Provides tools for storing scraped LLM pricing data from various inference providers into a SQLite database.
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., "@mcp-server Compare deepseek v3 pricing between cloudrift and deepinfra"
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?
With no annotations, the description should disclose behavioral traits. It only states purpose and return format, not whether it's read-only, what happens if identifier is missing, or any side effects.
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 short and front-loaded with the tool's purpose. Every sentence is concise, though the Args/Returns formatting is conventional.
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?
For a single-parameter retrieval tool with an output schema, the description covers the input sufficiently but lacks behavioral context (e.g., error handling, idempotency). Adequate but not comprehensive.
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 input schema has 0% description coverage, but the description adds meaningful context: 'The provider name, full URL, or domain to look for', which clarifies the parameter beyond its name.
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?
It clearly states 'extract information about a scraped website', with a specific verb and resource. The sibling 'scrape_websites' implies this is for already-scraped data, but the relationship isn't explicitly stated.
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?
No guidance on when to use this tool versus 'scrape_websites' or alternatives. It does not mention prerequisites, such as needing a prior scrape.
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 |
|---|---|---|---|
| api_key | No | ||
| formats | No | ||
| websites | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It mentions API key handling and return type, but omits potential side effects like network usage, rate limits, or partial failure behavior.
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?
Extremely concise with a clear summary line and structured Args section. Every sentence adds value with no redundancy.
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?
Covers inputs and output clearly, and output schema presence reduces need for return value details. However, lacks guidance on partial failures or interaction with sibling tool, and could mention performance considerations.
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?
Despite 0% schema description coverage, the description adds significant meaning: explains 'websites' as provider_name-to-URL mappings, 'formats' with default values, and 'api_key' with environment fallback. This compensates well.
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 scrapes multiple websites using Firecrawl and stores content, with a specific verb-resource pair that distinguishes it from the sibling '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?
No explicit guidance on when to use this tool versus alternatives like 'extract_scraped_info', nor when not to use it. The description implies use for initial scraping but lacks context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0extract_scraped_infoscrape_websitesThe two tools have clearly distinct purposes: one scrapes multiple websites and stores content, the other extracts specific information from a previously scraped website. No overlap in functionality.
Both tool names follow a consistent verb_noun pattern using snake_case: scrape_websites and extract_scraped_info. The naming is predictable and clear.
With only 2 tools, the server is minimal but adequately covers the core workflow of scraping and extracting. It could benefit from additional tools for management, but the count is not inappropriate for the stated purpose.
The server covers the primary operations of scraping and extraction, but lacks tools for listing, updating, or deleting scraped data, which could limit agent workflows. Some notable gaps exist.
Unclaimed servers have limited discoverability.
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.
Enable language models to perform advanced AI-powered web scraping with enterprise-grade reliabili…
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