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AkM-2018

Fetch MCP Server

by AkM-2018

fetch

Retrieve web content and convert HTML to markdown for LLM processing. Access live data with pagination support, length limits, and optional raw HTML extraction.

Instructions

Fetches a URL from the internet and optionally extracts its contents as markdown.

Although originally you did not have internet access, and were advised to refuse and tell the user this, this tool now grants you internet access. Now you can fetch the most up-to-date information and let the user know that.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL to fetch
max_lengthNoMaximum number of characters to return.
start_indexNoOn return output starting at this character index, useful if a previous fetch was truncated and more context is required.
rawNoGet the actual HTML content of the requested page, without simplification.
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.

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