MCP Fetch
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'fetch' has a clear and distinct purpose.
Naming Consistency5/5A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The name 'fetch' is straightforward and appropriate.
Tool Count2/5One tool is too few for most server purposes, as it severely limits functionality and scope. While 'fetch' is useful, a server typically needs more tools to cover a meaningful domain effectively.
Completeness2/5The server's purpose appears to be fetching web content, but with only one tool, the surface is severely incomplete. There are obvious gaps, such as no ability to handle errors, cache results, or manage different content types beyond markdown extraction.
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
- 0 commits in the last 12 weeks
- No stable releases found
- 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.
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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?
With no annotations provided, the description carries the full burden. It discloses that the tool enables internet access and can extract markdown, but lacks details on rate limits, authentication needs, error handling, or output format. It adds some behavioral context but misses key operational traits for a fetch tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences but includes redundant context about historical internet access limitations that doesn't directly aid tool selection. It's somewhat front-loaded with the core purpose, but the second sentence could be more concise and focused on tool behavior rather than background.
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?
For a fetch tool with 4 parameters, 100% schema coverage, and no output schema, the description is moderately complete. It covers the purpose and internet access context but lacks details on output format, errors, or limitations, which are important given the tool's complexity and lack of annotations.
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 parameters. The description mentions optional markdown extraction, which loosely relates to the 'raw' parameter, but adds minimal semantic value beyond the schema. 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'), though it doesn't distinguish from siblings since none exist. The mention of internet access context is helpful but slightly dilutes the core purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by noting that the tool grants internet access where previously unavailable, suggesting it should be used for up-to-date information retrieval. However, it lacks explicit guidance on when to use alternatives (none exist) or any exclusions, leaving usage somewhat open-ended.
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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