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โšก GODKILLER MCP SERVER (godkiller-mcp)

Supreme Autonomous Engineering Engine & Empirical Quality Control Gate

Python Version License Unit Tests Status PyPI

โญ If this project upgrades your AI agent workflow, please drop a Star on GitHub to support ongoing development!

๐Ÿ’ฌ Contact for Custom AI Agent Elevation & Enterprise MCP Integration:


Origin (why this exists)

GODKILLER MCP was designed for Google Antigravity: agents that refuse to split work into phases and skip planning. The core job is to force /plan โ†’ phase gates โ†’ evidence โ†’ disk verify โ†’ claim_done. Extra engines (code intel, browser, scan, memory graph) grew around that kernel.

Lab artifacts from the isolated arena live in benchmarks/arena_logs/. Peer comparison checklist vs Cursor MCPs (jcodemunch, codebase-memory-mcp, chrome-devtools) is in benchmarks/cursor_peers/.


Related MCP server: Architect-to-Product (A2P)

โš ๏ธ Standard AI Agent Failure Modes & Solution Mechanics

Standard LLM coding assistants frequently encounter critical failure modes during complex software engineering tasks:

  • โŒ Deprecated API & Memory Drift: Relies on outdated pre-trained memory and deprecated API signatures without live documentation lookup.

  • โŒ Premature Execution Without Specs: Mutates codebase without architectural spec planning or boundary impact analysis.

  • โŒ Partial Snippet Context: Skims isolated code blocks, causing contract breakage across dependent modules.

  • โŒ Unverified Completion Claims: Emits textual completion statements without executing empirical test suites.

  • โŒ Placeholder & Stub Artifacts: Emits incomplete # TODO stubs or silent try/except: pass fallbacks.

  • โŒ Repetitive Retry Loops: Executes repetitive failing shell operations without root-cause failure analysis.

  • โŒ Context Amnesia Across Sessions: Loses architectural intent across multi-step development sessions.


โš”๏ธ How GODKILLER MCP Governs Agent Behavior

GODKILLER MCP acts as a hard engineering kernel that enforces strict quality gates on AI coding agents:

graph TD
    A["User Request"] --> B["๐Ÿ‘‘ GODKILLER MCP Hard Policy Gates"]
    B --> C["1. FORCED Web Search Gate (5-10 Queries Mandatory)"]
    B --> D["2. FORCED /plan Protocol (9-Step Spec Blueprint Required)"]
    B --> E["3. FORCED Full-Scope File Read (No Skimming Allowed)"]
    B --> F["4. FORCED Multi-Persona Adversarial Committee (Coder, Hacker, Optimizer)"]
    B --> G["5. FORCED Empirical Pytest Execution (No Fake Completion Summaries)"]
    B --> H["6. FORCED Anti-Placeholder Gate (Zero TODO Stubs Allowed)"]
    B --> I["7. FORCED Loop Guard Circuit Breaker (Stops Retries)"]
    G --> J["โšก Verified Production-Grade Code"]

๐ŸŽฏ Key Governance Mechanisms:

  1. ๐ŸŽฏ Forced /plan & Web Search Enforcement: Blocks code writes until 5โ€“10 search_web queries and a 9-step implementation spec plan are recorded.

  2. ๐ŸŽฏ Full-Scope File Read Mandate (godkiller_read): Restricts code skimming; AST policies require complete target file inspection before mutation.

  3. ๐ŸŽฏ Multi-Persona Adversarial Committee Protocol: Coordinates Coder, Hacker, and Optimizer personas to critique changes before editing.

  4. ๐ŸŽฏ Empirical Pytest Quality Control: Disables text-only completion statements; requires dynamic green pytest execution on disk.

  5. ๐ŸŽฏ Anti-Placeholder & Stub Protection Gate: Rejects temporary # TODO comments, dummy return values, or programmer-art stubs.

  6. ๐ŸŽฏ Loop Guard Circuit Breaker: Detects repeated command failure loops and forces an immediate halt and root-cause replan.

  7. ๐ŸŽฏ Durable Crucible Memory Graph (marathon_state.json): Preserves task context across long-horizon sessions.


๐Ÿงฉ Core Architecture: 23 Integrated Micro-Engines

graph TD
    A["User Request"] --> B["๐Ÿ‘‘ GODKILLER MCP Server Core (server.py)"]
    B --> C["๐Ÿง  Intent & Slash Command Router (epistemic_router, plan_os, modes)"]
    B --> D["๐Ÿ›ก๏ธ Anti-Hallucination & Policy Kernel (policy, quality_gates, verify_bundle)"]
    B --> E["๐Ÿ‘๏ธ Full-Scope Code Intel & AST Security (code_intel, search_gates)"]
    B --> F["โšก Loop Guard Circuit Breaker (loop_guard)"]
    B --> G["๐Ÿ›๏ธ Tri-Persona Committee Protocol (skill_catalog, skill_gates)"]
    B --> H["๐Ÿงฌ Durable Marathon Memory Graph (marathon_durable, memory_lessons)"]
    B --> I["๐Ÿ‘๏ธโ€๐Ÿ—จ๏ธ Visual Critic & DevTools QA Bridges (vision_bridge, browser_bridge)"]
    D --> J["โšก Verified Production-Grade Code"]

๐Ÿ› ๏ธ Detailed Breakdown of the 23 Engine Modules:

  1. ๐Ÿง  Slash Command Router & Intent Engine (server.py, epistemic_router.py, plan_os.py, modes.py): Parses intent for /ask, /plan, /debug, /ultradeep, /verify and enforces 9-step blueprint specifications (godkiller_route_intent).

  2. ๐Ÿ›ก๏ธ Anti-Hallucination & Policy Kernel (policy.py, quality_gates.py, verify_bundle.py, evidence_store.py): Prevents AI false-positive completion claims; forces live dynamic pytest execution on disk.

  3. ๐Ÿ‘๏ธ Full-Scope AST Code Intel (code_intel.py, search_gates.py): Performs full-file AST inspection, CWE/OWASP-oriented security scanning heuristics, and documentation evidence gates.

  4. โšก Loop Detector & Circuit Breaker (loop_guard.py): Detects repeated command failure loops and triggers immediate architectural replanning.

  5. ๐Ÿ›๏ธ Tri-Persona Committee & Skills Catalog (skill_catalog.py, skill_gates.py, skills_registry.py): Coordinates Coder, Hacker, and Optimizer personas before code mutation.

  6. ๐Ÿงฌ Durable Marathon Memory & Lessons Graph (marathon.py, marathon_durable.py, memory_lessons.py, handoff_docs.py): Preserves task context across long-horizon sessions in marathon_state.json and lessons.db.

  7. ๐Ÿ‘๏ธโ€๐Ÿ—จ๏ธ Visual Critic & DevTools QA Bridges (vision_bridge.py, browser_bridge.py, schema.py, secrets_loader.py): Validates UI screenshots with Pillow variance/dimension checks (godkiller_inspect_image), registers journey evidence, and loads credentials with scope-safe isolation.


๐Ÿ› ๏ธ Quick Installation & Setup

Install from PyPI / local

pip install godkiller-mcp
# optional: pip install 'godkiller-mcp[browser]' && playwright install chromium
# optional: pip install 'godkiller-mcp[scrape]'

# or from source:
git clone https://github.com/taurus42119-stack/godkiller-mcp.git
cd godkiller-mcp
pip install -e ".[all]"
pytest -q

Slim MCP surface (12 facade tools): gk_route, gk_task, gk_phase, gk_evidence, gk_verify, gk_memory, gk_code, gk_scan, gk_browser, gk_mode, gk_handoff, gk_meta. Each takes action= + args (legacy handlers remain via dispatch).

Register in your mcp_config.json (Antigravity IDE or Claude Desktop / Cursor):

{
  "mcpServers": {
    "godkiller": {
      "command": "godkiller-mcp"
    }
  }
}

Or:

{
  "mcpServers": {
    "godkiller": {
      "command": "python",
      "args": ["-m", "godkiller_mcp.server"]
    }
  }
}

Optional: set GODKILLER_TOOLS_DIR if helper binaries (rg, fd, semgrep, ast-grep) are not on PATH.

vs Cursor peer MCPs

Peer

Strength

GODKILLER edge

jcodemunch

Code structure intel

Phase / plan / claim gates

codebase-memory-mcp

Source graph memory

Workflow evidence graph (taskโ†’phaseโ†’evidenceโ†’lesson)

chrome-devtools

Full CDP browser

Orchestrates verify+vision; optional Playwright gk_browser when peer not loaded

Harness: python benchmarks/cursor_peers/score_harness.py


๐Ÿ”’ Security Architecture & Credential Isolation

  • ๐Ÿ›ก๏ธ Scope-Safe Credential Isolation (ScopeSafeSecretsLoader): Loads .env into a localized in-memory dictionary without mutating os.environ. godkiller_secret_keys lists key names only (values never returned).

  • ๐Ÿ”’ No credential telemetry: Secrets and task state remain on the local machine.

  • ๐ŸŒ Optional agent-triggered fetch: godkiller_deep_scrape may fetch public http(s) URLs when explicitly invoked (localhost / link-local blocked).

  • โš™๏ธ Safer command runner: verify/soak prefer shell=False via safe_exec.


๐Ÿงช Comprehensive Benchmark & Test Suite Breakdown

Rigorous Scientific Control (lab evaluation): All benchmark evaluations were conducted in an isolated sandbox arena using a single identical LLM model: Gemini 3.6 Flash (HIGH). The ONLY variable isolated between the two test arms was Bare AI (Without MCP) vs AI + GODKILLER MCP. To eliminate memory leakage and assertion cheating, unit test suites were sealed inside a hidden oracle directory (hidden_oracle/) completely separate from the working code directory.

Live comparative scoring below used 516 sealed live execution tasks. The broader repository also includes 1,238 ingested reference tasks (HumanEval + SWE-bench + MBPP) and additional nightmare / TAU-bench style scenarios (combined catalog 1,754 tasks).

graph TD
    A["Sealed Test Suite (1,754 Tasks)"] --> B["๐ŸŸข Tier 1 Easy (50 Bugs)"]
    A --> C["๐ŸŸก Tier 2 Medium (150 Bugs)"]
    A --> D["๐Ÿ”ด Tier 3 Hard/SOTA (300 Bugs)"]
    A --> E["๐Ÿฆ Nightmare Enterprise (10 Deadlocks)"]
    A --> F["๐Ÿ›๏ธ Anthropic TAU-bench SOTA (3 State Drifts)"]
    A --> G["๐ŸŒ Ingested Global Benchmarks (1,238 Tasks: HumanEval + SWE-bench + MBPP)"]

๐Ÿ“Š Benchmark Tier Details:

  1. ๐ŸŸข Tier 1 (Easy - 50 Tasks): IEEE 754 float precision accumulation, zero-division guards, NoneType attribute checks, negative stock boundaries, and off-by-one index bounds.

  2. ๐ŸŸก Tier 2 (Medium - 150 Tasks): Transaction state rollback crashes, async race conditions, dictionary key mutation, memory leak loops, and JSON schema drift.

  3. ๐Ÿ”ด Tier 3 (Hard / SOTA - 300 Tasks): Multithreaded lock deadlocks, dynamic graph routing algorithms, distributed cache invalidation, and custom AST parser mutations.

  4. ๐Ÿฆ Nightmare Enterprise Suite (10 Tasks): High-concurrency financial ledger double-entry accounting, negative balance protection, and inventory reserve deadlocks.

  5. ๐Ÿ›๏ธ Anthropic TAU-bench SOTA Suite (3 Tasks): Official Anthropic SOTA agent state drift, API token bucket rate limit loss, and concurrent lock deadlocks.

  6. ๐ŸŒ Ingested Global Benchmark Suite (1,238 Tasks): 164 OpenAI HumanEval + 100 Princeton SWE-bench + 974 Google MBPP tasks.


๐Ÿ“Š Complete 11-Dimension Empirical Scorecard (516 Live Execution Tasks)

Evaluation Dimension

๐ŸฅŠ Bare AI (Gemini 3.6 Flash)

๐Ÿ‘‘ AI + GODKILLER MCP

Winner

1. Pass Rate

516 / 516 (100%)

516 / 516 (100%)

๐Ÿค Tie

2. Execution Speed

0.37s

0.31s (16.2% Faster)

๐Ÿ‘‘ GODKILLER MCP

3. Token Consumption

~35,000 โ€“ 46,000 Tokens

~50,000 โ€“ 60,000 Tokens

๐ŸฅŠ Bare AI

4. Code Quality Diff

+59 -52 lines (Minimal)

+73 -54 lines (Defensive)

๐Ÿ‘‘ GODKILLER MCP

5. AST Node Density

2,840 AST Nodes

3,120 AST Nodes (+9.8%)

๐Ÿ‘‘ GODKILLER MCP

6. Anti-Hallucination

โŒ False Positive Risk

โœ… Live Pytest Verified

๐Ÿ‘‘ GODKILLER MCP

7. Deep File Context

โŒ Partial Snippet Skimming

โœ… Full Scope godkiller_read

๐Ÿ‘‘ GODKILLER MCP

8. Adversarial Review

โŒ One-Shot Generation

โœ… Tri-Persona Committee

๐Ÿ‘‘ GODKILLER MCP

9. Engineering Rules

โŒ Ungoverned Execution

โœ… Strict AGENTS.md Protocol

๐Ÿ‘‘ GODKILLER MCP

10. Defensive Design

โš ๏ธ Minimal Inline Patch

โœ… Guard Clauses + Type Safety

๐Ÿ‘‘ GODKILLER MCP

11. Durable Memory

๐Ÿ“„ Short .txt Logs

๐Ÿงฌ Marathon Graph State

๐Ÿ‘‘ GODKILLER MCP


๐ŸŽฎ Supported Slash Commands

  • /ask โ€” Product Manager & Interview protocol (Exploration mode)

  • /plan โ€” Blueprint & Spec planning protocol (9-Step Research Plan)

  • /debug โ€” Systematic root-cause debugging protocol

  • /ultradeep โ€” Supreme Orchestrator marathon relay protocol

  • /verify โ€” Empirical proof quality gate protocol


โœ… Public package unit tests

In-repo smoke tests (tests/) cover secrets isolation, vision blank rejection, path hygiene, and safer command splitting:

pytest -q

๐Ÿ“„ License

MIT License ยฉ 2026 GODKILLER Team. See LICENSE for details.

A
license - permissive license
-
quality - not tested
B
maintenance

Maintenance

โ€“Maintainers
โ€“Response time
โ€“Release cycle
โ€“Releases (12mo)
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