keshro-mcp 0.1.7

The intelligent execution layer for AI agents — MCP server

Keshro MCP

Keshro as an MCP server. Same plans, tasks, and execution tracking — exposed as tool calls for any MCP-compatible agent.

pip install keshro-mcp

When to use this vs the CLI

Use the CLI (pip install keshro) for the full experience: interactive clarifying questions, migration detection, parallel execution in isolated worktrees, git checkpoints, cross-task context routing, and cost tracking.

Use MCP if your agent platform speaks MCP and you want direct tool-call access to Keshro plans and tasks.

The CLI gives you more control. MCP is more flexible for custom integrations.

Setup

Set your API token:

export KESHRO_API_TOKEN="ksh_pat_..."

Get one from keshro.com/account.

Connect to your agent

MCP works with any agent that supports the protocol — Claude Code, Cline, Continue, Zed, and others.

Claude Code — add to ~/.claude.json:

{
  "mcpServers": {
    "keshro": {
      "command": "keshro-mcp",
      "env": { "KESHRO_API_TOKEN": "ksh_pat_..." }
    }
  }
}

Other MCP clients — point your client at the keshro-mcp binary with KESHRO_API_TOKEN set in the environment. The server uses stdio transport.

Available tools

Tool	What it does
generate_plan	Generate a plan from a description using AI
list_plans	List all plans
get_plan	Get a plan with all tasks
plan_status	Progress summary (task counts, enrichment sources)
next_task	Get the next actionable task
create_plan	Create a plan manually
start_task	Mark a task as in progress
complete_task	Mark a task as done
block_task	Mark a task as blocked
unblock_task	Clear a blocker
append_task_note	Add a note to a task
add_task_artifact	Attach an artifact link
record_decision	Log a decision with context, choice, and reasoning
edit_task	Edit task title or description
push_to_tracker	Push tasks to Linear, Jira, or GitHub as issues
sync_pull	Pull status updates from connected issue tracker
export_project	Export project data

License

MIT