What transports does Bigbugai support?
It supports stdio transport for local MCP clients and HTTP/SSE transport (FastAPI + uvicorn) for remote access.

@bigbugAi
Basic information
MCP server exposing BigBugAI tools for trending tokens and token analysis.
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"bigbugai": {
"command": "uv",
"args": [
"-m",
"bigbugai_mcp.server_stdio"
],
"env": {
"BIGBUGAI_MCP_API_KEY": "your-secret",
"BTUNIFIED_API": "https://api.bigbug.ai",
"MCP_RATE_LIMIT": "60/hour"
}
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Bigbugai is a production-ready MCP server that exposes BigBugAI tools via stdio transport for local MCP clients (Claude Desktop, Cursor) and optional HTTP/SSE transport (FastAPI + uvicorn) for remote access. It is intended for developers who want to integrate BigBugAI token intelligence into AI assistants.
Install with uv pip install -e .[dev], set the required BIGBUGAI_MCP_API_KEY environment variable, then run uv run -m bigbugai_mcp.server_stdio for stdio mode or uv run -m bigbugai_mcp.server_http for HTTP mode. For Claude Desktop, add a bigbugai entry to claude_desktop_config.json with the appropriate command, args, and env.
It supports stdio transport for local MCP clients and HTTP/SSE transport (FastAPI + uvicorn) for remote access.
Set the `BIGBUGAI_MCP_API_KEY` environment variable. Optionally, `BIGBUGAI_API_KEY` or `BIGBUGAI_API_TOKEN` can be used for upstream HTTP calls; they fall back to `BIGBUGAI_MCP_API_KEY` if not set.
Rate limiting is per API key using a moving window strategy, configurable via the `MCP_RATE_LIMIT` environment variable (default: 60/hour).
Python 3.11 or higher.
Two tools: `get_trending_tokens(limit)` and `token_analysis_by_contract(chain, address)`. Their endpoints are documented in the README with fallback paths and response normalization.
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