ESG MCP Servers runs on your own machine — the client starts it, so there is no endpoint to ping. 77 installs a week from pypi. Last commit 28 Feb 2026.
31 MCP tools for ESG data extraction, PDF processing, vector search, and EU regulation analysis.
Today is the operative word: we check ESG MCP Servers every 15 minutes and re-read its code on every release. Watch it and you find out the day that stops being true.
This server runs on your own machine — install it with the package manager and the client starts it for you. Package name taken from the official registry entry.
claude mcp add esg-mcp-servers -- uvx esg-mcp-servers
{
"mcpServers": {
"esg-mcp-servers": {
"args": [
"esg-mcp-servers"
],
"command": "uvx"
}
}
}
[mcp_servers.esg-mcp-servers]
command = "uvx"
args = ["esg-mcp-servers"]
{
"mcpServers": {
"esg-mcp-servers": {
"args": [
"esg-mcp-servers"
],
"command": "uvx"
}
}
}
{
"mcpServers": {
"esg-mcp-servers": {
"args": [
"esg-mcp-servers"
],
"command": "uvx"
}
}
}
This one needs environment variables set before it will start:
ANTHROPIC_API_KEY (Anthropic API key — required for RAG queries and LLM-based metric extraction), POSTGRES_DSN (PostgreSQL connection string with pgvector extension (e.g. postgresql://esg:esg@localhost/esg_platform)), MONGODB_URI (MongoDB connection string for PDF binary storage via GridFS (e.g. mongodb://localhost:27017)), EMBEDDING_MODEL (Sentence-transformer model name for embedding generation (default: Snowflake/snowflake-arctic-embed-l-v2.0)), EMBEDDING_DIMENSIONS (Embedding vector dimension size (default: 1024)).
The author declared them in the registry entry; get the values from the project itself.
Local FAISS vector database for RAG with document ingestion, semantic search, and MCP prompts.
Bounded tools for rendering, extraction, RAG, enrichment, local discovery and review analysis.
Web design analysis with 26 MCP tools: layout, motion, quality, semantic search via pgvector
Web search, scraping, RAG answers with citations, and translation as MCP tools.
Temporal memory MCP server with LanceDB vector search, weight-decay scoring, and LLM consolidation
Encrypted-first embedded database with vector search and agent memory, exposed as MCP tools
Geospatial analysis: vector and raster GIS operations, cartographic maps, spatial data access
MCP server for Oracle, MSSQL, PostgreSQL, MySQL, SQLite, MongoDB, and Qdrant vector search access.
Answers built from our own checks of this server.