Skip to main content
Glama
pgzhang

MCP Google Server

by pgzhang

read_webpage

Extract readable text content from any webpage URL for analysis or processing.

Instructions

Fetch and extract text content from a webpage

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the webpage to read

Implementation Reference

  • Handler for the 'read_webpage' tool: validates input arguments, fetches the webpage content using axios, parses HTML with cheerio to extract title and cleaned text, returns structured content as JSON or error.
    } else if (request.params.name === 'read_webpage') {
      if (!isValidWebpageArgs(request.params.arguments)) {
        throw new McpError(
          ErrorCode.InvalidParams,
          'Invalid webpage arguments'
        );
      }
    
      const { url } = request.params.arguments;
    
      try {
        const response = await axios.get(url);
        const $ = cheerio.load(response.data);
    
        // Remove script and style elements
        $('script, style').remove();
    
        const content: WebpageContent = {
          title: $('title').text().trim(),
          text: $('body').text().trim().replace(/\s+/g, ' '),
          url: url,
        };
    
        return {
          content: [
            {
              type: 'text',
              text: JSON.stringify(content, null, 2),
            },
          ],
        };
      } catch (error) {
        if (axios.isAxiosError(error)) {
          return {
            content: [
              {
                type: 'text',
                text: `Webpage fetch error: ${error.message}`,
              },
            ],
            isError: true,
          };
        }
        throw error;
      }
    }
  • src/index.ts:109-122 (registration)
    Registration of the 'read_webpage' tool in the ListToolsRequestHandler, including name, description, and input schema definition.
    {
      name: 'read_webpage',
      description: 'Fetch and extract text content from a webpage',
      inputSchema: {
        type: 'object',
        properties: {
          url: {
            type: 'string',
            description: 'URL of the webpage to read',
          },
        },
        required: ['url'],
      },
    },
  • Helper function to validate input arguments for the 'read_webpage' tool.
    const isValidWebpageArgs = (
      args: any
    ): args is { url: string } =>
      typeof args === 'object' &&
      args !== null &&
      typeof args.url === 'string';
  • TypeScript interface defining the structure of the webpage content returned by the tool.
    interface WebpageContent {
      title: string;
      text: string;
      url: string;
    }
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'fetch and extract text content,' implying a read-only operation, but doesn't specify details like rate limits, authentication needs, error handling, or output format (e.g., plain text vs. structured data), leaving gaps in understanding how the tool behaves.

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, efficient sentence with no wasted words, clearly front-loading the core functionality. It's appropriately sized for a simple tool, making it easy to parse and understand quickly.

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 simplicity (1 parameter, no output schema, no annotations), the description is minimal but lacks completeness. It doesn't address behavioral aspects like what happens with invalid URLs or non-text content, and with no output schema, it should ideally hint at the return format. This leaves the agent with insufficient context for robust use.

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 input schema has 100% coverage, fully describing the single 'url' parameter. The description adds no additional semantic information beyond what the schema provides, such as URL format constraints or examples. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.

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 action ('fetch and extract text content') and resource ('from a webpage'), making the purpose immediately understandable. It doesn't differentiate from the sibling 'search' tool, which could be for broader web searches versus specific URL fetching, but the core function is well-defined.

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?

The description provides no guidance on when to use this tool versus the sibling 'search' tool or other alternatives. It lacks context about prerequisites, such as needing a valid URL or handling errors, which limits its utility for an AI agent in decision-making.

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

Install Server

Other Tools

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/pgzhang/mcp2'

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