ESG MCP Servers

{
  "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
  "name": "io.github.freminder/esg-mcp-servers",
  "description": "31 MCP tools for ESG data extraction, PDF processing, vector search, and EU regulation analysis.",
  "title": "ESG MCP Servers",
  "repository": {
    "url": "https://github.com/freminder/esg-mcp-servers",
    "source": "github"
  },
  "version": "0.1.2",
  "packages": [
    {
      "registryType": "pypi",
      "identifier": "esg-mcp-servers",
      "version": "0.1.2",
      "transport": {
        "type": "stdio"
      },
      "environmentVariables": [
        {
          "description": "Anthropic API key — required for RAG queries and LLM-based metric extraction",
          "isRequired": true,
          "format": "string",
          "isSecret": true,
          "name": "ANTHROPIC_API_KEY"
        },
        {
          "description": "PostgreSQL connection string with pgvector extension (e.g. postgresql://esg:esg@localhost/esg_platform)",
          "isRequired": true,
          "format": "string",
          "name": "POSTGRES_DSN"
        },
        {
          "description": "MongoDB connection string for PDF binary storage via GridFS (e.g. mongodb://localhost:27017)",
          "isRequired": true,
          "format": "string",
          "name": "MONGODB_URI"
        },
        {
          "description": "Sentence-transformer model name for embedding generation (default: Snowflake/snowflake-arctic-embed-l-v2.0)",
          "format": "string",
          "name": "EMBEDDING_MODEL"
        },
        {
          "description": "Embedding vector dimension size (default: 1024)",
          "format": "string",
          "name": "EMBEDDING_DIMENSIONS"
        }
      ]
    }
  ]
}