ATP Librarian MCP Server
Click on "Deploy 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., "@ATP Librarian MCP ServerClaim the next ready task for agent bob"
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.
ATP Librarian MCP Server
This server enforces the Agent Task Protocol (.atp.json) contract so worker agents can claim, finish, and decompose tasks without corrupting the dependency graph. It exposes MCP tools that wrap deterministic updates to the plan file on disk.
Quick start (no clone, uvx)
Add to your MCP client config and update the plan path:
[mcp_servers.atp]
command = "uvx"
args = ["--from", "git+https://github.com/Edgeworthless/atp-mcp-server", "atp-server"]
env = { "ATP_FILE" = "/path/to/your/.atp.json", "ATP_LEASE_SECONDS" = "600" }uvx downloads/builds on the fly. Tools still require plan_path per call; ATP_FILE is just a default.
Related MCP server: delega-mcp
Local install
pip install -e .Run:
python3 main.pyConfigure your MCP client to call that command, and pass
plan_pathon every tool call.
Docker (optional)
Build or pull, then mount your plan file:
docker build -t atp-librarian .
docker run --rm -v /path/to/.atp.json:/data/.atp.json -e ATP_FILE=/data/.atp.json atp-librarianEnvironment knobs
ATP_FILE: path to the ATP plan file (default.atp.json).ATP_LOCK_FILE: override the lock file path (defaults to<ATP_FILE>.lock).ATP_LEASE_SECONDS: duration before a claimed task is released (default 600s).Tools accept
plan_pathdirectly; the env vars mainly backstop the status resource or when clients omit the argument.
Available tools
atp_claim_task(plan_path, agent_id): Assigns the highest-priority READY node, recovers zombies, and returns dependency context. Re-enters an existing claim for the same agent.atp_complete_task(plan_path, node_id, report, artifacts=[], status="DONE"): Marks a task DONE or FAILED, clears the lease, and unlocks children whose dependencies are satisfied.atp_decompose_task(plan_path, parent_id, subtasks): Converts a task into a SCOPE with a new subgraph. Subtasks must form a DAG; start nodes inherit the parent’s original dependencies. The parent closes automatically once all new tasks complete.atp_apply_future_patch(plan_path, expected_graph_version, patch, reason, actor_id): Applies bounded future-graph mutations for ATP v1.4 adaptive judge flows. Apply is rejected unless claimed-node count is zero, the graph version matches, terminal/claimed nodes remain untouched, and the candidate graph stays valid.atp_read_graph(plan_path, view_mode="full" | "local", node_id=None): Returns the full JSON graph or a neighborhood view aroundnode_id.
Resource
atp://status/summary: Plaintext dashboard with project status, live claims, and ready tasks for the default plan (ATP_FILEor.atp.jsonin the current working directory).
Graph contract
Keep the plan as a valid task DAG: each node has
title,instruction,dependencies,status(LOCKED|READY|CLAIMED|COMPLETED|FAILED), plus optionalcontext,reasoning_effort(minimal|low|medium|high|xhigh),artifacts,report, timestamps.Node statuses:
LOCKED(waiting),READY,CLAIMED,COMPLETED,FAILED. The server will auto-release CLAIMED tasks after the lease window and auto-complete SCOPE nodes produced byatp_decompose_taskonce allscope_childrenfinish.atp_read_graph(..., view_mode="full")includes runtimemeta.graph_versionin its response payload for optimistic concurrency in adaptive judge flows.ATP v1.4 future-graph mutation is additive. Closed/superseded future nodes are tracked via optional metadata and are not claimable or counted as READY work.
Dependency integrity is checked on every read/write; missing node references will raise errors instead of silently mutating the file.
Testing
python3 -m compileall main.py
MCP client config example
Example JSON config (Codex-compatible):
{
"mcpServers": {
"atp-librarian": {
"command": "python3",
"args": ["/path/to/atp-mcp-server/main.py"],
"cwd": "/path/to/atp-mcp-server",
"env": {
"ATP_FILE": "/path/to/your/project/.atp.json",
"ATP_LEASE_SECONDS": "600"
}
}
}
}Tool calls should still provide plan_path (e.g., /path/to/your/project/.atp.json); ATP_FILE just sets a default for the status resource or when a client omits the argument.
This server cannot be deployed
Maintenance
Related MCP Connectors
- projectsOAuthcloud.tri2b
Task tracking built for coding agents. Work is leased, so two agents never take the same SubTask.
Project management MCP for AI agents with safe task reads and writes.
Task management for teams building with AI agents. Agents claim tasks and report progress.
Shared task queue for humans and AI agents: leases, handoffs, approvals and signed receipts.
Related MCP Servers
- FlicenseNot gradedqualityDmaintenanceEnables AI agents to manage a file-backed ticketing system directly within a local repository using a structured state machine and directory hierarchy. It enforces strict markdown schemas and provides specialized tools for claiming tasks, appending work logs, and validating ticket metadata.-
- AlicenseBqualityAmaintenanceTask management API for AI agents. Create, delegate, and track tasks with agent identity, delegation chains, lifecycle webhooks, and persistent context.44392 npm1MIT
- AlicenseNot gradedqualityAmaintenanceServer-enforced workflow discipline for AI agents. An MCP server providing persistent work items, dependency graphs, quality gates, and actor attribution. Schemas define what agents must produce — the server blocks the call if they don't. Works with any MCP-compatible client.205MIT
- AlicenseNot gradedqualityCmaintenanceEnforces that tasks are created with criteria, claimed by one agent with evidence, and verified by a different agent, preventing self-verification and ensuring independent validation.5 npmMIT