Tenzir
Tenzir MCP server that provides integration with Tenzir data pipelines through the Model Context Protocol, built using FastMCP and Python. The implementation enables AI assistants to execute Tenzir data processing pipelines and access OCSF (Open Cybersecurity Schema Framework) schema information, including retrieving available OCSF versions, event classes, class definitions, and object schemas. Designed for cybersecurity and data analysis workflows where AI assistants need to work with structured security data formats and execute data transformation pipelines through Tenzir's query language, making it valuable for security analysts, threat hunters, and data engineers working with cybersecurity datasets.
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 2026-09-19.
Scan details: Circle-IR · 2026-09-19 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- devops-ci
- Source
- PulseMCP
- Repository
- github.com/tenzir/mcp
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve Tenzir 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.