pytest

MCP tool from Claude Swarm by cj-vana

, python -m pytest

How to use it

pytest is exposed by the Claude Swarm MCP server. Add the server to your MCP client (Claude Desktop, Cursor, Windsurf and others), and the pytest tool becomes available to the model automatically. See the full listing for setup details and every tool this server provides.

Install Claude Swarm

$claude mcp add claude-swarm --scope user -- node $(pwd)/dist/index.js

FULL CLAUDE SWARM LISTING

Other tools in Claude Swarm (39)

Add discovered work

auto_orchestrate

Hands-free orchestration until completion

check_all_workers

Check all active workers at once

Monitor review worker status

check_rollback_conflicts

Check for conflicts with other workers

Get worker output (supports heartbeat mode)

commit_progress

Create git checkpoint

configure_plan_mode

Enable/configure worker plan-before-implement mode

configure_ralph_loop

Enable/configure Ralph Loop execution defaults

configure_reviews

Set auto-review preferences

configure_verification

Set pre-completion verification commands

Compare plans and select winner

get_feature_complexity

Analyze complexity score

get_progress_log

View history (paginated)

get_review_results

Get aggregated findings (summary/detailed/json)

get_session_stats

Success rates and timing

get_worker_confidence

Get detailed confidence breakdown

implement_review_suggestions

Convert review findings into features

Mark feature done/failed (auto-retry)

orchestrator_init

Start session with task and features

orchestrator_reset

Clear state and kill all workers

orchestrator_status

Get current state (use after compaction)

Pause and stop all workers

Resume paused session (supports crash recovery)

Reset failed feature for retry

rollback_feature

Restore files changed by a worker

Manually trigger code/architecture reviews

run_verification

Run tests/build commands

send_worker_message

Send instructions to running worker

set_confidence_threshold

Configure alert threshold

set_dependencies

Define feature dependencies

Analyze repo freshness and missing configs

Initialize repo configuration with parallel workers

shutdown_session

Graceful shutdown with checkpoint

start_competitive_planning

Spawn 2 planners with different approaches

start_parallel_workers

Launch multiple workers simultaneously

start_ralph_loop

Launch worker with Ralph Loop (fresh context per iteration)

Launch worker for a feature

validate_workers

Pre-flight validation before parallel execution