subconscious-mcp

subconscious-mcp

Local-first semantic memory for MCP agents. Recall, remember, forget, echo, drift detection, stats over stdio.

The server runs as an MCP stdio process on your machine. It exposes six tools that let an agent ask "have I seen this task before?" and, if so, get the previous answer back in milliseconds without re-running the work. Inspired by bat echolocation: even a recall miss reports how close the nearest memory was, an echo tool senses nearby memories without retrieving answers, and every recall outcome is logged so drift_report can flag cached answers that are absorbing too broad a family of queries (first-fill semantic drift).

Embeddings come from sentence-transformers/all-MiniLM-L6-v2 (384-dim, runs on CPU). Storage is a persistent local ChromaDB collection. No data leaves your machine.

Install

# From PyPI:
pip install subconscious-mcp

# Local development:
git clone https://github.com/vishaltorc/subconscious-mcp
cd subconscious-mcp
pip install -e ".[dev]"

After install you can run the server from anywhere:

subconscious-mcp --help

The first time a tool is called, the embedding model (~80MB) is downloaded into the local Hugging Face cache. Subsequent starts are fast.

Configure your MCP client

Claude Desktop

Edit your config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Add an mcpServers entry:

{
  "mcpServers": {
    "subconscious-mcp": {
      "command": "subconscious-mcp",
      "args": []
    }
  }
}

If subconscious-mcp isn't on Claude Desktop's PATH, use the absolute path printed by which subconscious-mcp, e.g. "command": "/Users/you/.local/bin/subconscious-mcp".

Then quit and restart Claude Desktop. The new tools appear under the 🔌 indicator.

Claude Code

Option A. Register from the CLI (recommended):

claude mcp add subconscious-mcp -- subconscious-mcp

Option B. Edit ~/.claude.json (or your project's .mcp.json) and add:

{
  "mcpServers": {
    "subconscious-mcp": {
      "command": "subconscious-mcp",
      "args": [],
      "type": "stdio"
    }
  }
}

Reload the Claude Code session and the six tools become available.

A copy-pasteable file is in examples/claude_desktop_config.json and examples/claude_code_config.json.

Tools

recall(task, threshold=0.85, top_k=1)

Semantic search for a previously remembered task.

arg	type	default	meaning
task	str	(required)	the task description to look up
threshold	float	0.85	minimum cosine similarity for a hit
top_k	int	1	how many candidates to consider

Returns:

{
  "hit": true,
  "similarity": 0.91,
  "answer": "...",
  "task_text": "...",
  "entry_id": "uuid",
  "stored_at": 1731000000.0,
  "tags": ["..."]
}

On a miss, hit is false, answer is null, and similarity is the best similarity observed in top_k. Callers can see how close they came.

remember(task, answer, tags=[], ttl_seconds=null)

Persist a (task, answer) pair. Returns {stored, entry_id, embedding_dim}.

ttl_seconds=null means never expire. Pass an integer to have the entry filtered out of future recalls after that many seconds.

echo(task, top_k=5)

Sonar ping: return the nearest non-expired entries without their answers.

arg	type	default	meaning
task	str	(required)	the task description to ping with
top_k	int	5	how many nearest entries to report

Returns:

{
  "count": 47,
  "echoes": [
    {"entry_id": "uuid", "similarity": 0.91, "task_text": "...", "stored_at": 1731000000.0, "tags": ["..."]}
  ]
}

Use it to sense whether a task sits in known territory before committing to a recall. Because no answer is returned, an echo can never propagate a stale or wrong cached answer. Echo calls don't count toward the hit rate and aren't written to the echo log.

drift_report(min_hits=3, min_spread=0.08)

Analyze the echo log for first-fill semantic drift candidates: entries whose recall hits span a wide similarity band across distinct query phrasings. A wide band means one cached answer is absorbing a broad family of queries that may carry subtly different interpretations (see validation/results.md for the failure mode this detects).

arg	type	default	meaning
min_hits	int	3	minimum recorded hits before an entry is considered
min_spread	float	0.08	minimum (max - min) hit-similarity band to flag

Returns:

{
  "analyzed_recalls": 412,
  "entries_with_hits": 38,
  "candidates": [
    {
      "entry_id": "uuid",
      "task_text": "Pull out all digits...",
      "still_stored": true,
      "hits": 5,
      "distinct_queries": 4,
      "similarity_min": 0.82,
      "similarity_max": 0.94,
      "similarity_spread": 0.12
    }
  ]
}

Flagged entries are review candidates: forget them, split them into more specific entries, or raise the recall threshold for that family.

forget(entry_id)

Delete the entry with this id. Returns {"forgotten": true} if it existed, else false.

stats()

Returns {"total_entries", "last_hit_at", "hit_rate_last_100"}. hit_rate_last_100 is a sliding window over the most recent 100 recall calls. Useful to see whether memory is actually paying off.

Configuration

Configuration is resolved in priority order:

• Environment variables (highest)
• ~/.subconscious-mcp/config.json
• Built-in defaults

key	default	env var
storage_dir	~/.subconscious-mcp/data	SUBCONSCIOUS_STORAGE_DIR
embedding_model	all-MiniLM-L6-v2	SUBCONSCIOUS_EMBEDDING_MODEL
default_threshold	0.85	SUBCONSCIOUS_DEFAULT_THRESHOLD
default_ttl_seconds	null
log_level	INFO	SUBCONSCIOUS_LOG_LEVEL
echo_log_enabled	true	SUBCONSCIOUS_ECHO_LOG_ENABLED
echo_log_max_bytes	5000000	SUBCONSCIOUS_ECHO_LOG_MAX_BYTES

Inspect the resolved config without starting the server:

subconscious-mcp --print-config

Files written on disk

~/.subconscious-mcp/
├── config.json            (optional, user-edited)
├── data/                  ChromaDB collection (sqlite + parquet)
│   └── echo_log.jsonl     one line per recall: query, nearest entry, similarity, hit
└── logs/server.log        rotating, 2MB x 3 backups

The echo log self-compacts: when it exceeds echo_log_max_bytes (5MB default), the oldest half is dropped. Set SUBCONSCIOUS_ECHO_LOG_ENABLED=0 to disable it entirely (this also disables drift_report).

To wipe your memory (including the echo log): rm -rf ~/.subconscious-mcp/data.

Demo session

See examples/demo_session.md for a worked example of an agent calling recall (miss, then remember), then on a later turn calling recall again with a paraphrase and getting a hit.

Architecture

See docs/architecture.md for the layered design (server / tools / memory / config), the rationale behind ChromaDB + cosine similarity, and the TTL strategy.

Troubleshooting

subconscious-mcp: command not found after install Your shell's PATH doesn't include the install location. Try python -m subconscious_mcp.server --help to confirm the package works, then use the absolute path in your MCP client config.

Claude Desktop says "Server disconnected" Check ~/.subconscious-mcp/logs/server.log for the traceback. Most common causes:

• The model download failed (offline at first launch). Re-run with network connectivity.
• The storage_dir is on a read-only volume.

First recall is slow The first invocation lazily loads the sentence-transformer model (~5s on a modest CPU). Subsequent calls reuse the loaded model and respond in milliseconds.

drift_report returns no candidates Either too few recalls have been logged (each candidate needs min_hits hits from at least two distinct phrasings), or the echo log is disabled (SUBCONSCIOUS_ECHO_LOG_ENABLED=0). Check that ~/.subconscious-mcp/data/echo_log.jsonl exists and is growing.

Recall keeps missing on obvious paraphrases Lower the threshold (recall(task=..., threshold=0.7)) or raise top_k to see candidates. all-MiniLM-L6-v2 is small and fast. For higher-quality matching set SUBCONSCIOUS_EMBEDDING_MODEL=all-mpnet-base-v2.

Tests fail with a sentence-transformers download error You're offline or behind a proxy. Set HF_HUB_OFFLINE=1 once you've pre-downloaded the model, or run python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('all-MiniLM-L6-v2')" once with connectivity.

License

MIT © 2026 Vishal Jayaprakash