
Task Graph Mcp
io.github.OortonautPublished by GitHub account Oortonautv0.3.0Updated Oct 25, 2026
MCP server for agent task workflows with phases, prompts, gates, and multi-agent coordination
Overview
A local MCP server that gives AI agents a shared SQLite task graph with phases, gates, dependencies and multi-agent coordination.
- What you can do
- Register and manage workers, workflows and overlays, including stale-worker cleanup and tag-based routing for agents working in parallel or in relay/hierarchical setups. Create, read, update, delete, rename and search tasks, including nested task trees, with full-text search across tasks and attachments. Link and unlink typed dependencies such as blocks, follows and contains, and claim tasks atomically with tag requirements. Track progress with live status broadcasts, transition history, token/cost/time metrics and inter-agent feedback. Coordinate file edits through advisory marks and change polling, and attach commits, diffs, notes or media to tasks. Check gates before transitions, read or,
- When to use it
- Use it when several AI agents or one agent working through phases need a durable, shared record of tasks, dependencies and ownership, and you want quality gates and handoff prompts enforced between steps. It suits local development workflows where a single SQLite file is enough and no database server should be run.
- Requirements
- Runs as a local process on the desktop; install via cargo or a pre-built binary for Linux, macOS or Windows and add it to the MCP client configuration. It stores data in a local SQLite file, configured through a config file or the TASK_GRAPH_CONFIG_PATH, TASK_GRAPH_DB_PATH, TASK_GRAPH_MEDIA_DIR and TASK_GRAPH_LOG_DIR environment variables. No authentication or external accounts are declared.
Installation
In SourceWeft
- Open Task Graph Mcp in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
Task Graph MCP Server
Agent task workflows that actually work.
When you have AI agents working on complex tasks, things go wrong fast. Agents lose context, skip steps, forget to coordinate. Task Graph solves this with structured workflows: phases to guide work, prompts for automatic guidance, gates to enforce quality, and coordination primitives for multi-agent scenarios—all through the Model Context Protocol.
Why Task Graph?
The problem: You've got complex tasks that need structured execution. Maybe a single agent working through phases, or multiple agents coordinating in parallel. Without proper workflows, agents lose track, skip steps, and produce inconsistent results.
What you get:
- Structured workflows — Phases (explore, implement, review, test) guide agents through work. Transition prompts provide automatic guidance at each step.
- Quality gates — Require tests to pass, code to be committed, or reviews to complete before transitions. Enforce your standards automatically.
- Ready-to-use topologies — Pre-built workflows for solo work, parallel swarms, specialist relays, or hierarchical delegation. Start immediately, customize later.
- Configurable workflows — Define your own states, phases, prompts, and gates. Match your process, not ours.
- Multi-agent coordination — Advisory file locks, DAG dependencies, atomic claiming. No more conflicts or duplicate work.
- Token-efficient — Designed for LLM context limits. Compact queries, minimal round-trips, structured outputs.
- Built-in accounting — Track tokens, cost, and time per task. Know exactly what your agents are spending.
- Zero infrastructure — SQLite with WAL mode. No database server to run. Just point at a file.
Features
Quick Start
Installation
From crates.io (Recommended)
Pre-built Binaries
Download the latest release for your platform from GitHub Releases:
Extract and place the binary in your PATH.
From Source
The binary will be at target/release/task-graph-mcp.
Usage
As an MCP Server
Add to your MCP client configuration:
CLI Options
Configuration
Full reference: See docs/CONFIGURATION.md for complete configuration documentation including workflows, prompts, gates, roles, and tags.
Create .task-graph/config.yaml:
States Configuration
Task states are configurable. Default states: pending, working, completed, failed, cancelled.
To add a ready state for auto-advance:
See SCHEMA.md for full documentation on state definitions.
Dependencies Configuration
Dependency types define how tasks relate to each other. Default types: blocks, follows, contains, duplicate, see-also, relates-to.
Attachments Configuration
Preconfigured attachment keys provide default MIME types and modes, reducing boilerplate when attaching common content types.
Built-in defaults:
Usage:
Environment variables:
TASK_GRAPH_CONFIG_PATH: Path to configuration file (takes precedence over.task-graph/config.yaml)TASK_GRAPH_DB_PATH: Database file path (fallback if no config file)TASK_GRAPH_MEDIA_DIR: Media directory for file attachments (fallback if no config file)TASK_GRAPH_LOG_DIR: Log directory path (fallback if no config file)
MCP Tools
Worker Management
Task CRUD
Task Claiming
Note: Release via update(status="pending"). Complete via update(status="completed"). Status changes auto-manage ownership.
Dependencies
Tracking
File Coordination
Attachments
Advanced
MCP Resources
Task Tree Structure
Create hierarchical tasks with create_tree:
Tree Node Fields
Top-Level Parameters
Referencing Existing Tasks
Use ref to integrate existing tasks into a tree structure:
Tag-Based Affinity
Workers declare capabilities via tags when connecting. Tasks can require specific tags to control which workers can claim them.
Example tag categories:
- Model capabilities:
image-in,audio-out,video-in,code,bulk - Access levels:
prod-access,admin,external - Specializations:
rust,python,frontend,database
Note: Roles like coordinator/reviewer/deployer are better represented using phases.
Task requirements:
needed_tags(AND): Agent must have ALL of thesewanted_tags(OR): Agent must have AT LEAST ONE
Workflows and Phases
Phases
Tasks can have a phase to categorize the type of work being performed:
Built-in phases: explore, implement, review, test, security, deploy, triage, diagnose, design, plan, doc, integrate, monitor, optimize
Phases enable:
- Transition prompts — Automatic guidance when entering/exiting phases
- Gates — Requirements that must be satisfied before phase transitions
- Role-based routing — In relay workflows, specialists own specific phases
Named Workflows
Pre-built workflow topologies optimize for different coordination patterns:
Select a workflow on connect:
Each workflow provides tailored prompts and coordination guidance. See WORKFLOW_TOPOLOGIES.md for detailed patterns.
Transition Prompts
Agents receive automatic guidance when status or phase changes:
Prompts support template variables: {{current_status}}, {{valid_exits}}, {{current_phase}}, {{valid_phases}}
Gates
Gates are requirements that must be satisfied before status or phase transitions:
Satisfy a gate by attaching evidence:
Enforcement levels: allow (advisory), warn (blocks unless force=true), reject (hard block)
File Coordination
Agents can coordinate file edits using advisory marks with change tracking:
Architecture
- Transport: Stdio — each worker spawns its own server process
- Database: SQLite with WAL mode for concurrent access across processes
- Deployment: Single binary, no external dependencies, works offline
Compared to Alternatives
Documentation
License
Apache 2.0
Built for AI agents that need structured workflows and reliable coordination.
Source: README.md at commit 75f71d6
Tools
0Version history
1- v0.3.0LatestSep 16, 2026
Score
50% of the applicable checks met
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- Reached in the last 30 days25 pts
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- Auth declared10 pts
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- Tools listed15 pts
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- DescriptionRequired15 pts
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