Fetch MCP Server
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 tool's purpose is clearly defined and distinct.
Naming Consistency5/5A single tool inherently has perfect naming consistency, as there are no other tools to compare it against for patterns or conventions.
Tool Count2/5A single tool is too few for most server purposes, making the set feel thin and limiting functionality. While the tool is well-defined, the server's scope appears minimal and could benefit from additional related operations.
Completeness2/5The server's domain is fetching web content, but with only a basic fetch tool, there are significant gaps. Missing operations might include handling different HTTP methods, managing cookies or sessions, parsing specific content types beyond markdown, or error handling for network issues.
Average 2.9/5 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 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
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions internet access and optional markdown extraction, but fails to detail critical behaviors such as error handling (e.g., for invalid URLs), rate limits, authentication needs, or what happens when max_length is exceeded. This leaves significant gaps in understanding how the tool operates.
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 efficiently; the first sentence is clear, but the second sentence adds redundant historical context about internet access that doesn't aid tool selection or invocation. This extra information reduces conciseness without adding practical value for the agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (internet fetching with multiple parameters) and lack of annotations and output schema, the description is incomplete. It doesn't explain return values, error conditions, or behavioral nuances, leaving the agent with insufficient information to use the tool effectively in varied contexts.
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?
The input schema has 100% description coverage, providing clear details for all four parameters (url, max_length, start_index, raw). The description adds minimal value beyond this, only implying that 'extracts its contents as markdown' relates to the raw parameter. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
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: 'Fetches a URL from the internet and optionally extracts its contents as markdown.' It specifies the verb ('fetches') and resource ('URL'), and distinguishes between fetching and optional markdown extraction. However, since there are no sibling tools, the differentiation aspect is not applicable, preventing a perfect score.
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 provides implied usage guidance by explaining that the tool grants internet access, which was previously unavailable, and suggests using it for up-to-date information. However, it lacks explicit instructions on when to use this tool versus alternatives (e.g., other data retrieval methods) or any exclusions, making it somewhat vague.
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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