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LLM Pricing Scraper MCP Server

LLM Pricing Scraper MCP Server

by alexis-htc
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

In this project, you are going to make a chatbot to scrape LLM Inference Serving websites to research costs of serving various LLMs. You will do this by writing an MCP Server that hooks up to Firecrawl's API and saving the data in a SQLite Database. You should use the following websites to scrape:

  1. Make a venv with uv

  2. Sync venv with pyproject.toml (uv sync)

  3. Make an API Key on Anthropic and Firecrawl

  4. Complete the 2 tool calls in starter_server.py

  5. Change the server_config.json to point to your server file

  6. Complete any section in starter_client.py that has "#complete".

  7. Test using any methods taught in the course

  8. Use the following prompts in your chatbot but play around with all the LLM providers in the list above:

Available Tools

2 tools
extract_scraped_infoB

Extract information about a scraped website.

Args: identifier: The provider name, full URL, or domain to look for

Returns: Formatted JSON string with the scraped information

Inputs: identifier (required). Returns: result

scrape_websitesC

Scrape multiple websites using Firecrawl and store their content.

Args: websites: Dictionary of provider_name -> URL mappings formats: List of formats to scrape ['markdown', 'html'] (default: both) api_key: Firecrawl API key (if None, expects environment variable)

Returns: List of provider names for successfully scraped websites

Inputs: websites (required), api_key, formats. Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.5/5.0

    Scored across 2 tools

    Disambiguation5/5

    The two tools have distinct purposes: scrape_websites handles scraping, while extract_scraped_info retrieves stored data. There is no overlap in functionality.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern using snake_case, making them predictable and easy to understand.

    Tool Count3/5

    Only 2 tools is minimal for a server claiming to be a pricing scraper. While the scope appears narrow, additional tools like listing providers or managing stored data would be expected.

    Completeness2/5

    The server lacks essential operations such as listing all scraped providers, deleting stored data, or updating scraped content. This creates a dead end where an agent cannot manage the scraped data effectively.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

    Resources

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    • mcp-price-scout

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      Enables scraping LLM inference pricing from websites like Cloudrift, DeepInfra, Fireworks, and Groq, storing results in SQLite for cost comparison.
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    • mcp-pricescout

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      Enables AI-powered competitor pricing analysis by scraping LLM inference pricing websites via Firecrawl, storing the data in SQLite, and answering cost comparison queries through natural language.
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