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Igor Ganapolsky
Igor Ganapolsky

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I built pre-action gates that physically block Claude Code from repeating mistakes

#claudecode #ai #mcp #devtools

If you use Claude Code, Cursor, or any AI coding agent daily, you've probably noticed three recurring problems:

  1. Session amnesia — Every new session starts from zero. Claude doesn't remember the architectural decisions you made yesterday.
  2. Hallucinated completions — Claude says "Done, all tests passing" when nothing actually passes.
  3. Repeated mistakes — You fix the same bug three times this week because the agent keeps making the same error.

I got frustrated enough to build a solution: mcp-memory-gateway.

What it is

An MCP server that adds a reliability enforcement layer to AI coding agents. It's not just memory storage — it's a feedback loop that learns from mistakes and blocks them from recurring.

The four tools

1. capture_feedback

When Claude does something wrong, you capture it:

# Captured automatically via MCP tool call
capture_feedback(
  feedback: "down",
  context: "Claude force-pushed without asking",
  whatWentWrong: "Overwrote teammates commits",
  tags: ["git", "destructive"]
)
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2. prevention_rules

After repeated failures, the system auto-generates rules:

  • Never force-push without explicit user confirmation
  • Always run tests before claiming completion
  • Check all 100 occurrences when updating pricing strings, not just 3

These rules persist outside the context window. They survive compaction.

3. satisfy_gate

Pre-action checkpoints that force the agent to prove conditions are met:

Gate CI green on current commit
Status BLOCKED — last CI run failed
Action Agent cannot claim "done" until gate passes

This kills the hallucinated completion pattern.

4. construct_context_pack

Bounded retrieval of relevant history for the current task. Instead of dumping everything into context, it selects what matters — prevention rules, recent feedback, task-specific decisions.

How it works under the hood

Each piece of feedback gets a reliability score via Thompson Sampling (beta-binomial posterior). Noisy or one-off signals don't immediately become rules. Only patterns that recur above a confidence threshold get promoted to prevention rules.

The gate engine uses a default-deny model for high-risk actions. The agent must pass through checkpoint validation before executing anything flagged by prior failures.

Install 

npx mcp-memory-gateway serve
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Or add to your MCP config:

{
  "mcpServers": {
    "memory-gateway": {
      "command": "npx",
      "args": ["-y", "mcp-memory-gateway", "serve"]
    }
  }
}
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Works with Claude Code, Cursor, Codex, Gemini CLI, and any MCP client.

Numbers

  • 466 tests, 90% coverage
  • 18 MCP tools in the default profile
  • MIT licensed, free OSS core
  • $49 one-time Pro tier for dashboard and analytics

What I'd love feedback on

  • The Thompson Sampling approach for reliability scoring — anyone tried different bandit strategies?
  • How do you handle the compaction problem in your workflow?
  • What's your current workaround for session amnesia?

Repo: github.com/IgorGanapolsky/mcp-memory-gateway

Disclosure: I'm the creator of this project.

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