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PortfolioKB

mcp-safe-fetch

by PortfolioKB
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so there is no ambiguity at all. An agent cannot misselect between tools.

    Naming Consistency5/5

    With a single tool, naming is trivially consistent. The name 'fetch_clean' follows a clear verb_noun pattern.

    Tool Count3/5

    One tool feels thin for a server dedicated to fetching content. While the tool is well-defined, the server would benefit from additional tools (e.g., batch fetch, status check) to justify its scope.

    Completeness4/5

    The tool covers the core fetch-and-sanitize operation completely for a single URL. Minor gaps exist (e.g., no way to fetch multiple URLs or configure sanitization levels), but the basic use case is fully served.

  • Average 3.3/5 across 1 of 1 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?

    The description discloses that the tool performs injection sanitization and size capping, and returns a dict with cleaned content and an audit. However, it lacks details on error behavior, rate limits, or side effects, and there are no annotations to supplement.

    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 concise: a single sentence for purpose followed by two bullet-point-like arg descriptions. Every sentence adds value, and the key action is front-loaded.

    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?

    The description omits details about the return value structure (e.g., what 'audit' contains), error handling, and supported URL schemes. Given no output schema, this leaves gaps in understanding the tool's full behavior.

    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?

    With 0% schema description coverage, the description compensates by explaining that 'url' must be an http/https URL and 'max_chars' is a per-fetch cap clamped to a hard ceiling. This adds significant meaning beyond the schema titles.

    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 fetches a URL and returns injection-sanitized, size-capped text, which is a specific verb-resource pair. However, since no sibling tools are provided, it cannot be evaluated for differentiation, hence slightly less than perfect.

    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?

    The description provides no guidance on when to use this tool versus alternatives, nor does it mention any preconditions or exclusions. It simply describes what the tool does without context for selection.

    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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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.

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