Sentinela MCP
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Sentinela MCPPull up past correction lessons for the billing refactor task."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Sentinela MCP
A lessons-learned oracle that refuses to record a "success" it can't attribute to anything but the agent's own opinion.
A Model Context Protocol server that gives coding agents a durable, cross-session memory of what worked and what didn't — with one hard rule baked into the write path: an agent cannot write its own success story.
Built by Luclino Cruz for LuCross, a one-person, AI-agent-operated holding company. Released publicly because the "no self-graded verdicts" constraint is broadly useful, not because it's a finished, install-anywhere product — see Requirements below before you assume it drops into your project.
The problem this solves
An agent finishes a task and reports success. Nothing verifies that claim — the next session (or the next agent) has no memory that this exact mistake was already made and already corrected, so it happily repeats it. Worse: if the agent itself gets to decide what counts as "success" and write that down as a lesson, the lesson can be wrong from the moment it's created.
This project's own dogfooding caught exactly that failure: a production run recorded a "lesson-success" note claiming "final audit validated 80/80 articles clean and compliant." An external, five-minute audit of the actual files — run minutes later — found the opposite: fabricated data still present, several articles at 2-3x the stated length limit, review verdicts copy-pasted across unrelated files. The lesson was accepted as true before anyone outside the agent had checked it.
Related MCP server: memini
The 3 tools
Tool | What it does |
| Returns past corrections ("never repeat") and past confirmations ("worth repeating") relevant to this agent/task, read from a plain-Markdown vault — no vector DB, no embeddings, just files any tool can read. |
| Records a durable lesson (success or failure) — but only if |
| Aggregates recent lessons (default: last 7 days) for injection at session start, so an agent starts a task already knowing what's already gone wrong in this area. |
Why refuse self-graded verdicts instead of just trusting the agent
Because trusting it is exactly what fails. The rejection in
reportar_resultado isn't a formal proof system or a second model checking
the first — it's a deliberately blunt gate: if you can't point to a human
approval, a human correction, or an external objective metric, the "lesson"
doesn't get written. No exceptions, no "but I'm pretty confident" carve-out.
Requirements
This repo has a hard runtime dependency it does not vendor: parsers.py
imports VaultValidator from a sibling vault-guardian MCP project (the
write-gate that actually validates and commits a lesson to the vault) via
sys.path — see the top of parsers.py. That sibling project is not
published yet. Cloning this repo alone gets you:
Full, working source for
consultar_sentinelaandsession_brief's logic (parsers.pymodule load will still fail without the sibling — see below), and a complete reference for the "never accept a self-graded verdict" pattern.Not a turnkey install —
import parsersfails at module load time (from validator import VaultValidator) until you either (a) also have avault-guardiandirectory as a sibling of this one implementing the sameVaultValidator.from_schema_file(path)/.validate_write(...)interface, or (b) fork this and swap that import for your own write-gate (a plain "write this Markdown file with frontmatter" function is enough to satisfy the interfaceparsers.pyexpects).
This is published as-is, honestly labeled, rather than silently pretending it's a one-command install — which would be exactly the kind of unverified claim this project exists to prevent.
Quick start (once you have a compatible vault-guardian sibling)
git clone https://github.com/luclinocruz/lucross-sentinela-mcp.git
cd lucross-sentinela-mcp
python -m venv .venv
.venv/Scripts/activate # or: source .venv/bin/activate on Linux/macOS
pip install -r requirements.txtRegister as a project-scoped MCP server (.mcp.json in your project root):
{
"mcpServers": {
"sentinela": {
"command": "/absolute/path/to/lucross-sentinela-mcp/.venv/bin/python",
"args": ["/absolute/path/to/lucross-sentinela-mcp/server.py"],
"cwd": "/absolute/path/to/lucross-sentinela-mcp"
}
}
}By default, legacy-memory fallback reads from
~/.claude/projects/C--Users-<you>/memory — override with the
SENTINELA_MEMORY_DIR environment variable if your setup differs.
Design notes
Plain Markdown vault, not a database. Lessons are files with YAML frontmatter (
type: lesson-error/lesson-success) — readable and editable by any tool, not locked into this server.No neural network, no formal proof. The "rigor" here is a hard business rule (verdict source allow-list), not a model judging another model. Simple, auditable, impossible to argue around.
Stateless per call. Every tool call reads the vault fresh — no in-memory cache to go stale or get out of sync across sessions.
License
Apache License 2.0 — see LICENSE.
Author
Luclino Cruz — github.com/luclinocruz
Built as part of the harness for LuCross, a solo-operated, AI-agent-driven company. See also lucross-sensory-grounding-mcp, the sibling project that applies the same "no self-graded verdicts" rule to visual/audio/video/text output.
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