elim-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., "@elim-mcpRecall what's been ruled out for the checkout timeout bug."
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.
elim-mcp
Every memory tool remembers what's true. This one also remembers what isn't.
An MCP server that remembers what coding agents have already tried, ruled out, and solved — so nobody, human or agent, re-investigates the same dead end twice across sessions, branches, and tools.
Before / after
── Session 1 · Claude Code · branch fix/checkout-500s ──────────────────
Agent: Checking if checkout timeouts are a DB connection pool issue…
→ remember(
content: "Investigated connection pool exhaustion — ruled out,
pool metrics normal under load",
kind: "ruled_out",
topic: "checkout-timeout"
)
→ remember(
content: "Suspected API key rotation mid-deploy — ruled out,
keys match and auth logs are clean",
kind: "ruled_out",
topic: "checkout-timeout",
follows_from: "<id of previous record>"
)
── Session 2 · Cursor · same branch, days later ────────────────────────
Agent: Let me check the DB connection pool—
→ recall(query: "checkout timeout")
← Found 3 record(s):
1. [ruled_out] checkout-timeout
Already ruled out: connection pool (metrics normal under load)
2. [ruled_out] checkout-timeout
Already ruled out: API key rotation (auth logs clean)
3. [solution] checkout-timeout
Confirmed cause: Redis disconnect on deploy
Agent: Skipping the pool. Looking at Redis reconnect behavior instead.Then dump the whole trail for a handoff or commit message:
→ trace(record_id: "<latest id>")
← Investigation chain (3 step(s), oldest → newest):
Step 1 — ruled out: connection pool
Step 2 — ruled out: API key rotation
Step 3 — solution: Redis disconnect on deployRelated MCP server: trw-mcp
Why this exists
Fresh chats and tool switches wipe working context. Mid-debug reasoning — “we ruled out the pool because CPU was fine, then ruled out the API key, then found Redis” — almost never survives past the current window. The fix sometimes lands in a commit message. The dead ends get thrown away.
Existing coding-agent memory tools are good at recording decisions and facts. None give first-class structure to what was investigated and eliminated, chained as one investigation. That negative trail is the wedge: narrower than “generic AI memory,” sharper for day-to-day debugging.
You already use some memory MCP. This one is specifically for stopping agents from cheerfully re-checking the connection pool next Tuesday.
Install
Paste into Cursor (.cursor/mcp.json) or Claude Code / Claude Desktop MCP config:
{
"mcpServers": {
"elim": {
"command": "npx",
"args": ["-y", "elim-mcp"]
}
}
}No API key. No setup. Restart your editor and it works.
On first write it creates .elim/ledger.db in the project root and auto-captures git branch + session id.
Same stdio / npx pattern. Example for VS Code–style MCP config:
{
"servers": {
"elim": {
"command": "npx",
"args": ["-y", "elim-mcp"]
}
}
}Use whatever key your client expects (mcpServers vs servers) — the command / args stay the same.
The 4 tools
Tool | Purpose | Example |
| Write a ruled-out theory, solution, decision, or note |
|
| Search the ledger before re-investigating |
|
| Load recent records for a chat, branch, or project |
|
| Walk the |
|
Four tools on purpose. Crowded memory servers expose dozens; MCP clients pick tools less reliably as that list grows.
How it works
Local-first SQLite at
.elim/ledger.db(project root, gitignored) with FTS5 keyword searchAuto-capture of git branch and a per-process session id — no manual scope required for the common case
follows_fromchaining links ruled-out steps into one investigation you cantrace()laterScopes:
chat·branch(default) ·project·global
Kinds: ruled_out · solution · decision · note.
Platform support (better-sqlite3)
elim-mcp keeps native SQLite performance via better-sqlite3. Install tries a prebuilt binary first, then compiles only if needed.
Usually zero compile | May need build tools |
macOS (Intel + Apple Silicon) | Alpine / musl Linux |
Windows 10/11 | Unusual architectures |
Linux glibc (Ubuntu, Debian, Fedora, …) | Very new / very old Node |
Node 18 / 20 / 22 / 24 · x64 / arm64 | Networks blocking prebuild downloads |
Most users never compile anything. If the native module can't load, elim-mcp prints actionable build-tool instructions to stderr and exits — it does not fall back to an in-memory or degraded database.
If that happens:
macOS:
xcode-select --installLinux (Debian/Ubuntu):
sudo apt install -y python3 make g++ build-essentialWindows: Visual Studio Build Tools (Desktop development with C++)
Then: npm rebuild better-sqlite3 and re-run npx -y elim-mcp.
Roadmap
Planned, not implemented yet:
Phase 2 — verify identical behavior across Cursor, Windsurf, VS Code; local embeddings for semantic recall; agent skill snippet (“
recallbefore proposing a fix”)Phase 3 — optional hosted team sync so “has anyone on the team already ruled this out?” is a real query
Contributing
Issues and PRs welcome on GitHub. Bug reports from real debugging sessions are especially useful — they double as product dogfooding.
License
MIT
Built by DevAsadYasin.
This server cannot be deployed
Maintenance
Related MCP Connectors
Shared debugging memory for AI coding agents
Persistent memory and cross-session learning for AI coding assistants (hosted remote MCP).
An MCP memory server. One memory your agents share — across models, devices and apps.
- memnodeOAuthdev.memnode
Persistent, inspectable memory for AI agents with lineage, correction, and a hosted MCP endpoint.
Related MCP Servers
- AlicenseNot gradedqualityBmaintenanceAn MCP server that lets coding agents build and query a persistent knowledge graph of concepts, architecture, and decisions, enabling them to remember across sessions.211 npm556MIT
- AlicenseBqualityBmaintenanceMCP server providing persistent engineering memory and spec-driven development workflows for AI coding agents, preserving learnings across sessions.41268 PyPIBusiness Source 1.1
- AlicenseNot gradedqualityCmaintenanceMCP server that captures and recalls coding session memory (failures, decisions, diffs) for AI agents, enabling cross-agent continuity and preventing repeated mistakes.41 npmMIT
- FlicenseNot gradedqualityBmaintenanceA local, cross-editor MCP server that provides persistent memory for coding agents, capturing and recalling decisions, conventions, and fixes across sessions without API keys.-