MCP server that gives an LLM agent (e.g. HolmesGPT) a materialized, queryable service dependency graph in a graph database - deterministic root-cause (deepest failing service), blast radius, and observability blind-spot detection.
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# woodpecker-mcp [](https://github.com/sspcodeflix/woodpecker-mcp/actions/workflows/ci.yml)    **woodpecker-mcp exposes a materialized service dependency graph as an MCP toolset.** It provides an LLM-based agent such as [HolmesGPT](https://github.com/robusta-dev/holmesgpt) with a capability those agents do not retain on their own: a persistent, queryable graph of how services depend on one another. Root-cause analysis therefore becomes a deterministic graph traversal rather than a conclusion re-derived on each investigation. HolmesGPT remains unmodified. It launches woodpecker-mcp as a subprocess (or connects over HTTP) and discovers the tools it exposes - no fork, custom image, or plugin is required. --- ## Why this exists HolmesGPT markets a "Runtime Dependency Graph", yet its source holds no graph data structure, no graph database, and no graph-traversal code. Each investigation infers the relationships on the fly - from traces, Kubernetes owner-refs, and metric labels - then discards them, and root cause is whatever the model concludes through a "five whys" prompt. That design is deliberate - it buys freshness, statelessness, and breadth - but it carries costs that a materialized graph removes: | | Holmes (inferred) | woodpecker-mcp (materialized) | |---|---|---| | Where relationships live | model context, one investigation | a graph database (FalkorDB) | | Root cause | reasoned per run (non-deterministic) | deepest-failing-service, one Cypher query (exact, repeatable) | | Blast radius | re-derived each time | variable-length path traversal | | Explore it yourself | no | yes (browser UI + Cypher) | | Blind-spot detection | no | yes | --- ## How it works ```mermaid flowchart TD H[…
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