AI Hub
This MCP AI Hub server by Pengfei Ni provides unified access to 100+ AI providers through LiteLLM integration, enabling seamless switching between OpenAI, Anthropic, Google, Azure, AWS Bedrock, and other AI services through a single configuration file. Built with Python using FastMCP and featuring comprehensive model management with YAML-based configuration, environment variable support, and robust error handling, it offers three core tools for chatting with models, listing available models, and retrieving model information with support for both string and OpenAI message format inputs. The implementation includes extensive testing coverage, multiple transport options (stdio, SSE, HTTP), and flexible deployment configurations, making it ideal for developers building AI applications that need provider flexibility, organizations wanting to avoid vendor lock-in, and teams requiring centralized AI model management across different services without code changes.
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/feiskyer/mcp-ai-hub
- Author type
- human
- Last scanned
- 2026-09-28
- Updated
- 2026-09-28
Use via MCP
Resolve AI Hub 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.