Eclipse S-CORE APM Monorepo

Local-only agent packages for code understanding, working memory...

What You Get

Two ready-to-use packages for your AI agent:

Package	What It Does
graphify-codegraph	Query code structure (what classes/functions exist, how they're connected)
context-discipline	Working memory + outcome recording (agent tracks decisions and learns from past work)

Local only: Everything runs in your project. Observations stored in .score-local/ (not committed).

Quick Start

1. Install APM CLI

# macOS/Linux/Windows: https://github.com/microsoft/apm#installation
brew install microsoft/apm/apm  # or: pip install apm-cli

2. Install Packages

Option A: From the marketplace

The marketplace manifest is generated from apm.yml during packaging and committed at .claude-plugin/marketplace.json. From this repository, run:

apm pack

Then, from your project root:

apm marketplace add https://github.com/eclipse-score/mcp-servers
apm install context-discipline@eclipse-score-apm-marketplace --target copilot --trust-transitive-mcp
apm compile -t copilot

Installing context-discipline also installs its transitive dependencies: graphify-codegraph and apm-setup.

Option B: From a local path (development)

apm install /path/to/mcp-servers/packages/context-discipline --target copilot --trust-transitive-mcp
apm compile -t copilot

Installing context-discipline also installs its transitive dependencies: graphify-codegraph and apm-setup.

Option C: From a cloned checkout

git clone https://github.com/eclipse-score/mcp-servers
cd mcp-servers
apm install ./packages/context-discipline --target copilot --trust-transitive-mcp
apm compile -t copilot

Installing context-discipline also installs its transitive dependencies: graphify-codegraph and apm-setup.

3. Initialize Repository Setup Through MCP

After registering the server, call these tools for the target repository:

verify_setup(repo_path)
setup_graphify(repo_path)
setup_context_discipline(repo_path)

APM installation registers the declared MCP server, but does not execute repository setup automatically. Repository setup is explicit and performed by the apm-setup MCP server.

4. Agent Uses It

Once configured, your agent can call MCP tools:

# Working memory
wm.initialize_session(goal="...", subgoals=[...])
wm.query_graph("Show auth functions")
wm.record_decision(decision="...", reason=[...])
wm.record_outcome(task="...", verdict="pass", coverage=0.85)

Results: .score-local/observations.jsonl (local only, not committed).

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Documentation

• For users: You're reading it. More examples in each package's README.md.
• For agents: See AGENTS.md to create new packages.
• For contributors: See CONTRIBUTION.md.

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Packages at a Glance

graphify-codegraph

Wraps Graphify Labs graphify — deterministic AST parsing, no LLMs.

Generates: graphify-out/graph.json (code structure), graph.html (interactive explorer)

One-time setup: setup_graphify(repo_path) through the apm-setup MCP
Runtime: Agents query via MCP (no re-parsing)

See packages/graphify-codegraph/README.md for details.

context-discipline

Working memory system + local learning.

MCP tools:

• initialize_session() — Start a session with goal + subgoals + assumptions
• query_graph() — Ask about code structure
• record_decision() — Track reasoning
• record_outcome() — Record results (appends to .score-local/observations.jsonl)
• get_working_memory() — Retrieve session memory
• get_unverified_assumptions() — Check uncertain assumptions

See packages/context-discipline/README.md for details.

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How It Works

apm install context-discipline@eclipse-score-apm-marketplace --trust-transitive-mcp
  ↓
  Installs context-discipline plus graphify-codegraph and apm-setup

apm compile -t copilot
  ↓
  Generates .github/copilot-instructions.md
  
Agent runs
  ↓
  Calls MCP tools (initialize_session, query_graph, etc.)
  MCP servers execute
  Observations accumulate in .score-local/

For deeper details on APM concepts, see Microsoft APM docs.

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Local Learning Loop

Session 1: query_graph() + record_outcome()
Session 2: query_graph() + record_outcome()
Session 3: query_graph() + record_outcome()
   ↓ (observations.jsonl grows)
Agent patterns emerge
   ↓
Result: Fewer tokens, faster time-to-solution (all local)

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More Information

• Setup MCP: See packages/apm-setup/README.md
• Package examples: See each package's README.md
• Create new packages: See AGENTS.md
• Contribute to this repo: See CONTRIBUTION.md