mcp-proxy
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to compare it to. The tool's purpose is clearly defined and distinct by default.
Naming Consistency5/5A single tool inherently has perfect naming consistency, as there are no other tool names to be inconsistent with. The name 'fetch' is straightforward and follows a common verb pattern.
Tool Count2/5A single tool is generally too few for most server purposes, as it limits functionality and flexibility. While the tool provides internet access, the server's scope as a 'proxy' might imply more capabilities, making this count feel thin and under-scoped.
Completeness2/5For a server named 'mcp-proxy', which suggests broader proxy or internet-related functionality, having only a fetch tool is severely incomplete. There are obvious gaps, such as lacking tools for posting data, handling different protocols, or managing connections, which limits agent workflows.
Average 2.9/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It adds some context: it mentions internet access (implying network operations) and markdown extraction (a processing behavior). However, it omits critical details like rate limits, error handling, authentication needs, or performance characteristics. The description compensates partially but leaves gaps for a tool with network dependencies.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is not front-loaded and includes verbose, historical context about internet access that doesn't directly aid tool selection. The first sentence is clear, but the second paragraph is redundant and could be condensed. Overall, it's moderately sized but inefficient, with sentences that don't all earn their place in a tool description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (network operations, markdown extraction) and lack of annotations or output schema, the description is partially complete. It covers the core purpose and some behavioral aspects but misses details like response format, error cases, or limitations. It's adequate as a minimum viable description but has clear gaps for effective agent use.
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 description coverage is 100%, so the schema fully documents all four parameters. The description adds minimal semantic value beyond the schema, only implying that 'url' is fetched and 'contents' are extracted as markdown (related to 'raw' parameter). It doesn't explain parameter interactions or provide usage examples. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: fetching a URL from the internet and optionally extracting contents as markdown. It specifies the verb ('fetches') and resource ('URL'), making the function unambiguous. However, it lacks differentiation from sibling tools, but since there are none, this doesn't significantly impact clarity. The mention of internet access context is helpful but slightly verbose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal guidance on when to use this tool. It mentions that it 'grants internet access' and can fetch 'most up-to-date information,' which implies usage for real-time data retrieval. However, it lacks explicit when/when-not scenarios, alternatives, or prerequisites. No sibling tools exist to differentiate from, but the guidance remains vague and insufficient for optimal agent decision-making.
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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- Evaluate tool definition quality.
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