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LLM Inference Pricing Research Server

LLM Inference Pricing Research Server

by Fadi88
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

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_infoC
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_websitesB
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), formats, api_key. 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

    B3.1/5.0

    Scored across 2 tools

    Disambiguation5/5

    The two tools have clearly distinct purposes: 'scrape_websites' performs the scraping operation to gather data, while 'extract_scraped_info' retrieves and formats previously scraped information. There is no overlap in functionality - one creates data, the other queries it.

    Naming Consistency4/5

    Both tools use snake_case naming which is consistent, but they follow different verb patterns: 'scrape_websites' uses a verb_noun format while 'extract_scraped_info' uses verb_adjective_noun. This minor deviation prevents a perfect score, but the naming is still clear and readable.

    Tool Count2/5

    With only 2 tools for a 'LLM Inference Pricing Research Server', the surface feels severely underpowered. The server's name suggests comprehensive pricing research capabilities, but the tools only cover basic website scraping and data extraction - missing essential operations like price comparison, model benchmarking, cost analysis, or API integration that would be expected for this domain.

    Completeness2/5

    The toolset is severely incomplete for the stated purpose of 'LLM Inference Pricing Research'. While scraping and extraction are useful first steps, there are no tools for analyzing pricing data, comparing providers, calculating costs, or generating research reports. The server provides only data collection capabilities without the analysis tools needed to fulfill its research purpose.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

    Resources

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