Automatised Pipeline

Cross-platform codebase intelligence for Codex, Gemini CLI, Claude Code, Cursor, VS Code, Zed, and any stdio MCP host.
One read-only Rust server, host-specific installation packages, and the same evidence-graded graph answers everywhere.

What An Agent Can Ask · Getting Started · Pipeline · Tools · Architecture · Zetetic Standard

Companion projects:
Hypermnesia MCP — persistent memory that consolidates and reconsolidates across sessions
zetetic-team-subagents — 97 genius reasoning agents + 18 team specialists
AI Architect Spec — TypeScript PRD generator that consumes our graph intelligence

Every AI coding assistant hits the same wall: you ask it to change handle_tool_call, and it either hallucinates a function that was renamed last week, edits something in the wrong community of the codebase, or silently breaks a call chain three modules away. Agents operate on strings; codebases have structure. The gap is where bugs live.

ai-architect-mcp-codebase is a cross-platform Rust MCP server for Codex, Gemini CLI, Claude Code, Cursor, VS Code, Zed, and other stdio MCP hosts. It indexes any Rust, Python, TypeScript, Java, Kotlin, Swift, Objective-C, C, C++, or Go codebase into a LadybugDB property graph (Ruby is dispatched on the shallow path — node-kind rows, no deep extraction — for 11 languages in total), resolves imports and call chains across files, detects functional communities via Leiden-class community detection, traces execution flows from entry points, builds a hybrid BM25 + sparse TF-IDF + RRF search index, and exposes all of it through 26 MCP tools.

It is the codebase intelligence layer that sits between a finding ("this bug exists") and a PRD ("here is the fix, here is what it affects, here is what it must never break"). It is read-only intelligence — it never writes code, opens PRs, or runs CI. It tells the system what is true about the code so the next stage can reason without guessing.

One pipeline stage = one MCP tool. 10 stages. 26 tools. 12,000+ lines of Rust. 1500+ tests. Zero warnings. Every constant sourced.

What an agent can ask it

For inferred Rust receiver calls, pass lsp: true to analyze_codebase and install rust-analyzer. The response's lsp_status.state distinguishes disabled, completed, and failed; failures retain their error and analysis continues on the available graph, which may contain partial LSP results. lsp_resolve retains the pass's counts; resolve.phase = "static" identifies the separate static-resolution receipt. Completion does not mean every call was resolved.

Analysis persists its coverage report for query_graph(graph="missed") and returns the same summary. Rust processes use explicit #[test] and #[kani::proof] attributes, with separate test and proof entry kinds. These are source declarations, not evidence of execution or successful proof. (Rust testing attributes, Kani proof attributes.) Graphs created before entry metadata was stored require a full reindex: analyze_codebase rebuilds them, and index_codebase automatically falls back to a full index when its incremental compatibility check detects the old schema.

analyze_codebase(path: "/path/to/project", output_dir: "/tmp/run")
  → index + resolve + cluster + build search index in one call
  → 430 nodes, 400 edges, 216 communities, 35 processes on our own codebase

search_codebase(graph_path, query: "process incoming tool requests")
  → hybrid ranked results: BM25 lexical + sparse TF-IDF semantic + RRF fusion
  → returns: handle_tool_call (score 0.021), dispatch_request (0.020), ...

get_context(graph_path, qualified_name: "src/main.rs::handle_tool_call")
  → 360° view: community membership, process participation,
    incoming calls, outgoing calls, types used, types that use it
  → did-you-mean suggestions when the symbol isn't found exactly

get_impact(graph_path, qualified_name)
  → blast radius: every process that transits this symbol, every community it touches
  → the answer to "what breaks if I change this?"

detect_changes(graph_path, diff_text OR base_ref+head_ref)
  → git diff → affected symbols → impacted communities → touched processes
  → risk score for the change

validate_prd_against_graph(prd_path, graph_path)
  → does the PRD reference real symbols? (symbol hallucination check)
  → does "scoped to X" match the actual community count?
  → does "doesn't affect main" hold against the call graph?

check_security_gates(graph_path, changed_symbols)
  → auth-critical community touch · unsafe symbol · public API change ·
    unresolved imports · test coverage gap

verify_semantic_diff(before_graph_path, after_graph_path)
  → what nodes/edges appeared, what disappeared, what dangles,
    new cycles via Tarjan SCC, regression score with verdict

Getting started

Prerequisites

• Rust 1.95.0 — pinned by rust-toolchain.toml, so rustup installs and selects it for you; the same compiler builds CI and the releases
• CMake (LadybugDB builds its C++ core from source — ~5 minutes first build, cached after)

Clone + build

git clone https://github.com/cdeust/ai-architect-mcp-codebase.git
cd ai-architect-mcp-codebase
cargo build --release
# First build: ~5 minutes (compiles LadybugDB C++ core)
# Subsequent builds: <1 second incremental

Register the MCP server

The repo ships a .mcp.json that Claude Code picks up automatically when you open the directory:

{
  "mcpServers": {
    "ai-architect": {
      "command": "cargo",
      "args": ["run", "--quiet", "--release", "--manifest-path", "Cargo.toml", "--", "--profile", "core"]
    }
  }
}

Or register globally (recommended agent setup — the core profile):

claude mcp add ai-architect -- /absolute/path/to/target/release/ai-architect-mcp-codebase --profile core

Tool profiles

The server registers one of two tool sets, chosen once at startup:

Select with the --profile flag or the AP_PROFILE environment variable (the flag wins):

ai-architect-mcp-codebase --profile core   # agent-facing 8
AP_PROFILE=core ai-architect-mcp-codebase  # same, via env
ai-architect-mcp-codebase                  # default: full (all 26)

The default stays full until the next major version — shrinking the default tool surface is a breaking change. New agent installations should opt into core: analyze_codebase already runs index + resolve + cluster in one call, so the 18 hidden tools are pipeline plumbing an agent never needs, and hiding them keeps the tool prompt small.

First run

# Run the binary directly to verify the handshake
./target/release/ai-architect-mcp-codebase

# Or exercise it via stdio JSON-RPC:
printf '%s\n' \
  '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{}}' \
  '{"jsonrpc":"2.0","id":2,"method":"tools/list"}' \
  '{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"health_check","arguments":{}}}' \
  | ./target/release/ai-architect-mcp-codebase

Use with other MCP hosts

The server is a self-contained stdio binary — any MCP host can launch it. Install once:

cargo install ai-architect-mcp-codebase   # installs the `ai-architect-mcp-codebase` binary into ~/.cargo/bin

Install into your agent host (auto-config)

One command detects your installed hosts and writes the right MCP config for each — never clobbering the rest of the file:

ai-architect-mcp-codebase install