MCP Web Search Server

fetch_url

Extract clean, LLM-optimized text content from any URL, including metadata. Ideal for feeding web pages into language models.

Instructions

Fetch and extract clean text content from a URL. Optimized for LLM consumption with metadata.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>"
    },
    "name": "fetch_url"
  }
}

Input Schema

Name	Required	Description
`url`	Yes	URL to fetch
`max_length`	No	Maximum content length in characters
`include_images`	No	Include image references

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jul 15, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden. It does not disclose behavioral aspects such as potential slowness, error handling, caching, or whether the fetch is destructive. Only mentions 'optimized for LLM consumption' without specifics.

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 a single sentence with no unnecessary words, achieving brevity and focus. It effectively communicates the core function.

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 absence of an output schema and the presence of three parameters, the description fails to explain what metadata is returned, how to interpret the response, or any error conditions. This leaves the agent without enough context to fully rely on the tool.

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 baseline is 3. The description does not add any semantic value beyond the schema; it merely restates the tool's purpose rather than elaborating on parameter usage.

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 tool fetches a URL and extracts clean text, with a specific verb and resource. It distinguishes from siblings by implying this is for direct URL fetching, but does not explicitly differentiate.

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 is provided on when to use this tool versus sibling tools like 'search_and_fetch' or 'web_search'. There is no indication of prerequisites or exclusions.

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

Deploy Server