BRI
BRI exposes video analysis tools through a FastAPI MCP server, with SQLite job storage and a Streamlit interface.
Project details
- Classification
- Open-source project
- Repository
- Alexi5000/Bri
- Language
- Python
- License
- MIT
- Status
- Active development
- Tests
- 3 test notes
- Last verified
- 2026-08-03
What it is
I use BRI as an example of exposing video analysis tools through MCP. Whisper and YOLO tooling sit behind a FastAPI MCP server, SQLite stores job state, and Streamlit provides the interface. The README and workflow files describe setup and checks.
Architecture
- FastAPI MCP server publishes the analysis tools with typed inputs.
- SQLite provides durable job and result state without an external database dependency.
- Separate Docker images for the MCP server and the UI so the tool surface can be deployed without the operator app.
- Pre-commit hooks and a Makefile define local checks and run commands.
Tests and gates
- Four workflows: ci, codeql, docs, and release.
- Pre-commit configuration and markdownlint run over the repository.
- GOVERNANCE.md, MAINTAINERS.md, and SECURITY.md define ownership and disclosure.
Limits
- Results depend on the underlying models. The surrounding tools do not establish their accuracy.
- SQLite durability suits single-node deployments; it is not a multi-writer data plane.
- The MCP surface is unauthenticated by default and expects to run behind a trusted boundary.
Source files and examples
Related methods
Acceptance tests for agent work →
Executable checks test the task and detect regressions.
Evaluation tests for releases →
A fixed evaluation suite checks release requirements.
MCP tool permissions →
The server checks which tools a caller may use.
Recording agent runs →
A shared trace ID connects the records of a run.
Spending limits for agent runs →
Budget checks run before provider requests.
Pausing for human approval →
The run waits for approval before the action executes.
Related projects
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ClawKeeper checks proposed Claude agent actions against explicit rules before a model call. Autonomous spend defaults to zero, and the database enforces an append-only audit log.
TechTide Harness Kit →
TechTide Harness Kit uses JSON Schema to declare skill permissions, risk tiers, network access, and approval requirements. Automated checks validate declarations and run prompt-injection fixtures.
CipherClaw →
CipherClaw analyzes recorded agent activity without model calls. It builds causal graphs, scores possible causes, and flags failure patterns.
Swarm 357 →
357 Claude agent roles across six business layers, with portable memory, enforced cost caps, a bash policy gate, and durable checkpoints.
I document the project here. TechTide AI publishes current service scope, prices, and terms.
Systems Decision Audit at TechTide AI→© 2026 Alex Cinovoj · TechTide AI · Columbus, OH