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SQLite Project Memory MCP

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SQLITE MCP

SQLite Project Memory MCP

SQLite-backed MCP server for storing project memory as a graph-friendly relational core.

The server is designed around four rules:

  1. Everything is an entity.
  2. Everything can relate to everything.
  3. State is authoritative.
  4. Narrative is separate from structure.

Instead of generating and maintaining many parallel documents, the MCP server stores project state in SQLite and exposes tools for safe access. Files such as todo.md or roadmap.md can be generated later on explicit request as views, not treated as the source of truth.

What It Stores

The schema supports project memory such as:

  • tasks
  • file metadata
  • dependencies
  • decisions
  • roadmap items
  • architecture elements
  • plans
  • notes
  • todos
  • reasoning records
  • snapshots and audit history

Everything is modeled through generic tables:

  • entities
  • attributes
  • relationships
  • content
  • events
  • snapshots
  • snapshot_entities
  • tags

The server also creates an FTS5 index for content.body when available.

Key MCP Tools

  • create_entity
  • upsert_entity
  • update_entity
  • get_entity
  • list_entities
  • find_similar_entities
  • resolve_entity_by_name
  • get_or_create_entity
  • upsert_attributes
  • set_tags
  • add_relationship
  • connect_entities
  • list_relationships
  • add_content
  • append_content
  • search_content
  • create_snapshot
  • get_snapshot
  • get_project_overview
  • get_project_state
  • get_open_tasks
  • get_decision_log
  • get_architecture_summary
  • get_recent_reasoning
  • get_dependency_view
  • get_recent_activity
  • get_database_health
  • prune_content_retention
  • get_entity_graph
  • bootstrap_project_memory
  • run_read_query
  • render_markdown_views with user_requested=true and a request reason
  • export_markdown_views with user_requested=true and a request reason
  • server_info

High-frequency summary tools also support a compact=true mode that returns an explicit schema envelope for stable machine consumption.get_recent_activity now supports limit, offset, and compact, and get_entity_graph uses explicit node and edge limits so large graph reads stay bounded.

Resources And Prompt

  • memory://schema
  • memory://overview
  • memory://recent-activity
  • entity://{entity_id}
  • prompt: project_memory_policy

Run

Option 1: pip

python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install -e .
python -m sqlite_mcp_server

Option 2: uv

uv venv
.\.venv\Scripts\Activate.ps1
uv pip install -e .
python -m sqlite_mcp_server

The default database path is data/project_memory.db under the repository root.

Admin CLI

For local bootstrap and inspection workflows, the package also exposes an admin CLI:

sqlite-project-memory-admin bootstrap-self --repo-root .
sqlite-project-memory-admin project-state
sqlite-project-memory-admin health
sqlite-project-memory-admin export-views --user-requested --request-reason "User asked for a roadmap export" --require-existing-dir exports todo roadmap architecture
sqlite-project-memory-admin export-views --user-requested --request-reason "User asked for refreshed generated docs" --force todo roadmap architecture
sqlite-project-memory-admin sync-document architecture --input-path architecture.md
sqlite-project-memory-admin sync-document decisions --input-path decisions.md
sqlite-project-memory-admin export-json --output-path exports/project_memory.snapshot.json
sqlite-project-memory-admin import-json --input-path exports/project_memory.snapshot.json

This is mainly useful when you want the project to use its own SQLite memory store without writing one-off scripts.Generated markdown export is locked by default: it refuses to render or write views unless the caller explicitly marks the request as user-requested and supplies a reason. It also refuses to overwrite existing view files unless --force is provided, and --require-existing-dir can be used when automation should fail instead of creating a new output directory.

Sample MCP Config

A repo-local sample MCP client configuration is available at .vscode/mcp.sample.json. Adjust the Python path if needed for another machine.

Configuration

Environment variables:

  • SQLITE_MCP_DB_PATH: override the SQLite database file path.
  • SQLITE_MCP_TRANSPORT: stdio or streamable-http.
  • SQLITE_MCP_EXPORT_DIR: default output directory for generated markdown views.
  • SQLITE_MCP_LOG_LEVEL: log level for server lifecycle and tool request logs. Defaults to INFO.
  • SQLITE_MCP_LOG_FORMAT: json or text for stderr logs. Defaults to json.

Example:

$env:SQLITE_MCP_DB_PATH = "D:\memory\project.db"
$env:SQLITE_MCP_TRANSPORT = "stdio"
python -m sqlite_mcp_server

Design Notes

  • Entity ids, relationship ids, tags, types, and attribute keys are validated.
  • Duplicate entities are prevented by primary key.
  • Duplicate edges are prevented by a unique constraint on (from_entity, to_entity, type).
  • Narrative content is stored separately from authoritative state.
  • Mutating operations record audit events.
  • Raw arbitrary SQL write access is intentionally not exposed through MCP tools.
  • A constrained read-only SQL tool is available for diagnostics and ad hoc retrieval.
  • Markdown files are treated as generated views, not storage.

AI-First Tooling Guidance

If this server is going to be called frequently by an AI, the useful surface is not a single RUN SQL tool. The practical surface is:

  • bootstrap_project_memory to initialize a project root and standard memory areas.
  • upsert_entity so the AI can write idempotently instead of guessing whether to create or update.
  • connect_entities so repeated graph writes do not produce duplicate edges.
  • append_content so narrative memory can be added without the AI having to mint content ids every time.
  • get_recent_activity so an AI can resume context quickly after a new session.
  • run_read_query for controlled read-only analytics when the built-in tools are not enough.
  • render_markdown_views and export_markdown_views only after the user explicitly asks for a human-readable todo, roadmap, plan, architecture, decisions, or notes document.

render_markdown_views and export_markdown_views are intentionally locked behind an explicit user-request contract so an AI does not casually generate markdown and then start using those files as a substitute for SQLite.

export_markdown_views also supports explicit overwrite control so generated documents do not silently replace existing files.

For the remaining human-facing documents, sync-document provides a structured migration path into the anchor memory areas for architecture, decisions, plan, and notes. The generated views then combine that synced document content with the structured SQLite state instead of rendering a flat dump.

Roadmap state is different: it is maintained directly through SQLite entities, attributes, relationships, and content. There is no supported roadmap.md import workflow anymore. If an AI needs to change roadmap state, it should use normal MCP write tools such as upsert_entity, append_content, set_tags, and connect_entities, then generate roadmap.md only when a user explicitly asks for that artifact.

The intended pattern is:

  1. Use explicit domain tools for writes.
  2. Use summary-first read tools such as get_project_state, get_open_tasks, get_decision_log, get_architecture_summary, get_recent_reasoning, and get_dependency_view before falling back to lower-level queries.get_recent_activity is also safe for resumptions because it paginates instead of returning an unbounded activity dump.
  3. Use run_read_query only for read-only inspection when the built-in summaries are not enough.
  4. Generate markdown views only when a user explicitly asks for a document, and pass that request through the MCP call.
  5. Keep SQLite authoritative.

For long-running AI usage, the hygiene tools matter as much as the write tools:

  • find_similar_entities helps avoid creating duplicate memory objects.
  • resolve_entity_by_name lets the AI reuse existing entities when a human-style name is all it has.
  • get_or_create_entity gives the AI a safer name-first workflow with stable id generation.
  • get_database_health reports duplicate candidates, invalid statuses, low-signal attributes, and retention pressure.
  • prune_content_retention provides a controlled cleanup path for high-volume reasoning and log content.

Suggested Modeling Conventions

  • Use stable ids such as task.auth-flow, file.src.server, decision.schema-graph-core.
  • Keep type broad and durable: task, file, module, decision, feature, plan, note.
  • Put volatile metadata in attributes, not in new tables.
  • Use content_type to distinguish note, spec, analysis, reasoning, log.
  • Use relationships deliberately: depends_on, implements, blocks, calls, owns.

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