Fix Memory MCP

Stop debugging the same error twice.

Fix Memory MCP is a local-first developer memory system for bugs.

AI coding agents are fast, but they often behave like they have no memory. They may fix a Python path issue today, then spend tokens rediscovering the same python.exe, venv, PATH, npm, build, deployment, or MCP setup issue three days later.

Fix Memory MCP gives Claude Code, Codex, Cursor-like agents, or any MCP client a curated memory of verified fixes. Before guessing, the agent can search your past bug fixes. After a real fix is verified, the agent can save the clean repair as a Markdown case.

In plain English: it is a debugging notebook for agents.

error appears
  -> search previous fixes
  -> reuse the closest repair pattern
  -> fix and verify
  -> save the new case
  -> future agents get smarter

Why This Exists

AI agents are fast, but they often waste tokens rediscovering the same environment, dependency, build, path, MCP, or deployment bug.

Fix Memory MCP gives them a small long-term memory:

• Local Markdown cases you can read and edit

• Hybrid keyword + TF-IDF vector search

• Failed-attempt notes so agents do not repeat dead ends

• A stdio MCP server with tools for search, read, save, recent, and index rebuild

• No cloud database, no external embedding API, no required network access

Related MCP server: Projectmem

What Makes It Different

This is not a general "remember everything I said" memory.

Many AI memory features store user preferences, project notes, or broad conversation context. Fix Memory MCP stores a narrower kind of memory: verified developer experience.

It does not embed every chat transcript. It saves only useful cases after a bug is fixed and verified:

• the exact error

• the project and environment context

• the root cause

• the patch summary

• the verification command and result

• the failed attempts that should not be repeated

That curation step matters. If every conversation is saved, the memory becomes noisy. If only repaired bugs are saved, the memory becomes a useful debugging asset.

Why Markdown Instead of SQLite

Fix cases are stored as Markdown because the data is developer knowledge, not ordinary business data.

Markdown is a good fit because it is:

• easy for humans to read and edit

• easy for AI agents to read

• friendly to Git, diff, merge, review, and sync

• portable across machines and tools

• transparent when a saved case contains private paths or sensitive details

SQLite may become useful later for very large collections, full-text search, or team usage. For a personal developer memory system, Markdown keeps the memory inspectable and versionable.

Why TF-IDF Instead of Embeddings

Many bug fixes are keyword-heavy. Errors often contain strong tokens such as:

• ModuleNotFoundError

• python.exe

• venv

• PATH

• pip

• npm

• cargo

• MCP

• ECONNREFUSED

TF-IDF is cheap, local, fast, private, and good enough for this kind of error retrieval. No embedding API key is required, and private bug history does not leave the machine.

Embedding search can still be added later when the case library grows or when semantic matching becomes more important.

What It Is Good At

• Repeated build failures

• Python / Node / Windows path problems

• MCP connection issues

• Dependency and virtual environment mistakes

• Framework-specific errors

• Deployment and service startup fixes

• Recording failed attempts as "do not try this again"

Features

• Local-first memory: cases live under data/ as Markdown files.

• MCP server: expose fix memory to AI coding tools through stdio.

• Hybrid retrieval: keyword search plus local TF-IDF cosine similarity.

• Zero external AI dependency: no OpenAI/Anthropic API key needed.

• Readable case format: root cause, patch, verification, reusable advice.

• Privacy by default: real fix cases are ignored by Git unless you choose to share them.

Project Layout

fix-memory-mcp/
  data/
    fixes/              # your private fixed cases
    failed-attempts/    # private "do not repeat" notes
    commands/           # private useful command notes
  scripts/
    fix_memory.py       # CLI
    fix_memory_mcp.py   # MCP stdio server
    vector_search.py    # local TF-IDF vector index
    self_check.py       # CLI + tool self-check
    mcp_smoke.py        # stdio MCP smoke test
  skills/
    fix-memory-workflow/
      SKILL.md          # optional Codex/agent skill instructions
  templates/
    fix-case.md         # case template

Quick Start

git clone https://github.com/l111403717-cloud/fix-memory-mcp.git
cd fix-memory-mcp
python -m pip install mcp
python scripts/self_check.py

Create your first case:

python scripts/fix_memory.py new \
  --title "Python ModuleNotFoundError from wrong working directory" \
  --project "demo-api" \
  --language "Python" \
  --framework "FastAPI" \
  --command "python app/main.py" \
  --error "ModuleNotFoundError: No module named app" \
  --tags "python,path,fastapi,windows"

Search it later:

python scripts/fix_memory.py search "ModuleNotFoundError FastAPI working directory"

Search modes:

python scripts/fix_memory.py search "MCP failed stdio" --mode hybrid
python scripts/fix_memory.py search "MCP failed stdio" --mode keyword
python scripts/fix_memory.py search "MCP failed stdio" --mode vector

Rebuild the local vector index:

python scripts/fix_memory.py rebuild-index

MCP Server

Run the server:

python scripts/fix_memory_mcp.py

Tools exposed:

• save_fix_case

• search_fixes

• search_fixes_vector

• get_fix_case

• list_recent_fixes

• rebuild_vector_index

Generic MCP config:

{
  "mcpServers": {
    "fix-memory": {
      "command": "python",
      "args": [
        "/absolute/path/to/fix-memory-mcp/scripts/fix_memory_mcp.py"
      ],
      "env": {
        "FIX_MEMORY_ROOT": "/absolute/path/to/fix-memory-mcp/data"
      }
    }
  }
}

Windows + Claude Code helper:

cd path\to\fix-memory-mcp
.\scripts\install_claude_mcp.ps1

Agent Prompt

Use FIX_MEMORY_AGENT_PROMPT.md to teach an agent this loop:

bug -> search memory -> repair -> verify -> save the clean fix

Case Quality Rules

A useful case should include:

• Exact error

• Project/environment context

• Root cause

• Related files

• What changed

• Verification command/result

• Reusable advice

• Failed attempts

• Sensitive-info check

Do not save full chats, full terminal logs, secrets, API keys, cookies, passwords, private account data, or private source files.

Self Check

python scripts/self_check.py
python scripts/mcp_smoke.py

Roadmap

• Optional SQLite + FTS5 index for very large case libraries

• Optional embedding backends

• Better case sanitizer

• Git diff capture after verification

• Agent-friendly install command for more clients

• Web UI for browsing fix cases

License

MIT