ViperGPT Visual Question Answering
ViperMCP server implementation by Ryan Sherby that provides visual question-answering capabilities through a mixture-of-experts approach based on the ViperGPT framework. The implementation combines multiple AI models including Grounding DINO, SegmentAnything, GPT-4o variants, X-VLM, MiDaS, and BERT to handle three core task areas: visual grounding, compositional image question answering, and external knowledge-dependent image question answering. Built as a FastMCP streamable-http server with Docker support and Smithery deployment options, it generates and executes Python code to solve complex visual reasoning tasks, making it valuable for applications requiring advanced image analysis, object detection with natural language queries, and multi-step visual reasoning workflows.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-28.
Scan details: Circle-IR · 2026-09-28 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- cloud-infra
- Source
- PulseMCP
- Repository
- github.com/ryansherby/vipermcp
- Author type
- human
- Last scanned
- 2026-09-28
- Updated
- 2026-09-28
Use via MCP
Resolve ViperGPT Visual Question Answering from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.