Model Context Protocol: How RL4 Makes Your AI Context Portable and Permanent

The Problem Model Context Protocol Was Built to Solve

Every AI coding tool has the same fundamental limitation: it cannot access your data unless you manually feed it. Your project history, your architectural decisions, your past conversations — all of it is locked away from the AI that is supposed to help you code.

Model Context Protocol (MCP) solves this by creating a standardized way for AI tools to communicate with external data sources. And RL4 is the MCP implementation that makes your entire development history — every conversation, file change, and decision — permanently accessible to your AI assistant.

What Is Model Context Protocol?

Model Context Protocol is an open standard created by Anthropic that defines how AI applications talk to external tools and data. Before MCP, every AI tool needed custom integrations for every data source. MCP standardizes this with a universal protocol.

The three core primitives:

• **Tools** — functions the AI can call (search files, run queries, trigger actions)
• **Resources** — data the AI can read (schemas, file contents, project history)
• **Prompts** — pre-built templates for common workflows

Think of model context protocol as USB-C for AI tools. Before USB-C, every device needed its own cable. Before MCP, every AI editor needed its own integration for every data source.

The protocol uses JSON-RPC 2.0 and runs over stdio or HTTP with Server-Sent Events. This means MCP servers can be written in any language and run anywhere — locally, on a server, or embedded in an IDE extension.

How RL4 Implements Model Context Protocol

RL4 is an IDE extension for Cursor and VS Code that implements model context protocol as its communication layer. When you install RL4, it registers as an MCP server that your AI editor can call natively.

Here is what happens under the hood:

1. You install RL4 in Cursor or VS Code
2. RL4 registers as an MCP server (automatic, no config needed)
3. RL4 starts capturing your development context:
   - Chat messages → chat_history.jsonl
   - File events → activity.jsonl
   - Work sessions → sessions.jsonl
   - Thread summaries → chat_threads.jsonl
4. Your AI assistant can now call RL4's MCP tools
5. RL4 returns compressed, cited context to the AI

The critical difference between RL4 and a generic MCP server: RL4 does not just serve data — it enriches it. Raw chat logs become structured timelines. File events become patterns. Conversations become searchable, cited evidence.

RL4's MCP Architecture: Four Layers

Layer 1: Capture

RL4 captures development context automatically in the background:

• **Every chat message** between you and your AI assistant
• **Every file event** — saves, creates, deletes
• **Work sessions** — detected and grouped by activity bursts
• **CLI commands** — git operations, builds, test runs

No manual action required. RL4 captures from the moment you install it.

Layer 2: RCEP Compression

Raw development data is massive. Thousands of messages, hundreds of file events. RL4 uses RCEP (Reversible Context Extraction Protocol) to compress this data without losing meaning:

• **Semantic extraction** — pulls out decisions, constraints, and patterns instead of storing raw text
• **SHA-256 checksums** — every artifact gets a cryptographic hash for [integrity verification](/cursor/blog/evidence-based-ai-development)
• **ECDSA P-256 integrity seals** — device-only signatures prove context has not been tampered with

The result: hundreds of messages compressed into a compact snapshot that fits in any AI context window. Verified. Auditable. Trustworthy.

Layer 3: Enrichment

RL4 does not just store data — it understands it:

• **Timeline Enricher** — transforms raw events into a structured narrative with sessions, bursts, and causal links between conversations and code changes
• **Pattern Detector** — identifies recurring patterns: which files change together, common error types, architectural decisions that emerge over time
• **KPI Aggregator** — tracks development metrics: session duration, files touched, conversation depth, context utilization

When your AI calls an MCP tool, it gets enriched context — not a data dump.

Layer 4: MCP Serving

RL4 exposes 14 MCP tools that your AI editor can call:

| Tool | Purpose |

|------|---------|

| `run_snapshot` | Generate a full context package |

| `search_context` | RAG search across all evidence |

| `rl4_ask` | Natural language Q&A with citations |

| `search_chats` | Search conversation history |

| `search_cli` | Search CLI command history |

| `get_evidence` | Retrieve the activity journal |

| `get_timeline` | Read the enriched timeline |

| `get_decisions` | List logged decisions |

| `list_workspaces` | See synced workspaces |

| `set_workspace` | Switch workspace context |

| `get_content_store_index` | Browse content checksums |

| `read_rl4_blob` | Read content by checksum |

| `finalize_snapshot` | Clean up after snapshot use |

| `rl4_guardrail` | Validate citation requirements |

For the full reference, see the 14-tool MCP guide14-tool MCP guide/cursor/blog/rl4-mcp-tools-cursor-complete-guide.

Why Model Context Protocol Matters for Developers

Context Portability

With RL4's MCP implementation, your development context is not locked into any single tool. The same MCP server serves context to Cursor, VS Code, or any MCP-compatible client. Switch tools without losing contextSwitch tools without losing context/cursor/blog/switch-llm-without-losing-context.

Persistent Memory

Standard AI editors forget everything between sessions. RL4's MCP server gives your AI permanent memory. Monday morning, your AI knows exactly what you built on Friday — the decisions you made, the files you changed, the constraints you established.

Cited Answers

When RL4 answers a question via `rl4_ask` or `search_context`, it includes citations: the source file, date, and thread ID. You can verify every claim. This is evidence-based AI developmentevidence-based AI development/cursor/blog/evidence-based-ai-development — not hallucinated summaries.

Team Context Sharing

RL4 auto-syncs to Supabase. Team members can access the same project context from their own IDE. Onboard new developersOnboard new developers/cursor/blog/onboard-developer-ai-context-guide by giving them instant access to the full project history — not a stale README.

MCP in Practice: A Real Workflow

Here is how model context protocol works in a daily workflow with RL4:

Developer asks Cursor: "What did we decide about the auth system?"

→ Cursor recognizes this needs external context
→ Cursor calls RL4's MCP tool: rl4_ask("auth system decision")
→ RL4 searches evidence, timeline, and decisions
→ RL4 returns: "Decision dec-004 [Jan 14]: JWT with 15-min
   refresh and sliding window. Confidence: pass."
   Source: .rl4/timeline.md, L142

→ Developer gets a cited, verified answer in 2 seconds
→ No scrolling through old threads, no re-asking the AI

This is what model context protocol enables: AI that remembers, searches, and cites — automatically, through a standardized protocol.

Getting Started with RL4 and MCP

RL4 is the fastest way to experience what model context protocol can do for your development workflow:

1. Install RL4 from the VS Code Marketplace
2. Open your project — RL4 auto-registers as an MCP server
3. Run your first snapshot (Cmd+Shift+P → "RL4: Run Snapshot")
4. Start asking your AI questions — it now has persistent memory

Your development history becomes a searchable, portable, verified knowledge base that your AI can access through standard MCP tools.

**Install RL4 free**Install RL4 free/cursor/form — model context protocol for your IDE, set up in 2 minutes.