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Memory for coding conventions and standing instructions that persists across sessions.
Memory for coding conventions and standing instructions that persists across sessions.
This is a well-architected MCP server for local coding convention storage with thoughtful security design. The codebase properly isolates sensitive operations, uses environment variables for credentials, and limits network exposure to a single intentional API call for metadata extraction. Minor code quality observations exist around error handling and input validation, but they do not pose meaningful security risks given the server's local-first architecture and narrow scope. Supply chain analysis found 3 known vulnerabilities in dependencies (0 critical, 3 high severity). Package verification found 1 issue.
6 files analyzed · 9 issues found
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Set these up before or after installing:
Environment variable: OPENROUTER_API_KEY
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-fedgeno-conventions-mcp": {
"env": {
"OPENROUTER_API_KEY": "your-openrouter-api-key-here"
},
"args": [
"-y",
"conventions-mcp"
],
"command": "npx"
}
}
}From the project's GitHub README.
Personal memory for coding conventions and standing instructions — one store, any MCP-compatible AI client, available in every project. Occasional other notes are fine too, but this is primarily meant to hold things like "always use 2-space indent in this language," "never force-push to main," corrections after getting something wrong, and workflow preferences — the kind of thing that should carry across sessions and projects rather than get re-explained every time.
There's no shortage of memory MCP servers — several well-established ones (mem0/OpenMemory, Zep/Graphiti, the official reference memory server, plus a long tail of smaller projects) already do "remember things across sessions." What's different here:
SessionStart, UserPromptSubmit) load and re-remind about standing rules on a fixed schedule, independent of whether any given model happens to notice. If you're not on Claude Code, you still get the tool descriptions, just not the hook guarantee.If what you want is a general-purpose "remember everything" store, or you're not on Claude Code and don't need the hook-driven determinism, one of the more general options above may fit better. This one is for someone who specifically wants a tight, coding-convention-focused memory that stays accurate and doesn't require trusting the model to remember to check it.
Every capture is classified into one of five types by the calling agent, guided by capture_thought's tool description (src/server.js):
| Type | What it means |
|---|---|
convention | A specific coding style/pattern rule (e.g. "always use 2-space indent") |
instruction | A standing directive on how to work/behave (e.g. "never force-push to main") |
correction | A past mistake and the corrected approach |
preference | A softer preference, not a hard rule |
other | Catch-all for anything that doesn't fit but still got captured |
Each thought also gets a scope (a language/framework/topic, or "global") and 1–3 topic tags for filtering. This is deliberately narrow — it's not a general note-taking store — but the taxonomy isn't hardcoded logic, it's just the wording of the tool description and its zod schema in src/server.js. Retuning what counts as a convention vs. an instruction, adding a new type, or changing what "scope" means is a matter of editing that description text, not restructuring the code. The one wrinkle: the five type names are also referenced in the type filter's enum in list_thoughts (src/server.js) — if you rename or add a type, update that enum too or the new type will get rejected as a filter value. Everything's stored as a JSON blob column, so none of this needs a schema migration.
Separately, every thought gets a project field — null by default (applies everywhere), or a specific project name if it's obviously scoped to one codebase. The calling agent only judges whether it's project-specific; the actual project name is derived deterministically from the current git repo's directory name, not guessed by the model — so retrieval can do an exact match instead of fuzzy text comparison.
better-sqlite3) + sqlite-vec for native vector search, FTS5 for keyword search, combined via reciprocal rank fusion. One file, no server, no daemon.Xenova/bge-small-en-v1.5 (384-dim, quantized, ~130MB). Loads lazily on first use, no network call, no GPU needed.Two ways to get this: a git checkout (if you want to read/modify the source) or the npm package (if you just want it running).
Git checkout:
npm install
npm run init-db # creates data/memory.db
npm package:
npm install -g conventions-mcp
conventions-mcp init-db # creates ~/.conventions-mcp/memory.db
Nothing to configure — there's no API key and no external service. MEMORY_DB_PATH is the only environment variable this reads, and it's optional (see .env.example).
Register at user scope so it's available in every project, not just one repo — use the claude mcp add CLI, not a hand-edited config file:
# Git checkout — an absolute path, since Claude Code may spawn this from an
# arbitrary working directory:
claude mcp add --scope user conventions -- node /absolute/path/to/conventions-mcp/src/server.js
# npm package — already on PATH:
claude mcp add --scope user conventions -- conventions-mcp
Either way, this writes to ~/.claude.json's mcpServers key, which is what the CLI actually reads; a mcpServers entry placed directly in ~/.claude/settings.json is silently inert. Verify with claude mcp list. A new Claude Code session is required to pick up a newly-registered server.
Two hooks in ~/.claude/settings.json make retrieval deterministic every session and every turn:
{
"hooks": {
"SessionStart": [
{ "hooks": [{ "type": "command", "command": "node /absolute/path/to/conventions-mcp/bin/session-rules.js", "timeout": 15 }] }
],
"UserPromptSubmit": [
{ "hooks": [{ "type": "command", "command": "node /absolute/path/to/conventions-mcp/bin/prompt-reminder.js", "timeout": 5 }] }
]
}
}
bin/session-rules.js loads every global + current-project rule into context before the model's first action each session.bin/prompt-reminder.js fires on every turn, re-anchoring "keep following the loaded rules" and "capture this if it's a new one" — there's no hook event for "the user just stated a rule," since that's a semantic judgment only the model can make.Both read the current project from the session's working directory, so they work correctly regardless of where the conventions-mcp install itself lives on disk.
npm package: the scripts live inside the global install rather than a known clone path — resolve it first with npm root -g, then point the hook at $(npm root -g)/conventions-mcp/bin/session-rules.js the same way.
Windows: point the command at the .cmd wrapper instead of the .js file directly (no node prefix — the batch file invokes it) — bin\session-rules.cmd / bin\prompt-reminder.cmd for a git checkout, or the equivalent path under npm root -g for the npm package.
| Tool | Description |
|---|---|
capture_thought | Save a convention, instruction, correction, or preference. Embeds locally; classification is provided by the calling agent. |
update_thought | Correct/refine an existing thought in place — same id, re-embedded and re-tagged from the new content. |
search_thoughts | Hybrid semantic + keyword search. |
list_thoughts | List recent captures, optionally filtered by type or time range. |
list_rules | Every global + current-project rule in one deterministic call — no embeddings, no ranking. What the hooks use under the hood. |
thought_stats | Totals, type breakdown, top topics, and scopes. |
delete_thought | Permanently delete a thought by id. |
capture_thought gets called once per rule, each relayed individually — not merged into one capture or summarized together.data/memory.db in a git checkout, or ~/.conventions-mcp/memory.db for the npm package (override either with MEMORY_DB_PATH). It's gitignored — back it up yourself if you care about it (it's just a SQLite file; cp is a complete backup).MODEL_NAME in src/embeddings.js — but re-embed existing thoughts if the new model's vector space isn't compatible with the old one (different models' embeddings aren't comparable, even at the same dimension).Be the first to review this server!
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