openvds-mcp-server
Verified Safeby raghujayan
Overview
AI-powered natural language interaction and exploration of seismic data, including visualization, advanced analytics, and data validation.
Installation
docker-compose up --buildEnvironment Variables
- ANTHROPIC_API_KEY
- ANTHROPIC_MODEL
- VDS_DATA_PATH
- ACTIVE_VDS_SOURCE
- MOUNT_HEALTH_CHECK_ENABLED
- MOUNT_HEALTH_CHECK_TIMEOUT
- MOUNT_HEALTH_CHECK_RETRIES
- ES_ENABLED
- ES_URL
- ES_INDEX
- MAX_DATA_ELEMENTS
- VALIDATION_MODE
- VALIDATION_ENABLED
- VALIDATION_TIER1_ENABLED
- VALIDATION_TIER2_ENABLED
- VALIDATION_TIER3_ENABLED
Security Notes
The server's core functionality involves dynamically launching subprocesses (MCP servers for different VDS profiles) based on a `vds-profiles.json` configuration file. While this design enables profile switching, it introduces a critical dependency on the integrity of this configuration file. If a local attacker can modify `vds-profiles.json` to specify arbitrary commands or arguments, they could achieve code execution. For remote attackers, the risk is mitigated as the API endpoint for profile switching only allows selecting from pre-defined profiles, not creating new ones with arbitrary commands. No direct `eval` or `os.system` calls with unsanitized user input were observed. Sensitive credentials like `ANTHROPIC_API_KEY` are managed via environment variables, which is a good practice. Network communication with Elasticsearch and Anthropic API uses standard libraries.
Similar Servers
kreuzberg
Extracts text, tables, images, and metadata from a wide range of document formats (PDF, Office, images, HTML, etc.), with support for multiple OCR backends and an extensible plugin system. Can be run as a Micro-Agent Communication Protocol (MCP) server.
mcp-redis
Provides a natural language interface for AI agents to manage, search, and interact with structured and unstructured data in a Redis database.
mcp-server-datahub
Enables AI agents to interact with DataHub for comprehensive data discovery, governance, lineage exploration, and SQL query generation across an organization's data ecosystem.
Matryoshka
Processes large documents beyond LLM context windows using a Recursive Language Model (RLM) that executes symbolic commands for iterative document analysis.