Skip to main content
Glama
pamela-0

safe-fix-mcp

by pamela-0
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.2

  • Disambiguation5/5

    scan_dead_code is purely read-only analysis, while propose_removal_pr actually creates a branch and PR. Their purposes are complementary and unlikely to be confused.

    Naming Consistency4/5

    Both tool names use lower_snake verb-first conventions: scan_dead_code and propose_removal_pr. The pattern is predictable, though the second name is slightly less clean because 'removal_pr' bundles an action and an object into one noun phrase.

    Tool Count3/5

    Two tools is on the thin side and sits at the borderline of feeling complete. However, each tool has a distinct role in a focused analyze-then-propose workflow, so the small count is plausible for a narrow server.

    Completeness3/5

    The scan surfaces both dead code and dependency issues, but propose_removal_pr only handles unused imports. Dependency fixes and non-import dead-code removals are therefore dead ends, creating a notable gap in the advertised safe-fix domain.

  • 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
    • 6 commits in the last 12 weeks
    • Last stable release on
    • 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.

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

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

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

  • Behavior5/5

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

    With no annotations, the description fully carries the behavioral burden, and it does so well. It explains that the test suite is a safety gate, that failures revert all changes, that branch commits/pushes persist if only PR creation fails, and that human merge is always required.

    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 somewhat long but every sentence earns its place, covering prerequisites, side effects, failure modes, and parameter meaning. The critical safety guarantees are front-loaded, and there is no filler.

    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?

    For a tool with one optional parameter and an output schema, the description is remarkably complete. It covers prerequisites, refused inputs, failure behavior, push semantics, and merge policy, so an agent has enough to call it correctly without additional context.

    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 description coverage is 0%, but the description compensates by explaining repo_path as a real git repository with a clean working tree and noting uncommitted changes are refused. This adds meaningful semantics beyond the bare schema field, though it does not mention optionality/default behavior beyond what the schema already shows.

    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 specific action: propose a real branch and PR that removes unused imports from a Python repository. It adds precise scoping (single-name import lines, >=90% vulture confidence) that makes its purpose concrete and distinct from a general cleanup tool.

    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 specifying criteria for eligible imports and requiring a clean git repository with a full test suite. However, it never explicitly addresses when to use this tool versus scan_dead_code or mentions exclusions, leaving the routing partially to inference.

    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 present, the description carries full burden and does so well: it states the tool is read-only, never modifies anything, and describes the exact output (human-readable findings or 'No real candidates found.'). It also discloses the pyproject.toml dependency for deptry and explains the confidence threshold's trade-off. No annotation contradiction.

    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 compact—three sentences for behavior plus two parameter explanations—and all content earns its place, with read-only status front-loaded.

    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?

    For a two-parameter tool with an output schema and a single sibling, this description covers input semantics, prerequisites, behavioral safety, and result format. The only omitted piece is explicit sibling differentiation, which belongs to usage guidelines. Combined with schema and output schema, an agent has everything needed to invoke it correctly.

    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?

    Schema has 0% description coverage, and the description fills in all of it: repo_path is the path to the repository and must contain pyproject.toml for dependency checks, and min_confidence maps to vulture's 0-100 threshold with a false-positive trade-off. This goes well beyond the parameter names and defaults.

    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 specific verb-resource pair: scan a Python repository, and names the two tools (vulture, deptry) and the two classes of findings (dead code, dependency issues). This clearly distinguishes it from the sibling propose_removal_pr, whose action is proposing a PR, not scanning.

    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?

    No explicit guidance on when to use this tool versus propose_removal_pr, nor any when-not conditions. However, the read-only declaration and human-readable output imply this is an analysis step, and the requirement about pyproject.toml provides some context. This makes usage predictable but not explicitly ruled for alternatives.

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

GitHub Badge

Glama performs regular codebase and documentation scans to:

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

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

safe-fix-mcp MCP server – quality and maintenance score on Glama

Copy to your README.md:

Score Badge

safe-fix-mcp MCP server – quality and maintenance score on Glama

Copy to your README.md: