College Football Data
This College Football Data MCP server, developed by Chris Leonard, provides AI assistants with access to comprehensive college football statistics via the College Football Data API. Built with Python and leveraging libraries like httpx and pydantic, it offers a natural language interface for querying game results, team records, player stats, rankings, and advanced metrics. The server implements robust error handling, rate limiting, and caching to optimize API usage. By abstracting the complexities of data retrieval and analysis, it enables AI systems to generate insights on team performance, analyze historical trends, and compare statistics across seasons. This implementation is particularly valuable for sports analysts, researchers, and fans seeking in-depth college football data analysis, facilitating use cases such as game prediction, player evaluation, and historical performance comparisons.
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
- monitoring
- Source
- PulseMCP
- Repository
- github.com/lenwood/cfbd-mcp-server
- Author type
- human
- Last scanned
- 2026-09-28
- Updated
- 2026-09-28
Use via MCP
Resolve College Football Data 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.