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AgentTakt demo: drag nodes, draw a dependency edge, approve

AgentTakt is an MCP (Model Context Protocol) server and TUI tool. When an AI agent (an "Executor" such as Claude Code) sends a task execution plan over MCP, AgentTakt renders it as a node graph in your terminal. You review it with mouse and keyboard — move, add, and delete nodes, draw dependency edges, edit parameters — then approve, and the edited plan JSON is returned to the Executor for execution.

Claude Code (Executor)
   │ stdio (MCP)                        your other terminal
   ▼                                           │
[agenttakt serve] ── Unix domain socket ──▶ [agenttakt (TUI)]
 MCP server                               review / edit / approve

Features

  • Terminal-native — no web UI; everything runs inside your terminal

  • Visual node editor — rounded nodes, dependency edges, and per-type coloring, powered by Textual

  • Mouse-first editing — drag nodes to move them, draw edges between ports (rubber band), click to select and delete

  • Safe approval loop — cycle detection (DAG guarantee) and other validations at the entry point, returning errors the agent can self-correct

Related MCP server: textual-mcp-server

Requirements

  • Python 3.10+ (recommended: uv)

  • A terminal emulator with mouse reporting (iTerm2, WezTerm, kitty, Ghostty, ...)

Installation

If you have uv, no installation is needed. uvx agenttakt fetches and runs AgentTakt on demand, and the .mcp.json example below starts the MCP server the same way.

If you don't have uv, install AgentTakt once:

brew install ryoohshima/tap/agenttakt    # Homebrew
pipx install agenttakt                   # pipx

Installing is also handy for everyday use even with uv — you start the TUI by hand, so plain agenttakt beats typing uvx agenttakt each time:

uv tool install agenttakt

Quick Start

AgentTakt runs as two processes: the MCP server, which Claude Code starts for you, and the TUI, which you start yourself in a separate terminal. The TUI is what displays the plan, so start it before asking the Executor for approval.

┌─ Terminal A: you ───────────────────┐   ┌─ Terminal B: Claude Code ───────────┐
│ $ uvx agenttakt                     │   │ $ claude                            │
│                                     │   │                                     │
│   ╭─ grep ───╮                      │   │ > Plan the refactor, then ask       │
│   │ pattern  │───╮                  │   │   me to approve it                  │
│   ╰──────────╯   │                  │   │                                     │
│             ╭────▼─────╮            │   │   calls request_approval(plan)      │
│             │   edit   │            │   │   waiting for approval...           │
│             ╰──────────╯            │   │   (blocked until you decide)        │
│                                     │   │                                     │
│   [a] Approve   [r] Reject          │   │                                     │
└─────────────────────────────────────┘   └─────────────────────────────────────┘
             ▲                                                    │
             ╰──────────────── Unix domain socket ────────────────╯

Running the TUI in the same session as Claude Code does not work. A stdio MCP server has its standard input and output reserved for protocol traffic, so the same process cannot also drive a full-screen terminal UI. That is why the two halves are separate processes talking over a Unix domain socket.

1. Start the TUI (in its own terminal)

uvx agenttakt           # if installed: agenttakt (short alias: agt)

An idle screen appears, waiting for plans from the Executor. Leave this terminal open. If no TUI is running when the Executor calls request_approval, the call fails with:

AgentTakt editor is not running. Ask the user to run "agenttakt" in a separate terminal, then call request_approval again.

On startup the TUI checks PyPI in the background and shows a notification when a newer version is available. Set AGENTTAKT_NO_UPDATE_CHECK=1 to disable the check.

2. Register the MCP server with the Executor (Claude Code)

Add the following to your project's .mcp.json:

{
  "mcpServers": {
    "agenttakt": {
      "command": "uvx",
      "args": ["agenttakt", "serve"],
      "timeout": 1800000
    }
  }
}
IMPORTANT

Setting timeout (milliseconds) explicitly is required. The request_approval tool blocks until the human finishes reviewing. MCP progress notifications do not extend client-side timeouts, so the default would cut the request off before approval. The example above sets 30 minutes (1800000). This does not apply to show_plan, which returns as soon as the TUI receives the plan.

3. Request approval from the Executor

When the Executor calls the MCP tool request_approval(plan, summary), the plan appears in the TUI as a node graph. Once the human edits and approves (or rejects) it, the result is returned as:

{ "status": "approved", "plan": { "...edited plan..." }, "reason": null }

See docs/schema.md for the plan JSON format and what to write in each node.

Display-only plans (show_plan)

show_plan(plan, summary) shows a plan in the TUI without waiting for approval — it returns {"status": "displayed"} as soon as the editor receives it. Use it when you just want visibility into what the agent is planning, in any mode (not only plan mode). The plan opens with a [view-only] header; closing it sends nothing back to the Executor.

Agents call request_approval naturally when the host is in plan mode, but they will not volunteer plans outside it. To encourage that, add an instruction like this to your project's CLAUDE.md (or equivalent agent instructions):

## AgentTakt

Whenever you formulate a multi-step plan — in any mode, not just plan mode —
submit it with the AgentTakt `show_plan` tool so the human can see it as a
node graph. Use `request_approval` instead when you need the human's approval
before executing.

Note: a [view-only] plan occupies the editor until dismissed; a later request_approval waits in the queue behind it.

Debug mode (try it without MCP)

uvx agenttakt open examples/sample_plan.json --out edited.json

Loads a plan from a file, opens the editor, and writes the approval result to --out.

Key Bindings

Key

Action

a

Approve the plan (confirmation dialog)

r

Reject the plan (with a reason)

n

Add a node

d / Delete

Delete the selected node/edge

u / U

Undo / Redo

Arrow keys

Move the selected node by one cell (fine-tuning)

Escape

Clear selection

p

Toggle the parameter panel

?

Help (controls and how to write type / data)

q

Quit

Mouse: drag a node to move it; drag from a node's output port (●, right edge) and release on another node to create an edge.

Edges are drawn as braille Bezier-like curves by default. If they render poorly in your environment, switch to rounded orthogonal lines with --edges orthogonal.

Documentation

  • Plan JSON schema — data model, node fields, what to write in type / data, and validation rules

  • Changelog — release notes for each version

License

MIT

Available Tools

1 tool
request_approvalA

Submit a task execution plan for human review in the AgentTakt editor.

Blocks until the human approves or rejects the plan in the TUI. Returns {"status": "approved" | "rejected", "plan": , "reason": <str | null>}. The returned plan may differ from the submitted one (the human can edit nodes, edges and parameters) — always execute the returned plan.

Args: plan: Plan JSON with graph_id, nodes[] and edges[] (must be a DAG). summary: One-line description shown in the editor header.

ParametersJSON Schema
NameRequiredDescriptionDefault
planYes
summaryNo

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description fully carries the behavioral burden. It discloses the blocking behavior, the exact return format, the possibility of the plan being edited, and the critical instruction to execute the returned plan. This goes beyond the schema and provides essential behavioral insights.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded with purpose, followed by behavior, return value, and parameter explanations. Every sentence provides value, with no tautology or fluff. The formatting with paragraphs and an Args list enhances readability without being overly verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's purpose, blocking behavior, return format, and parameter semantics, which is comprehensive for a two-parameter tool. It lacks explicit error scenarios or timeout handling, but the output schema (as described in the return format) and parameter hints are sufficient for correct usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Since schema description coverage is 0%, the description must compensate, and it does. It explains 'plan' as 'Plan JSON with graph_id, nodes[] and edges[] (must be a DAG)' and 'summary' as 'One-line description shown in the editor header'. This adds meaning, though the inner structure of nodes/edges is not detailed, leaving some room for improvement.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Submit') and resource ('a task execution plan for human review in the AgentTakt editor'). It unambiguously identifies what the tool does, making its function distinct even in the absence of sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context by noting that the tool blocks until human approval/rejection and instructs to 'always execute the returned plan'. It does not explicitly mention alternatives or exclusions, but given no sibling tools, this is acceptable and gives practical usage direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev0.1.0
    • First observedrequest_approval

TDQS

A4.4/5.0
Disambiguation5/5

Only one tool exists, so there is no possibility of confusion or overlap. The tool has a clear, single purpose.

Naming Consistency5/5

With only one tool, naming consistency is trivially satisfied. The name 'request_approval' clearly follows a verb_noun pattern and is unambiguous.

Tool Count2/5

A single tool is too few for a server that might reasonably support additional operations such as querying approval status or managing plans. The minimal surface feels thin for a general-purpose agent toolkit.

Completeness4/5

The tool covers the full submit-and-wait-for-approval lifecycle, but there are minor gaps like cancellation or historical querying. For its stated purpose, the core workflow is complete.

Maintenance

ActivityMaintained
ResponsivenessSyncing

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