ESG MCP Servers

Official

by freminder · Python

31 MCP tools for ESG data extraction, PDF processing, vector search, and EU regulation analysis.

31 tools for ESG data extraction, PDF processing, vector search, and EU regulation analysis.

README.md5 KBView on GitHub

ESG MCP Servers

Open-source Model Context Protocol servers for ESG (Environmental, Social, and Governance) data extraction, analysis, and regulation management.

31 tools across 6 servers — install once, run only what you need.

Author: Ioannis Michos (johnmichos.tf@gmail.com)

Quick Start

bash

pip install esg-mcp-servers

Prerequisites

Service	Required for
PostgreSQL 16 + pgvector	Vector storage, metrics, regulations
MongoDB 7	PDF binary storage (GridFS)
Anthropic API key	RAG queries, metric extraction

Spin up local databases with Docker:

bash

docker compose up -d

Run the database migration:

bash

esg-mcp-migrate

Environment Variables

Copy .env.example and fill in your values:

bash

cp .env.example .env

Key variables:

Variable	Default	Description
`POSTGRES_DSN`	`postgresql://esg:esg@localhost/esg_platform`	PostgreSQL connection string
`MONGODB_URI`	`mongodb://localhost:27017`	MongoDB connection string
`ANTHROPIC_API_KEY`	—	Required for RAG and LLM extraction
`EMBEDDING_MODEL`	`Snowflake/snowflake-arctic-embed-l-v2.0`	Sentence-transformer model
`EMBEDDING_DIMENSIONS`	`1024`	Embedding vector size

Servers & Tools

esg-metrics-extractor (11 tools)

ESRS-aligned KPI extraction for emissions, energy, water, waste, social, and governance domains.

Tool	Description
`extract_emissions_data`	GHG Scope 1, 2, 3 extraction
`extract_energy_data`	Energy consumption & renewables (ESRS E2)
`extract_water_data`	Water withdrawal, discharge, consumption (ESRS E3)
`extract_waste_data`	Waste generation, recycling, landfill (ESRS E5)
`extract_social_data`	Workforce, diversity, H&S (ESRS S1)
`extract_governance_data`	Board composition & governance (ESRS G1)
`answer_esg_query`	Free-text RAG Q&A over documents
`keyword_similarity_search`	Semantic keyword search in documents
`detect_query_domain`	Classify query into ESG domain
`detect_emissions_query_type`	Detect emissions scope/year from query
`batch_extract_metrics`	Extract all KPIs and persist to DB

esg-pdf-processor (5 tools)

PDF validation, text/table extraction, and embedding generation.

Tool	Description
`verify_esg_report`	RandomForest ESG report classifier
`extract_text_chunks`	Extract and chunk PDF text
`extract_tables`	Table extraction with OCR fallback
`generate_embeddings`	Batch embedding generation
`process_pdf_full_pipeline`	End-to-end: extract, embed, store

esg-vector-store (5 tools)

pgvector CRUD operations for document chunks and query cache.

Tool	Description
`upsert_document_chunks`	Insert/update chunks with embeddings
`similarity_search`	Cosine similarity search
`get_cached_query_response`	Retrieve cached LLM responses
`cache_query_response`	Store LLM response in cache
`list_documents`	List indexed documents

esg-regulations (5 tools)

EU ESG regulation download, ingestion, and semantic search.

Tool	Description
`download_regulation`	Download regulation PDF from EUR-Lex
`download_all_regulations`	Batch download all configured regulations
`ingest_regulation`	Extract, parse articles, embed, store
`search_regulation_text`	Semantic search across regulation articles
`list_regulations`	List ingested regulations

esg-scraper (5 tools)

ESG report discovery and download from the web.

Tool	Description
`search_esg_reports`	Multi-engine search for ESG PDFs
`crawl_company_website`	Deep-crawl website for PDF links
`download_pdf`	Download PDF and store in GridFS
`get_scraping_status`	Check scrape job status
`cancel_scraping_job`	Cancel running scrape job

esg-mcp-all (31 tools)

All tools from all servers in a single process.

Claude Desktop Configuration

Add to your claude_desktop_config.json:

json

{
  "mcpServers": {
    "esg-metrics": {
      "command": "esg-metrics-extractor"
    },
    "esg-pdf": {
      "command": "esg-pdf-processor"
    },
    "esg-vectors": {
      "command": "esg-vector-store"
    },
    "esg-regulations": {
      "command": "esg-regulations"
    },
    "esg-scraper": {
      "command": "esg-scraper"
    }
  }
}

Or use the combined server for all tools:

json

{
  "mcpServers": {
    "esg": {
      "command": "esg-mcp-all"
    }
  }
}

Optional: Scraper Dependencies

The scraper server requires Selenium for JavaScript-heavy sites:

bash

pip install esg-mcp-servers[scraper]

Development

bash

git clone https://github.com/freminder/esg-mcp-servers.git
cd esg-mcp-servers
pip install -e ".[scraper,dev]"
docker compose up -d
esg-mcp-migrate

License

MIT — see LICENSE.

Install

Configuration

Environment variables

ANTHROPIC_API_KEYrequiredsecret

Anthropic API key — required for RAG queries and LLM-based metric extraction

POSTGRES_DSNrequired

PostgreSQL connection string with pgvector extension (e.g. postgresql://esg:esg@localhost/esg_platform)

MONGODB_URIrequired

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)

Add to VS Code Add to Cursor

claude_desktop_config.json

json

{
  "mcpServers": {
    "esg-mcp-servers": {
      "command": "uvx",
      "args": [
        "esg-mcp-servers"
      ],
      "env": {
        "ANTHROPIC_API_KEY": "<YOUR_ANTHROPIC_API_KEY>",
        "POSTGRES_DSN": "<YOUR_POSTGRES_DSN>",
        "MONGODB_URI": "<YOUR_MONGODB_URI>",
        "EMBEDDING_MODEL": "<YOUR_EMBEDDING_MODEL>",
        "EMBEDDING_DIMENSIONS": "<YOUR_EMBEDDING_DIMENSIONS>"
      }
    }
  }
}