Scribe MCP server, tooling, and universal CLI
Project description
Scribe MCP
Scribe MCP is the accountability layer for agent-driven engineering work.
It gives your agents a durable audit trail, governed engineering documents, and repo-safe tool contracts so plans, edits, and verification do not disappear into chat history or terminal scrollback.
Scribe is strongest when you want three things at the same time:
- a project-scoped execution record you can query later
- managed docs that stay tied to the work instead of drifting away from it
- MCP-safe read/search/edit primitives that are easier to automate than ad hoc shell mutations
If you want the fast product tour first, start here:
Why teams reach for Scribe
Without Scribe, agent-heavy work tends to fragment:
- plans live in one place, edits happen somewhere else, and rationale disappears
- project docs become stale snapshots instead of active engineering artifacts
- logs exist, but they are too noisy or too unstructured to explain what actually happened
- automation can touch files, but it is harder to keep the changes reviewable and reproducible
Scribe turns that into a tighter loop:
- bind a project and repo root
- generate or manage the project docs
- log meaningful actions as the work happens
- query the resulting history later by project, status, message, or scope
What Scribe gives you
- project and session binding with explicit repo scope
- governed docs such as
ARCHITECTURE_GUIDE.md,PHASE_PLAN.md, andCHECKLIST.md - audit logs such as
PROGRESS_LOG.md,DOC_LOG.md,SECURITY_LOG.md, andBUG_LOG.md - MCP tools for reading, searching, editing, and logging without leaving the repo boundary
- a Postgres-first runtime for shared use, plus explicit standalone SQLite for local-only workflows
- CLI helpers for bootstrap, MCP server startup, migrations, backups, metrics, and Codex projection
What makes Scribe different
The pitch is simple: keep the work record, the docs, and the repo operations in one system. The reason it holds up in practice is the machinery underneath it.
- Scribe keeps a project registry with lifecycle and hygiene metadata, not just loose markdown files.
- It tracks doc readiness and drift using stored hashes, last-update timestamps, and advisory flags such as
doc_drift_suspected. - It computes activity signals like
days_since_last_entry,days_since_last_access,staleness_level, andactivity_scoreso projects become queryable operational objects instead of folders you have to inspect manually. - Managed docs are anchored with stable section IDs like
<!-- ID: problem_statement -->, which is what makes laterreplace_sectionand checkliststatus_updateoperations deterministic. - The bootstrap and migration toolchain is deeper than most projects in this space:
scribe-bootstrap-postgres,scribe-migrate,scribe-migrate-postgres,scribe-migrate-objects,scribe-backup-postgres,scribe-metrics-postgres, andscribe-soak-postgresall ship in the package.
Five-minute quickstart
Install the package:
pip install scribe-mcp
Sanity-check the shipped commands:
scribe --help
scribe bootstrap --help
scribe-server --help
Recommended: bootstrap Postgres
Use the guided bootstrap first. It is the best default path for a real installation.
scribe bootstrap
The bootstrap flow is designed to handle the annoying setup work for you:
- create or update database roles
- provision the Scribe app database
- apply schema grants
- write or update the runtime keys you need in
.env
After bootstrap, load the environment and start the server:
set -a
source .env
set +a
scribe-server
Just want to try Scribe locally?
You can use explicit standalone SQLite for a local-only demo or one-user workflow:
export SCRIBE_MODE=standalone
export SCRIBE_STORAGE_BACKEND=sqlite
export SCRIBE_DB_PATH=".scribe/state/scribe.db"
scribe-server
Create your first governed project scaffold
Once your runtime is configured, bind a project:
scribe call set_project \
--agent demo-agent \
--repo-root "$PWD" \
--arg name=demo_docs \
--arg root="$PWD" \
--arg format=structured \
--pretty
On a fresh project, that one call can generate the core scaffold:
.scribe/docs/dev_plans/demo_docs/
ARCHITECTURE_GUIDE.md
PHASE_PLAN.md
CHECKLIST.md
PROGRESS_LOG.md
DOC_LOG.md
SECURITY_LOG.md
BUG_LOG.md
That scaffold is the point. Scribe is not just starting a server. It is creating a project surface that later MCP calls can work against.
Downstream customization now lives in the repo
On first bind/bootstrap, Scribe also seeds the downstream customization surface under .scribe/ so each repo can own its local scaffolding and settings without forking the library:
.scribe/
config/
scribe.yaml
seed_registry.json
templates/
documents/
ARCHITECTURE_GUIDE_TEMPLATE.md
PHASE_PLAN_TEMPLATE.md
CHECKLIST_TEMPLATE.md
PROGRESS_LOG_TEMPLATE.md
DOC_LOG_TEMPLATE.md
SECURITY_LOG_TEMPLATE.md
BUG_LOG_TEMPLATE.md
.env.example
That seeded surface is live, not decorative:
generate_doc_templatesand template-driven doc flows now resolve repo-local.scribe/templates/first- repo-local seeded files are tracked in
.scribe/config/seed_registry.jsonso refreshes can update untouched files without clobbering customized ones .scribe/.env.exampleis a discovery artifact only; runtime never auto-loads it
The ownership split is intentional:
- shared infrastructure defaults such as
SCRIBE_DB_URL, backend mode, and pool settings belong in user/global config by default - repo-specific runtime overrides belong in repo root
.env - repo-scoped structured behavior belongs in
.scribe/config/scribe.yaml
The user/global config home resolves in this order:
SCRIBE_CONFIG_DIRXDG_CONFIG_HOME/scribe_mcp~/.config/scribe_mcp
Inside that directory, use:
runtime.envfor shared env-backed defaults across reposscribe.yamlfor user-level structured defaults such as display preferences
That means you do not need to restate DB credentials in every downstream Scribe project just to make the runtime work.
The tour walks through that loop in more detail:
If you want to see the registry surface after that first bind, Scribe also exposes project inventory and health-oriented views through tools such as list_projects and managed-doc project_health.
How governed docs work
Generated docs are not just blank markdown files. They are structured scaffolds designed for later managed updates.
Example excerpt from a generated ARCHITECTURE_GUIDE.md:
## 1. Problem Statement
<!-- ID: problem_statement -->
...
## 3. Architecture Overview
<!-- ID: architecture_overview -->
...
Those stable anchor IDs are what let Scribe patch sections deterministically later through managed doc operations instead of relying on brittle freeform edits.
Example excerpt from a generated CHECKLIST.md:
## Phase 0
<!-- ID: phase_0 -->
- [ ] Add package-specific acceptance item with expected verification command
The value is not just that Scribe writes docs. It writes docs that agents and operators can update without turning them into mush.
The project registry and drift story
Scribe does more than remember that a project exists.
The runtime keeps registry-backed metadata for each project, including:
- lifecycle timestamps such as
created_at,last_entry_at, andlast_access_at - activity signals such as
days_since_last_entry,days_since_last_access,staleness_level, andactivity_score - doc hygiene metadata such as
baseline_hashes,current_hashes,doc_drift_days_since_update,drift_score, anddoc_drift_suspected
This is the useful part: Scribe is not just storing docs and logs side by side. It keeps enough structured state to warn when active work has outpaced the planning docs.
If you want the template-side view of those fields, start with:
The .scribe/ working surface
Once Scribe is active, your repo grows a real working surface under .scribe/. Depending on runtime mode and the tools you use, that can include:
.scribe/
.env.example
config/
scribe.yaml
seed_registry.json
templates/
documents/
ARCHITECTURE_GUIDE_TEMPLATE.md
PHASE_PLAN_TEMPLATE.md
CHECKLIST_TEMPLATE.md
PROGRESS_LOG_TEMPLATE.md
DOC_LOG_TEMPLATE.md
SECURITY_LOG_TEMPLATE.md
BUG_LOG_TEMPLATE.md
state/
vectors/
backups/
sentinel/
cli/
docs/
agent_report_cards/
dev_plans/<project>/
ARCHITECTURE_GUIDE.md
PHASE_PLAN.md
CHECKLIST.md
PROGRESS_LOG.md
DOC_LOG.md
SECURITY_LOG.md
BUG_LOG.md
TOOL_LOG.jsonl
That layout is part of the product story. Scribe gives agents and operators a durable project memory layer inside the repo boundary instead of scattering evidence across chat threads, shell history, and CI logs.
The important new bit is that .scribe/templates/ and .scribe/config/ are now first-class downstream surfaces. Customize templates there when you want repo-specific scaffolds, keep repo behavior in .scribe/config/scribe.yaml, keep repo-specific env overrides in repo root .env, and keep shared cross-repo runtime defaults in the user/global config home.
Run Scribe as an MCP server
For MCP hosts such as Codex or Claude-compatible setups, the usual entry point is:
scribe-server
Generic mcp.json example:
{
"mcpServers": {
"scribe": {
"command": "scribe-server",
"env": {
"SCRIBE_STORAGE_BACKEND": "postgres",
"SCRIBE_DB_URL": "postgresql://scribe_app:pass@127.0.0.1:5432/scribe"
}
}
}
}
Codex-specific guidance lives here:
The repo also ships bundled plugin assets for Codex and Claude under plugins/, so the MCP server surface is not the only integration story.
Documentation map
Start with these:
- Install and Bootstrap
The canonical install guide, including Postgres bootstrap, standalone mode, and Codex projection. - Tour: Scribe as an MCP product
A short MCP-first walkthrough with verified live response shapes. - Scribe Usage Guide
The day-to-day operating loop and the tool families you will actually use. - MCP Server Guide
How to connect Scribe to Codex or other MCP hosts.
Reference and release docs:
- Compatibility matrix
- Release surface
- Release file map
- Remote client contract
- Template variables reference
- Bridge development
- Global deployment guide
- Deployment README
- Scribe MCP whitepaper
Examples:
Who Scribe is for
Scribe is a strong fit if you are:
- building with MCP-hosted agents and want better operational memory
- running multi-agent engineering workflows that need a durable trail
- trying to keep specs, plans, and checklists attached to implementation reality
- tired of reconstructing "why did the agent do this?" from scattered logs
What still needs work
There is still one obvious next step:
- a clean-room walkthrough of the full pip-installed path from bootstrap to first live MCP host integration
That work matters because the public install story should be proven end-to-end, not implied.
License
See LICENSE.
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