Stores and manages scraped LLM inference pricing data from various providers in a local database for querying and comparison.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@LLM Inference Pricing Research Server compare cloudrift and deepinfra pricing for llama 3.1"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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:
"cloudrift": "https://www.cloudrift.ai/inference"
"deepinfra": "https://deepinfra.com/pricing"
"fireworks": "https://fireworks.ai/pricing#serverless-pricing"
"groq": "https://groq.com/pricing"
Make a venv with uv
Sync venv with pyproject.toml (uv sync)
Make an API Key on Anthropic and Firecrawl
Complete the 2 tool calls in starter_server.py
Change the server_config.json to point to your server file
Complete any section in starter_client.py that has "#complete".
Test using any methods taught in the course
Use the following prompts in your chatbot but play around with all the LLM providers in the list above:
"How much does cloudrift ai (https://www.cloudrift.ai/inference) charge for deepseek v3?"
"How much does deepinfra (https://deepinfra.com/pricing) charge for deepseek v3"
"Compare cloudrift ai and deepinfra's costs for deepseek v3"
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
| Name | Required | Description | Default |
|---|---|---|---|
| identifier | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool extracts information, implying a read operation, but doesn't cover key aspects like whether it requires authentication, has rate limits, or what happens if the identifier isn't found. The description is too vague for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the purpose stated first, followed by brief sections for args and returns. There's no wasted text, but the structure could be slightly improved by integrating usage context more naturally.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (returns 'Formatted JSON string'), the description doesn't need to explain return values in detail. However, with no annotations and low schema coverage, it lacks completeness regarding behavioral traits and usage guidelines. It's minimally adequate but has clear gaps for a tool with one parameter.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It adds meaning by explaining that 'identifier' can be a 'provider name, full URL, or domain to look for,' which clarifies the parameter's purpose beyond the schema's basic type. However, it doesn't detail format constraints or examples, leaving gaps in understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Extract information about a scraped website.' It specifies the verb ('extract') and resource ('scraped website'), making it understandable. However, it doesn't explicitly differentiate from its sibling 'scrape_websites' (which likely performs scraping rather than extraction), so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 alternatives. It doesn't mention the sibling tool 'scrape_websites' or explain the relationship between scraping and extraction. There's no context about prerequisites, such as whether scraping must occur first, leaving usage unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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
| Name | Required | Description | Default |
|---|---|---|---|
| websites | Yes | ||
| formats | No | ||
| api_key | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 that content is stored, which implies persistence, but doesn't specify where or how. It also mentions the API key fallback to environment variables, which is useful context. However, it lacks critical behavioral details like rate limits, error handling, authentication requirements beyond the API key, or what happens if scraping fails for some websites.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and well-structured. It starts with a clear purpose statement, then lists parameters with helpful explanations, and ends with return information. Every sentence adds value with no redundancy or unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 parameters, nested objects, no annotations) and the presence of an output schema (which covers return values), the description does a good job. It explains parameters thoroughly and states the return type. However, for a tool that performs web scraping and storage operations, more behavioral context (like error handling or storage details) would make it more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the input schema, which has 0% description coverage. It explains that 'websites' is a dictionary mapping provider names to URLs, clarifies that 'formats' accepts specific values with a default, and describes the 'api_key' parameter's behavior with environment variable fallback. This compensates well for the schema's lack of descriptions, though it doesn't fully document all parameter nuances.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Scrape multiple websites using Firecrawl and store their content.' This includes a specific verb ('scrape'), resource ('websites'), and technology ('Firecrawl'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from its sibling tool 'extract_scraped_info'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 alternatives. There's no mention of the sibling tool 'extract_scraped_info' or any other context about appropriate use cases. The only implied usage is for scraping websites with Firecrawl, but no explicit when/when-not guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
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.
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.
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.
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If you are the server author, to access and configure the admin panel.
Live LLM API pricing: token prices, comparisons, cheapest-model lookups. No key required.
40+ web scraping tools from Firecrawl, Bright Data, Jina, Olostep, ScrapeGraph, Notte, and Riveter. Scrape, crawl, screenshot, and extract from any website. Starts at $0.01/call. Get your API key at app.xpay.sh or xpay.tools
Extract structured pricing tiers and addons from any SaaS pricing page URL. Built for AI agents.
AI inference pricing for agents: live and historical model prices, provider comparison.
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