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mcp-ai-agent-server

mcp-ai-agent-server

by elcaiseri
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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fetch_webpage

Retrieve webpage content and metadata. Optionally extract clean text for analysis.

Instructions

Fetch content from a webpage.

Args: url: URL to fetch extract_text: If True, extract and clean text content

Returns: Webpage content and 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_webpage"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYes
extract_textNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavior. It states it fetches content, but omits critical details like network requirements, rate limits, timeout behavior, handling of redirects, or respect for robots.txt. The description is too minimal for a network-bound tool.

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 extremely concise: three lines in a clear Args/Returns structure. No redundant sentences; every part adds value. Ideal for quick comprehension.

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 (network fetch, two parameters, no output schema or annotations), the description lacks completeness. It does not explain return format (e.g., raw HTML, JSON structure for metadata), error behavior for invalid URLs, or handling of large pages. The agent cannot fully anticipate tool behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must compensate. For 'extract_text', it adds 'If True, extract and clean text content'—a useful semantic beyond the boolean type. However, the 'url' parameter lacks any added meaning (e.g., expected format, protocols). The coverage is unbalanced, leaving half the schema underexplained.

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 'Fetch content from a webpage' clearly states the action (fetch) and resource (webpage content). It distinguishes from sibling tools like fetch_news (news-specific) and read_file (local files), making purpose unambiguous.

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 on when to use this tool versus alternatives. The description implies general webpage fetching but does not mention prerequisites, limitations, or when not to use (e.g., for dynamic content or large pages).

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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