squad-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@squad-mcpreview recent changes in auth.py and suggest tests"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
squad-mcp
An MCP server that reviews your code changes with four specialist agents running in parallel — for a fraction of the cost of doing it naively.
The problem
Ask an AI coding agent to "review this PR, write tests, update the docs, and check for risky changes" and it usually does these one at a time — reading the whole codebase again for each step. That's slow and it burns a lot of tokens (which means it costs more).
Related MCP server: mcp-agent-review
What squad-mcp does
You call one tool, dev_squad_process, with a repo path and a task description. Behind the scenes:
It fetches the relevant context once (git diff + architecture summary) instead of letting every agent re-read the codebase separately.
A cheap model decides which specialists are actually needed — no point running a Test Agent on a comment-only change.
The specialists that are needed (Docs, Test, Refactor, Review) run in parallel, each using the model that fits the job — a fast/cheap model for simple tasks, a stronger one for anything that needs real judgment.
Everything comes back as one combined report with a diff you can apply, plus a cost breakdown.
It doesn't scan your codebase from scratch — it queries an existing indexed code graph (codebase-memory-mcp) for just the structure it needs. squad-mcp doesn't reimplement that part; it builds the orchestration and cost-optimization layer on top of it.
Example
Input (calling dev_squad_process from Claude Code or any MCP client):
{
"repo_path": "/Users/user/projects/my-python-app",
"task_description": "Review the recent changes in core/auth.py, suggest unit tests, and check for refactor opportunities.",
"target": "staged"
}Output (trimmed):
# squad-mcp — Pipeline Report
**Total cost:** $0.0244 | **Estimated savings:** 83.5% vs. running everything on Sonnet without caching
**Duration:** 3.42s
## Docs Agent (claude-haiku-4-5-20251001)
Added a missing docstring to `authenticate_user`.
```diff
def authenticate_user(username, password):
+ """Authenticates a user against the database."""
return db.query(username, password)
```
## Test Agent (claude-haiku-4-5-20251001)
Added edge-case tests (empty password, SQL injection input).
## Refactor Agent (claude-sonnet-5)
Found a timing-attack risk in the password comparison — suggested `secrets.compare_digest`.
## Review Agent
Skipped — triage determined this change carries no architectural risk.How it's put together
graph TD
User([Claude Code / User]) -->|dev_squad_process| Server[squad-mcp]
Server --> Fetcher[Context Fetcher]
Fetcher -->|one query| Graph[codebase-memory-mcp]
Fetcher --> Triage{Triage}
Triage -->|skip what's not needed| Router[Model Router]
Router --> Docs[Docs Agent]
Router --> Test[Test Agent]
Router --> Refactor[Refactor Agent]
Router --> Review[Review Agent]
Docs & Test & Refactor & Review --> Report[Combined report + cost log]
Report --> UserSetup
# 1. Install codebase-memory-mcp (squad-mcp depends on it)
curl -fsSL https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/install.sh | bash
# 2. Clone and install squad-mcp
git clone https://github.com/MustafaBozann/squad-mcp.git
cd squad-mcp
python -m venv .venv
.venv\Scripts\activate # Windows
# source .venv/bin/activate # macOS/Linux
pip install -e .[dev]
# 3. Add your API key
copy .env.example .env # Windows
# cp .env.example .env # macOS/Linux
# then fill in ANTHROPIC_API_KEYAdd it to Claude Desktop / Claude Code by putting this in your MCP config:
{
"mcpServers": {
"squad-mcp": {
"command": "C:\\path\\to\\squad-mcp\\.venv\\Scripts\\python.exe",
"args": ["C:\\path\\to\\squad-mcp\\squad_mcp\\mcp_server.py"]
}
}
}Does it actually save money?
The numbers below are from a simulated benchmark (benchmarks/run_benchmark.py). Run it yourself with --real (and a real API key) to get measured numbers on your own repo instead of trusting mine:
Scenario | Naive (tokens / cost) | squad-mcp (tokens / cost) | Reduction |
Small PR (2 files) | 11,600 / $0.054 | 5,800 / $0.010 | 80.8% |
Medium PR (10 files) | 63,200 / $0.228 | 47,400 / $0.036 | 84.2% |
Large PR (30+ files) | 226,000 / $0.750 | 226,000 / $0.138 | 81.5% |
How this compares
CodeRabbit | Qodo | squad-mcp | |
Code graph context | No (diff-only) | Partial | Yes |
Parallel specialist agents | No | No | Yes |
Fully local mode (Ollama) | No | No | Yes |
Per-run cost breakdown | No | No | Yes |
Language support (v1) | Multiple | Multiple | Python only |
Known limitations
v1 only handles Python repositories.
The Test Agent proposes tests, it doesn't run them yet.
The GitHub Action in
examples/is a template, not a published reusable action.Benchmarks are simulated by default — see
--realabove.
Built with
The full build log (what was built in what order, and why) is in docs/DEVELOPMENT.md.
License
MIT — see LICENSE.
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