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466,425 tools. Updated 2026-08-19 15:16

"etcd" matching MCP tools:

  • Get the complete inventory of projects Ratatosk tracks, with canonical slugs, to avoid guessing IDs in other calls.
    Apache 2.0
  • Execute and test TypeScript code in a sandboxed environment to manage Kubernetes clusters. Cache scripts for reuse, stream output, and validate with integrated tests.
    MIT
  • Verify that a record exists in the tamper-evident audit log by generating a cryptographic inclusion proof for a row ID, including signed epoch and Merkle proof.
    MIT
  • Retrieve etcd pod logs from Kubernetes/OpenShift clusters with time-based filtering, line limits, and real-time streaming capabilities for troubleshooting.
    Apache 2.0

Matching MCP Servers

  • Verify audit log integrity by retrieving the current head state and closed-epoch signatures. Enables compliance auditors to detect operator misbehavior since the last check.
    MIT
  • Verify that an answer is grounded in a given set of facts, checking each claim against the sources and returning confidence, claim verification lists, and source pointers.
    MIT
  • Attach signed human notes, override justifications, or intervention records to agent event spans, chaining into the Merkle audit log for verifiable provenance.
    MIT
  • Diagnose memory health by surfacing orphans, stale facts, contradictions, decay candidates, and database sizes. Use insights to maintain clean, consistent data.
    MIT
  • Extract GitHub PR review comments and transform them into learned constraints for the knowledge graph.
    MIT
  • Retrieve linting rules, patterns, and conventions for a file, filtering by type like architecture or API contracts.
    MIT
  • Assess the regression risk of a proposed code change by analyzing past bugs, test failures, and constraint violations for the file. Helps identify potential issues before deployment.
    MIT
  • Parse project markdown files (AGENTS.md, CLAUDE.md, GEMINI.md, .agents/skills/*.md) and return the declarative constraints they define.
    MIT
  • Scan a project codebase to extract entities and relationships, populating the knowledge graph. Optionally re-seed already processed files.
    MIT
  • Predict which tests will fail based on edited files, helping focus test runs and catch issues early.
    MIT
  • Record context-compaction events to audit token counts and re-injected facts, ensuring transparency across compaction boundaries.
    MIT
  • List recent compaction audit entries, newest first. Filter by session ID or limit count to verify AI agent decisions offline against a signed audit chain.
    MIT
  • Generate a CLAUDE.md document from the knowledge graph, summarizing top constraints, recent decisions, known bug regions, and co-edit patterns to give AI agents actionable context.
    MIT
  • Validate proposed code changes against known constraints, covering edit, create, and delete operations.
    MIT