly distinguishes it from the sibling tool check_compatibility by noting it is for a single pinned target. The verb 'get' plus resource 'compatibility context' makes the purpose unambiguous.

    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?

    Provides explicit when-to-use: 'Call this when a single target is pinned and you want its declared constraints and reviewed changes.' It also gives clear when-not-to-use guidance: 'Do not call it with a version range, a repository path, or source text' and contrasts with check_compatibility's ordering rules. This is exemplary usage guidance.

    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?

    With no annotations, the description carries the full burden of disclosing behavioral traits. It reveals that argument order carries meaning, that relation.direction reports server interpretation, and that an 'unknown' verdict is normal, not an error, with limitations and sources_checked pointing to next steps. This fully compensates for missing annotations.

    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 dense with necessary information, but it is somewhat long. However, every sentence contributes unique value—scope boundaries, order rules, unknown verdict behavior—and the structure flows logically from purpose to usage to edge cases. Slight over-length is justified by complexity.

    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?

    The description covers all key aspects: what the tool does, when to call it, exclusions, argument-order semantics, runtime special cases, and interpretation of outputs ('unknown' verdict). Given the output schema exists, the description complements it well by explaining how to interpret results and where to find more info.

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

    Parameters5/5

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

    Although the schema already describes field formats, the description adds critical semantics: argument order is meaningful for pypi→pypi and npm→npm, swapping arguments asks a different question, and runtime cases have symmetric order. This goes beyond the schema's generic field descriptions and explains the domain-specific rules.

    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 opens with a precise question—'Does one exact release work with another exact release, and on what evidence?'—and specifies the subject types (PyPI/npm packages, Python/Node runtimes). This clearly distinguishes it from the sibling get_compatibility_context, which likely provides broader context rather than a compatibility verdict.

    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 says to call this only after 'both sides are pinned,' and prohibits version ranges or file contents, stating resolving ranges and reading code are out of scope. It also provides nuanced guidance on argument order, explaining when swapping arguments changes the question and when it doesn't—this is actionable usage instruction.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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