MCP WebAnalyzer
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENAI_MODEL | No | OpenAI model to use | gpt-4 |
| OPENAI_API_KEY | Yes | Your OpenAI API key for Q&A functionality |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| url_to_markdown_toolA | Extract and convert web page content to markdown format. This tool scrapes a web page, removes unnecessary elements, ranks content by importance using a custom algorithm, and returns clean markdown. Perfect for RAG applications. Args: url: The web page URL to analyze and convert Returns: str: Clean markdown representation of the web page content |
| web_content_qnaA | Answer questions about web page content using RAG. This tool combines web scraping with RAG (Retrieval Augmented Generation) to answer specific questions about web page content. It extracts relevant content sections and uses AI to provide accurate answers. Args: url: The web page URL to analyze question: The question to answer based on the page content Returns: str: AI-generated answer based on the web page content |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: one converts web pages to markdown for general content extraction, while the other answers specific questions about web page content using RAG. There is no overlap in functionality or ambiguity about when to use each tool.
Both tools follow a consistent snake_case naming pattern with descriptive names that clearly indicate their function: url_to_markdown_tool and web_content_qna. The naming convention is uniform and predictable across the tool set.
With only 2 tools, the server feels thin for a web analysis domain. While the tools cover basic extraction and Q&A, there are likely missing operations like content summarization, metadata extraction, or batch processing that would make the set more complete and useful for agents.
For a web analysis server, the tool surface has significant gaps. It lacks essential operations such as summarizing content, extracting structured data (e.g., tables, links), analyzing page structure, or handling multiple URLs. Agents will struggle with common web analysis tasks beyond the two provided tools.