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MCP server · AI

Oco Claude Plugin

OCO Claude Code plugin — safety hooks, skills, agents, and MCP tools for any project

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Scored 6 months ago · breakdown

About Oco Claude Plugin

Oco Claude Plugin is an MCP server in the AI category: oCO Claude Code plugin — safety hooks, skills, agents, and MCP tools for any project.

Install

Claude Code
claude mcp add oco-claude-plugin -- npx -y oco-claude-plugin
npx
npx -y oco-claude-plugin

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README

Open Context Orchestrator (OCO)

CI npm License

Intelligent orchestration middleware for IDE-based coding assistants.

OCO sits between your IDE, an LLM, local tools, and context sources. It decides at each step whether to respond, retrieve context, call a tool, verify a result, or stop — producing structured decision traces for full auditability.

Claude Code Plugin

Install OCO as a Claude Code plugin in any project — one command:

npx oco-claude-plugin install          # project-level
npx oco-claude-plugin install --global # all projects
npx oco-claude-plugin status           # check installation
npx oco-claude-plugin uninstall        # clean removal

What you get:

  • Safety hooks — blocks destructive commands, protects sensitive files, detects loops, enforces verification before completion
  • 5 skills — /oco-inspect-repo-area, /oco-investigate-bug, /oco-safe-refactor, /oco-trace-stack, /oco-verify-fix
  • 3 agents — codebase-investigator, patch-verifier, refactor-reviewer
  • MCP tools — composite codebase search, error tracing, patch verification, findings collection

Plugin-only mode (Node.js only, no build required): hooks, skills, agents, and basic MCP tools work immediately. No API key required.

Full mode (requires Rust toolchain + OCO source repo): install the oco binary (cargo install --path apps/dev-cli) for indexed search, stack trace mapping, and task delegation via MCP.

Architecture

┌──────────────┐     ┌──────────────────────────────────┐     ┌─────────────┐
│  VS Code Ext │◄───►│  Orchestrator Core (Rust)        │◄───►│  ML Worker  │
│  (TypeScript)│     │  ┌────────────┐  ┌────────────┐  │     │  (Python)   │
└──────────────┘     │  │ Policy Eng │  │  Planner   │  │     └─────────────┘
                     │  │ Context Eng│  │ (DAG gen)  │  │
┌──────────────┐     │  │ Code Intel │  ├────────────┤  │     ┌─────────────┐
│  Claude Code │◄───►│  │ Tool RT    │  │ GraphRunner│  │◄───►│  LLM APIs   │
│  (MCP/Hooks) │     │  │ Retrieval  │  │ (parallel) │  │     │  (any)      │
└──────────────┘     │  │ Verifier   │  │ LlmRouter  │  │     └─────────────┘
                     │  └────────────┘  └────────────┘  │
                     │  MCP Server    Agent Teams        │     ┌─────────────┐
                     └──────────────────────────────────┘     │  SQLite     │
                                                              └─────────────┘

Orchestration v2 — Emergent Plan Engine

Medium+ tasks get a unique ExecutionPlan (DAG of steps) generated by the Planner. No templates — plans emerge from task, repo context, and available capabilities.

Request → Classifier → Trivial/Low: flat action loop
                      → Medium+: Planner → DAG → GraphRunner
                         ├── Parallel step execution (tokio::spawn)
                         ├── Observable plans (DAG overview, progress bar, per-step events)
                         ├── Hard step count enforcement per complexity tier
                         ├── Verify gates after implementation steps
                         ├── Replan on failure (max 3 attempts, budget pre-check)
                         ├── Multi-model routing (opus/sonnet/haiku per step)
                         ├── Effort-level routing (low/medium/high per step)
                         ├── Agent Teams (Mesh/HubSpoke/Pipeline topologies)
                         ├── HTTP hooks for Claude Code event integration
                         ├── MCP elicitation for interactive decisions
                         └── Cooperative cancellation + per-step budget limits

Rust Crates (15)

Crate Role
shared-types Domain types, ExecutionPlan DAG, CapabilityRegistry, TeamCoordinator, ElicitationRequest, EffortLevel
shared-proto Protobuf definitions (gRPC IPC)
policy-engine Deterministic action selection, budget enforcement, zero-limit guards
code-intel Tree-sitter parser, symbol indexer
retrieval SQLite FTS5, vector search, hybrid RRF ranking
context-engine Context assembly, dedup, step-scoped filtering
planner DirectPlanner (trivial) + LlmPlanner (Medium+ DAG generation)
orchestrator-core GraphRunner (DAG execution), LlmRouter (multi-model + effort), AgentTeamsExecutor
tool-runtime Shell/file executors
verifier Test/build/lint/typecheck runners
telemetry Tracing, event recording
mcp-server Axum HTTP + MCP server, Claude Code HTTP hook endpoints
dev-cli CLI binary with Terminal/JSONL/Quiet renderers
architecture-tests Crate dependency DAG enforcement, layer violation detection

Key Principles

  • Emergent orchestration — each plan is unique, generated from task + repo + capabilities
  • Provider-agnostic — works with Anthropic, Ollama, or any LLM API
  • Local-first — no cloud dependencies required
  • Auditable — every decision produces a structured trace
  • Observable — plan DAG overview, live progress bar, per-step lifecycle events, verify gate results
  • Bounded — explicit token, time, and tool-call budgets with per-step cancellation and step count limits
  • Deterministic policy — no LLM calls for routing decisions
  • Multi-model routing — opus for architecture, sonnet for implementation, haiku for exploration
  • Effort-level routing — automatic --effort selection per step role, budget-aware downgrade
  • Agent Teams native — Mesh, HubSpoke, Pipeline topologies with runtime enforcement
  • Interactive decisions — MCP elicitation for replan confirmation, architecture choices, verify gate failures
  • Event-driven UI — core emits structured events, CLI renders via pluggable renderers

Stack

Layer Technology
Core runtime Rust 1.85+ (edition 2024), Tokio, Axum
Orchestration DAG planner, GraphRunner, LlmRouter, Agent Teams
Storage SQLite + FTS5
Code analysis Tree-sitter (regex fallback)
IPC gRPC / Protobuf
IDE extension TypeScript, VS Code API
Claude Code MCP bridge, hooks, skills, agents
ML worker Python, Sentence Transformers
Telemetry tracing + OpenTelemetry

Getting Started

Prerequisites

  • Rust 1.85+ (edition 2024)
  • Node 20+ (for Claude Code plugin)
  • Python 3.11+ and uv (optional, for ML worker)
  • pnpm (optional, for VS Code extension)

Build from source

git clone https://github.com/hoklims/oco.git
cd oco

# Build all Rust crates
cargo build

# Run the test suite (421+ tests)
cargo test

# Run the CLI
cargo run -p oco-dev-cli -- --help

Optional components

# Python ML worker (embeddings & reranking)
cd py/ml-worker && uv sync

# VS Code extension
cd apps/vscode-extension && pnpm install

Quick usage

oco index ./my-project                    # Index a workspace
oco search "auth handler" --workspace .   # Full-text search
oco run "fix the login bug" --workspace . # Orchestrate (live trace)
oco serve --port 3000                     # Start HTTP/MCP server
oco doctor --workspace .                  # Check health
oco eval scenarios.jsonl                  # Run evaluations
oco runs list                             # List past runs
oco runs show last                        # Replay last run's trace

Output modes

oco doctor                                # Human: colors, spinners, icons
oco --format jsonl doctor                 # Machine: 1 JSON event per line
oco --quiet doctor                        # Quiet: only final result/errors

Configuration

Runtime config in oco.toml. See examples/oco.toml for a documented template.

LLM Providers

Provider Config Requirements
claude-code provider = "claude-code" (default) claude CLI on PATH
stub provider = "stub" None
anthropic provider = "anthropic" ANTHROPIC_API_KEY env var
ollama provider = "ollama" Local Ollama at localhost:11434

Documentation

Contributing

See CONTRIBUTING.md for setup instructions, conventions, and workflow.

License

Apache-2.0

README copy captured when this server was indexed; it may lag the repository. The original is authoritative.

Questions

About Oco Claude Plugin

How do I install Oco Claude Plugin?

Run claude mcp add oco-claude-plugin -- npx -y oco-claude-plugin, then add the server to your MCP client's configuration.

Is Oco Claude Plugin safe to use with an AI agent?

Its trust score is 37 out of 100 (low). It passes 1 of the 1 static security checks run so far. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Oco Claude Plugin still maintained?

Conduid hasn't recorded a commit date for this repository yet. Check the repository directly for recent activity.