Fetch MCP Server

css_query

Read-onlyIdempotent

Fetch a web page and extract only the content matching a given CSS selector, returning just the relevant parts to reduce token usage and avoid processing irrelevant data.

Instructions

Fetch a page and return only the content matching a CSS selector.

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>",
      "selector": "<selector>"
    },
    "name": "css_query"
  }
}

Input Schema

Name	Required	Description
`ttl`	No	Cache TTL in seconds (default 1800)
`url`	Yes	URL to fetch
`selector`	Yes	CSS selector to extract (e.g. '#pricing-table', '.product-description', 'article')
`max_chars`	No	Maximum characters in output
`use_cache`	No	Return cached response if available (default True)

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 9, 2026

TDQS

A3.6/5.0

Behavior3/5

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

The annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is known. The description adds that only content matching the selector is returned, which is useful behavioral context, but it does not disclose caching nuances, failure modes, or handling of no-match scenarios.

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 sentence that front-loads the core action and output. There is no filler or redundant information, making it highly concise and well-structured.

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 presence of a rich input schema, output schema, and annotations, the description is sufficient for an agent to understand the tool's core function. It lacks sibling differentiation, but that is partially addressed by the clarity of the CSS selector extraction purpose. The lack of cache mention is mitigated by the schema descriptions.

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?

The schema provides 100% coverage for all parameters with descriptions, including examples for the selector. The description itself does not add meaningful parameter explanation beyond restating the selector's purpose, which is already in the schema. 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 the tool's function: fetching a page and extracting content matching a CSS selector. It uses a specific verb ('Fetch') and resource (page + CSS selector), effectively distinguishing it from siblings like web_search or pdf_fetch.

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?

There is no explicit guidance on when to use this tool versus alternatives like smart_fetch or browser_fetch. The description implies the use case but does not provide exclusions or mention alternative tools, leaving the agent to infer when this is the right choice.

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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