Skip to main content
Glama
flutterninja9

Better Fetch

by flutterninja9
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one handles single-page fetching while the other includes nested URL crawling. The descriptions explicitly differentiate between these scopes, leaving no room for confusion or misselection.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'fetch_website' as the base, differentiated by descriptive suffixes ('_single' and '_nested'). This predictable naming makes it easy to understand their relationship and functionality.

    Tool Count2/5

    With only two tools, the server feels under-scoped for a 'Better Fetch' purpose. While the tools cover basic fetching scenarios, there are likely missing operations like handling authentication, adjusting fetch parameters, or error management that would be expected in a robust fetching toolset.

    Completeness2/5

    For a fetching domain, the toolset is severely incomplete. It lacks essential operations such as configuring fetch options (e.g., headers, timeouts), handling different content types, managing errors, or providing status information. This will likely cause agent failures when dealing with complex fetching tasks.

  • Average 3.2/5 across 2 of 2 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.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 crawling and markdown conversion but fails to disclose critical traits like rate limits, authentication needs, error handling, or what happens when limits (maxDepth/maxPages) are reached. The description is too vague about operational 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 a single, efficient sentence that front-loads the core functionality. Every word earns its place, with no redundant or vague phrasing, making it easy to parse quickly.

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

    Completeness2/5

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

    Given the tool's complexity (7 parameters, crawling behavior) and lack of annotations and output schema, the description is insufficient. It omits details on return format, error cases, performance implications, and practical usage constraints, leaving significant gaps for an AI agent to operate effectively.

    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%, so the schema fully documents all 7 parameters. The description adds no additional meaning beyond implying crawling behavior, which is already suggested by parameter names like 'maxDepth' and 'sameDomainOnly.' 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.

    Purpose4/5

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

    The description clearly states the action ('fetch website content with nested URL crawling') and transformation ('convert to clean markdown'), providing a specific verb+resource combination. It distinguishes from the sibling tool 'fetch_website_single' by specifying 'nested URL crawling,' though it could be more explicit about the distinction.

    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?

    The description provides no guidance on when to use this tool versus the sibling 'fetch_website_single' or other alternatives. It lacks context about scenarios where nested crawling is preferred over single-page fetching, such as for multi-page documentation or site-wide content extraction.

    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 mentions conversion to markdown, which is a behavioral trait, but lacks details on error handling, rate limits, authentication needs, or what 'clean' entails. This is inadequate for a tool that performs network operations.

    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, efficient sentence with zero waste. It is front-loaded with the core purpose and transformation, making it easy to understand quickly.

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

    Completeness2/5

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

    Given no annotations and no output schema, the description is incomplete. It lacks information on return values (e.g., markdown structure, error formats), behavioral constraints, and differentiation from the sibling tool, which is crucial for a tool with network dependencies.

    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%, so the schema fully documents both parameters. The description does not add any meaning beyond what the schema provides, such as URL format expectations or timeout implications. Baseline 3 is appropriate when schema does the heavy lifting.

    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 specific action ('Fetch content'), target resource ('from a single webpage'), and transformation ('convert to clean markdown'). It distinguishes from the sibling tool 'fetch_website_nested' by specifying 'single' versus implied nested/multiple pages.

    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 fetching a single webpage's content, but does not explicitly state when to use this tool versus the sibling 'fetch_website_nested' or other alternatives. No exclusions or prerequisites are mentioned.

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

GitHub Badge

Glama performs regular codebase and documentation scans to:

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

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

better-fetch MCP server

Copy to your README.md:

Score Badge

better-fetch MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/flutterninja9/better-fetch'

If you have feedback or need assistance with the MCP directory API, please join our Discord server