BigQuery & Tavily FastAPI MCP

Created By
osushinekotana year ago
bigquery & web search api + mcp server
Overview

What is BigQuery & Tavily FastAPI MCP?

BigQuery & Tavily FastAPI MCP is a lightweight and secure API designed for accessing and querying Google BigQuery datasets and Tavily search functionalities.

How to use BigQuery & Tavily FastAPI MCP?

To use this project, clone the repository, install the necessary dependencies, configure your environment variables, and run the application. The API will be accessible at http://localhost:8000.

Key features of BigQuery & Tavily FastAPI MCP?

  • Read-only access to BigQuery datasets and tables.
  • Security features including query validation and dataset access control.
  • Full support for standard BigQuery queries with cost control.
  • Tavily search and web content extraction capabilities.
  • RESTful API with comprehensive documentation.

Use cases of BigQuery & Tavily FastAPI MCP?

  1. Querying and analyzing large datasets in Google BigQuery.
  2. Extracting web content using Tavily for data enrichment.
  3. Integrating with other applications via a secure API.

FAQ from BigQuery & Tavily FastAPI MCP?

  • What are the prerequisites for using this API?

You need Python 3.11 or higher, a Google Cloud Project with BigQuery enabled, and a Tavily API key.

  • Is there a cost associated with using BigQuery?

Yes, BigQuery charges based on the amount of data processed by your queries.

  • Can I use this API for production applications?

Yes, it is designed to be secure and efficient for production use.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
osushinekotan
Star
0
Language
Python
License
-

Recommend Servers

View All
Mnemom

14 hours ago
//beforeyouship — LLM Cost Modeling From Your Editor
@Indiegoing

Query realistic LLM cost models without leaving your editor. beforeyouship models the **true monthly cost** of an LLM app architecture — retries, prompt caching, batch discounts, infra overhead, and 3×/10× growth — across GPT-5.x, Claude, Gemini, DeepSeek, and more. Not a token calculator: a planning tool for the design phase, before you commit to a stack. **No API key needed to try it** — demo mode covers the six free-tier models. A Pro key from [beforeyouship.dev](https://beforeyouship.dev) unlocks the full 18-model catalog. ## What you can ask - "How much will a RAG chatbot cost at 10,000 requests/day?" - "Compare Claude Haiku vs Gemini Flash pricing for my workload" - "What's the cheapest model for a multi-step agent at scale?" - "Show me current per-token prices for Anthropic models" ## Tools ### `estimate_cost` Full cost model for an architecture at a given usage level. Returns Naive / Realistic / Worst Case monthly cost per model, 3×/10× growth scenarios, and an opinionated recommendation with reasoning. ### `get_model_prices` Current per-1M-token pricing — input, output, cached input, batch — with context windows and staleness metadata. ### `list_archetypes` Seven preset architecture patterns (simple chatbot, chatbot with history, RAG pipeline, multi-model router, coding assistant, document processor, multi-step agent) used as starting points for estimates. ## Setup **Claude Code:** ​```bash claude mcp add --transport http beforeyouship https://beforeyouship.dev/api/mcp ​``` **Cursor / other clients** — add a remote server: ​```json { "mcpServers": { "beforeyouship": { "type": "streamable-http", "url": "https://beforeyouship.dev/api/mcp" } } } ​``` Add an `Authorization: Bearer bys_...` header with a Pro key for the full catalog. ## Try it > Estimate the monthly cost of a RAG pipeline at 10,000 requests/day

13 hours ago
Docwand

13 hours ago