MCP Proxy Server
Server Quality Checklist
Latest release: v0.12.1
- Disambiguation5/5
Only one tool exists, so there is no risk of ambiguity. Its purpose is clear and distinct.
Naming Consistency5/5With a single tool, naming consistency is not an issue. The tool name 'fetch' is clear and conventional.
Tool Count3/5One tool is minimal. While it covers the core action of fetching a URL, a proxy server typically involves additional operations like caching, authentication, or transformation, making this feel slightly under-scoped.
Completeness2/5The server only provides a single fetch operation, lacking obvious features for a proxy such as request customization, error handling, or support for different protocols. This is a significant gap for the stated purpose.
Average 3.9/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 8 of 22 community issues answered or closed in the last 6 months
- 0 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.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description partially covers behavior: it mentions fetching and markdown extraction but omits error handling, rate limits, or response truncation details. The meta note about past limitations does not add behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief with two sentences, front-loading the main action. The second sentence is slightly verbose but still concise overall.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no siblings or output schema, the description covers the core functionality and parameter usage adequately. It lacks mention of error scenarios but is sufficient for a fetch tool with well-defined schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the description adds minimal value beyond parameter descriptions. It provides context by linking 'optionally extracts markdown' to the 'raw' parameter, but not enough to raise the score above baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches a URL from the internet and optionally extracts markdown. It distinguishes this from any other tool by emphasizing internet access, and there are no siblings to differentiate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description tells the agent to use this tool for obtaining up-to-date internet information. Though lacking explicit when-not-to-use, the context of granting internet access implies its primary use case.
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.
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