system-brain-mcp
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<img src="./assets/header.svg" alt="system-brain-mcp" width="100%">
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# system-brain-mcp
Eleven read-only MCP tools for inspecting a software workspace and returning answers tied
to observable evidence. Missing or degraded evidence produces a partial result or no
recommendation, never invented certainty.
## Start
```bash
npx -y -p system-brain-mcp system-brain-init /path/to/code
```
Or from source:
```bash
git clone https://github.com/QEbellavita/system-brain-mcp
cd system-brain-mcp
npm install
node bin/init.js /path/to/code
```
The initializer scans for repositories and deployment configuration, then writes a draft
manifest. It leaves uncertain fields unresolved for human review.
Point an MCP client at the server:
```json
{
"mcpServers": {
"system-brain": {
"command": "npx",
"args": ["-y", "system-brain-mcp"],
"env": {
"SYSTEM_BRAIN_DEPLOY_MANIFEST": "/path/to/deploy-targets.json",
"SYSTEM_BRAIN_FABRICATION_DIRS": "/path/to/source"
}
}
}
}
```
## Tools
| Tool | Purpose |
|---|---|
| `brain_where_deploys` | Compare declared and detected deployment evidence |
| `brain_fabrication_audit` | Flag narrow placeholder patterns for human verification |
| `brain_backlog` | Summarize accessible open work and local branches |
| `brain_db_schema` | Inspect configured local database structure |
| `brain_analytics` | Measure a configured outcome-labeling loop |
| `brain_ml_models` | Inventory configured local model artifacts |
| `brain_architecture` | Index configured architecture documents |
| `brain_lenses` | List available reasoning lenses |
| `brain_roadmap` | Find open checklist items in configured notes |
| `brain_recommend` | Produce evidence-linked candidate actions |
| `brain_reframe` | Apply a reasoning lens to one candidate action |
All tools are read-only. They do not write to databases, repositories, or deployment
targets.
## Configuration
| Variable | Purpose |
|---|---|
| `SYSTEM_BRAIN_DEPLOY_MANIFEST` | Path to a reviewed deployment manifest |
| `SYSTEM_BRAIN_FABRICATION_DIRS` | Directories eligible for static inspection |
| `SYSTEM_BRAIN_DB` | Local SQLite database path |
| `SYSTEM_BRAIN_MODELS_DIRS` | Directories containing model artifacts |
| `SYSTEM_BRAIN_ARCH_DOCS` | Architecture documents to index |
| `SYSTEM_BRAIN_OBSIDIAN_VAULTS` | Optional named vault paths |
| `SYSTEM_BRAIN_ANALYTICS_CONFIG` | Reviewed analytics configuration |
| `SYSTEM_BRAIN_TAXONOMY_CONFIG` | Reviewed work taxonomy |
The files under `config/` are neutral examples only. Copy them to user-specific files and
replace every example table, label, taxonomy, and threshold before interpreting analytics.
The project intentionally does not ship a production schema or recommended operating
threshold.
`brain_analytics` measures labels within the configured outcome ledger. Other activity
tables are reported separately so they cannot silently change that denominator. The tool
reports incomplete evidence when a required query fails.
`brain_fabrication_audit` returns candidate locations, not verdicts. Read the surrounding
implementation before classifying a capability.
## Tests
```bash
npm test
```
Tests use temporary files and injected command behavior. Network access and a database are
not required.
## Public-data boundary
Public fixtures are synthetic. Do not add customer data, real internal identifiers,
production table names, operating thresholds, incident details, or exact production-shaped
examples to this repository or its release archives.
## Licence
MIT. See [LICENSE](LICENSE).
TDQS
Scored across 11 tools
Each tool targets a distinct resource or action: deploys, backlog, DB schema, ML models, analytics, architecture docs, fabrication audit, lenses, roadmap, recommend, and reframe. Descriptions clearly separate related tools like db_schema vs. analytics and backlog vs. roadmap.
All tools share the 'brain_' prefix, creating a clear family, but the suffixes mix nouns (backlog, analytics, architecture), verbs (recommend, reframe), and query-like phrases (where_deploys). This is mostly consistent but not a strict verb_noun pattern.
11 tools is well within the ideal range for a decision-support brain. Each tool covers a discrete capability needed for the apparent domain, and none feel redundant or superfluous.
The tool surface covers the full gather-analyze-recommend-reframe loop using available evidence sources. Minor gaps exist—e.g., no direct GitHub issue/PR detail lookup beyond the backlog aggregate, and no tool for explicit doc searches—but agents can work around these.