Skip to main content
Glama
AkM-2018

Fetch MCP Server

by AkM-2018
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.6.3

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'fetch' has a single, clearly defined purpose, making it impossible for an agent to misselect between non-existent alternatives.

    Naming Consistency5/5

    A single tool inherently exhibits perfect naming consistency, as there are no other tools to compare it against. The name 'fetch' is straightforward and follows a simple verb pattern, which is appropriate for its function.

    Tool Count2/5

    A single tool is generally too few for most server purposes, as it limits functionality and scope. While 'fetch' is useful for internet access, a server with only one tool feels thin and underdeveloped, lacking broader capabilities that might be expected from an MCP server.

    Completeness3/5

    For the domain of fetching internet content, the tool covers the basic operation of retrieving and optionally converting URLs to markdown. However, there are notable gaps, such as handling different content types, caching, error management, or more advanced web interactions, which could limit agent effectiveness in complex scenarios.

  • Average 3.5/5 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.

  • 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

  • Behavior3/5

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

    No annotations are present, so the description carries full burden. It discloses the key behavioral trait of markdown extraction (vs raw HTML), which aligns with the 'raw' parameter semantics. However, it omits other critical behaviors like error handling on invalid URLs, timeout behavior, redirect following, or rate limits.

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

    Conciseness3/5

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

    The first sentence is efficient and front-loaded with core function. However, the second paragraph contains unnecessary historical context ('Originally you did not have internet access') that does not aid tool invocation and consumes space without earning its place in a functional specification.

    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 simplicity (4 params, complete schema documentation, no output schema), the description provides sufficient context by explaining the markdown conversion behavior. It adequately covers the tool's functionality despite lacking error-handling details.

    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 coverage is 100% (baseline 3). The description adds value by clarifying the default markdown extraction behavior ('extracts its contents as markdown'), which complements the 'raw' parameter's description of HTML retrieval. This helps agents understand the default output format beyond what the schema-alone conveys.

    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 first sentence clearly states the verb (Fetches), resource (URL), and output format (markdown), earning high marks. However, the second paragraph shifts to meta-commentary about AI capabilities rather than tool function, slightly diluting the purpose statement. No siblings exist to differentiate from.

    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 context ('fetch the most up-to-date information') and mentions informing the user about internet access, but lacks explicit when-to-use/when-not-to-use guidance or alternatives. The guidance is embedded in narrative rather than structured directives.

    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

tmp_fetch MCP server

Copy to your README.md:

Score Badge

tmp_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/AkM-2018/tmp_fetch'

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