Fetch MCP Server

Server Quality Checklist

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

Latest release: v0.1.0
Disambiguation4/5
Each tool has a clear primary purpose (fetch, search, CSS extraction, JS rendering, PDF, JSON optimization), but smart_fetch overlaps somewhat with the specialized fetch tools, requiring attention to descriptions to pick the right one.
Naming Consistency3/5
Most tools follow an object_action pattern (web_search, css_query, browser_fetch, pdf_fetch), but smart_fetch and optimize_json deviate by putting the action first, resulting in mixed conventions.
Tool Count5/5
Six tools is well-scoped for a fetch server, covering general fetching, specialized fetch modes, search, and JSON optimization without redundancy.
Completeness4/5
The server covers major fetch types (HTML, PDF, JS-rendered) and adds search and JSON optimization. A minor gap is the lack of a raw HTML fetch without transformation, but smart_fetch and css_query nearly fill this need.
Average 4/5 across 6 of 6 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
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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 annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is known. The description adds that only content matching the selector is returned, which is useful behavioral context, but it does not disclose caching nuances, failure modes, or handling of no-match scenarios.
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 a single sentence that front-loads the core action and output. There is no filler or redundant information, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of a rich input schema, output schema, and annotations, the description is sufficient for an agent to understand the tool's core function. It lacks sibling differentiation, but that is partially addressed by the clarity of the CSS selector extraction purpose. The lack of cache mention is mitigated by the schema descriptions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides 100% coverage for all parameters with descriptions, including examples for the selector. The description itself does not add meaningful parameter explanation beyond restating the selector's purpose, which is already in the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: fetching a page and extracting content matching a CSS selector. It uses a specific verb ('Fetch') and resource (page + CSS selector), effectively distinguishing it from siblings like web_search or pdf_fetch.
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?
There is no explicit guidance on when to use this tool versus alternatives like smart_fetch or browser_fetch. The description implies the use case but does not provide exclusions or mention alternative tools, leaving the agent to infer when this is the right choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds no extra behavioral context such as side effects, permissions, or result format, but does not contradict annotations.
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 a single, front-loaded sentence with no wasted words, making it highly concise and structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The combination of a clear one-sentence description and a fully documented schema with an output schema provides sufficient information for an agent to invoke the tool. However, it could benefit from a note about file path handling, though that is already in the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All four parameters are fully described in the schema with 100% coverage, so the description adds no additional parameter semantics. The schema's parameter descriptions carry the full burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Optimize') with a clear resource ('any JSON payload') and purpose ('reduce token usage'), effectively distinguishing it from the sibling fetch/search tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for reducing token consumption of JSON payloads, but it does not explicitly state when to use it vs alternatives or any exclusions. Given unrelated siblings, the context is clear but not exhaustive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds the key behavioral detail of falling back to HTML markdown when the URL is not a PDF, which goes beyond the annotations and clarifies expected behavior.
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 two sentences with no wasted words. It front-loads the primary action and then provides the fallback behavior, making it easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description captures the core functionality and fallback, and the output schema covers return values. While it does not mention limitations like pagination behavior or timeouts, the rich schema and annotations make the description sufficiently complete for a tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the parameter descriptions in the schema already explain url, pages, headers, and max_chars. The description itself does not add extra parameter semantics, so it rests at the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches a PDF URL and returns markdown text, with a specific verb ('Fetch'), resource ('PDF URL'), and output ('text content as markdown'). It also distinguishes itself from generic fetch tools by mentioning the PDF conversion and fallback behavior.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for PDF extraction but does not explicitly state when to use this tool over siblings like smart_fetch or browser_fetch. The fallback to HTML markdown is mentioned, but no exclusions or alternative recommendations are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds specific behavioral details: the use of DuckDuckGo as the engine and the markdown list output format, which go beyond the generic annotation hints.
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?
A single, front-loaded sentence conveys the action, method, and output format without any wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With a detailed parameter schema, informative annotations, and an output schema present, the description is sufficiently complete for a straightforward search tool. It lacks explicit usage guidance versus alternatives, but that gap is minor.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% parameter coverage with descriptions for query, region, and max_results. The tool description does not add further parameter meaning, so the baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs a web search via DuckDuckGo and outputs a markdown list, which distinguishes it from sibling fetch tools (e.g., smart_fetch, browser_fetch) that retrieve specific URLs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for web searches but does not explicitly contrast with sibling tools or state when to use this tool over fetch-based tools. No exclusions or alternative recommendations are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds useful context: it uses a real browser (Playwright) and converts to markdown, which is beyond what annotations provide. It does not mention edge cases like CAPTCHA handling, but the schema covers that via the 'headed' parameter.
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 a single, front-loaded sentence that conveys the essential purpose without unnecessary words. It earns its place and is easily scanned.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (8 params, output schema present), the description is sufficient: it names the core function and output. It does not need to explain return format since the output schema exists. Slightly more context about when to prefer this over smart_fetch could improve it, but it's already fairly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description does not add meaning beyond the schema; all parameter details are already in the input schema. It adds no new semantic information to justify a higher score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches JavaScript-rendered pages using Playwright and returns markdown, with a specific verb ('fetch'), resource ('page'), and output format. This distinguishes it from sibling tools like pdf_fetch (PDFs) and web_search (search results).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for pages that require JavaScript rendering, giving clear context on when to use it. However, it does not explicitly mention alternatives or exclusion criteria, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Goes beyond the readOnly/idempotent annotations by disclosing the HTML transformation (stripping navigation, ads, scripts), JSON default behavior (schema + sample for large arrays), and the availability of jsonpath for refinement. This adds meaningful behavioral context without contradiction.
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?
Three short, focused paragraphs. The main purpose is front-loaded, and every sentence provides useful detail without redundancy. It is appropriately concise given the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the two primary content types and key behaviors, and the presence of an output schema reduces the need to explain return values. It does not mention caching, headers, or error handling, but these are documented in the schema/annotations, making the overall definition sufficiently complete for effective use.
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?
Schema descriptions cover 100% of parameters, but the description adds semantic value by explaining the intended use of jsonpath (drill into specific items on follow-up calls) and the default JSON output behavior. It also highlights token-reduction benefits that influence parameter choices like max_chars.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb+resource: 'Fetch any URL and auto-optimize based on content type.' It clearly distinguishes from siblings by highlighting content-type adaptation (HTML→markdown, JSON→schema+sample) and token reduction, which sets it apart from browser_fetch or pdf_fetch.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear context on when to use the tool: for HTML vs JSON, and instructs to use the jsonpath parameter for follow-up drilling into JSON data. It also implies a token-saving use case, though it does not explicitly name alternative tools or state when not to use it.
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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