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alexyangjie

Multi Fetch MCP Server

by alexyangjie
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.8.1

  • Disambiguation5/5

    All three tools have distinct purposes: fetching a single URL, fetching multiple URLs in parallel, and performing a web search. There is no overlap in functionality, so an agent can easily select the right tool.

    Naming Consistency4/5

    Tool names use a verb-only or verb+modifier pattern (fetch, fetch_multi, search). The naming is mostly consistent, though 'fetch_multi' introduces an underscore suffix that deviates slightly from the simple verb form.

    Tool Count5/5

    With three tools, the server is lean and focused on its core purpose of web fetching and searching. Each tool serves a distinct need without unnecessary additions, making the count well-scoped.

    Completeness4/5

    The server covers the essential operations for web access: single URL fetch, parallel fetch, and web search. While advanced features like custom headers or response filtering are absent, the basic lifecycle is complete enough for most use cases.

  • Average 3.6/5 across 3 of 3 tools scored. Lowest: 2.9/5.

    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
    • 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 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, the description carries full burden, but only mentions output format and API used. Does not disclose side effects, rate limits, or confirmation that it is read-only.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Single sentence that is clear and reasonably concise. Could be slightly more terse, but avoids unnecessary words.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Adequately describes function and output format for a simple tool. Missing usage guidance and behavioral context, but otherwise complete given no output 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?

    Input schema covers both parameters (query, limit) fully (100% coverage). Description adds no parameter-specific meaning beyond what schema provides, so baseline 3 applies.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    Clearly states it searches the web using Firecrawl and scrapes results in markdown and link formats. Implicitly differentiates from sibling tools 'fetch' and 'fetch_multi' which likely fetch specific URLs, but does not explicitly distinguish.

    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?

    No guidance on when to use this tool vs alternatives. Lacks any 'when to use' or 'when not to use' instructions, leaving the agent guessing.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description carries full burden. It discloses that the tool grants internet access and optionally extracts markdown, but it fails to mention behavioral traits such as error handling, rate limits, authentication, or handling of large responses. The minimal disclosure leaves significant gaps.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is concise with two front-loaded sentences. It conveys the essential purpose without unnecessary fluff. However, it could be slightly more structured to include usage hints or parameter context.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given no output schema and the tool's internet-access nature, the description should explain return format, error handling, and limitations. It mentions markdown extraction and max_length parameter indirectly, but does not cover timeout, error codes, or pagination. It is adequate but not fully complete.

    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?

    The schema already describes all parameters (100% coverage). The description adds meaning by stating that the tool 'optionally extracts its contents as markdown,' which clarifies the default behavior of the 'raw' parameter. This adds value beyond the schema's description of 'raw' as 'Get the actual HTML content ... without simplification.'

    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 single URL from the internet and optionally extracts its contents as markdown.' This is a specific verb+resource combination that distinguishes it from siblings 'fetch_multi' and 'search' by emphasizing single URL retrieval.

    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 provides clear context that it is for fetching a single URL, but it does not explicitly exclude alternatives or mention when to use this tool versus 'fetch_multi' or 'search'. Usage guidelines are implied but not explicit.

    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?

    The description discloses parallel execution and return structure with error messages, which is sufficient for a read-only tool with no annotations. No behavioral contradictions.

    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, well-structured sentence that efficiently conveys the core functionality with no 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?

    The description covers parallel execution and return format (content or error), which is complete for a batch fetch tool. However, it does not detail the exact structure of the result objects.

    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 Fetch sub-object params are well-described. The description adds no extra meaning beyond 'multiple URLs in parallel', so 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 'Fetches multiple URLs in parallel' with a verb and resource, and the parallel aspect distinguishes it from the sibling 'fetch' tool for single URLs.

    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 use when multiple URLs need to be fetched efficiently via parallelism, but does not explicitly state when not to use or provide alternatives to the sibling 'fetch' tool.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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