AI Intervention Agent
This server provides a real-time human-in-the-loop intervention system for AI agents via a single MCP tool, interactive_feedback. Key capabilities include:
Request user feedback: Pause agent execution mid-task and present a Markdown-formatted message or question through a Web UI.
Predefined options: Offer single or multi-select choices to guide the agent with structured responses.
Text & image input: Collect free-form text and image uploads (returned as base64-encoded data) from the user.
Multi-task support: Manage multiple concurrent feedback requests, each with independent countdown timers.
Auto re-submit: Automatically re-submit to keep long-running sessions alive if the user doesn't respond in time.
Notifications: Alert users via web, sound, system notifications, or Bark (push notifications).
Broad compatibility: Integrates with Cursor, VS Code, Claude Code, Augment, Windsurf, Trae, and more.
VS Code extension: Optionally embed the feedback panel directly into the IDE sidebar.
Configurable: Customize the web UI port and feedback timeouts via a configuration file.
Uses Flask to provide a Web UI service with HTTP API endpoints for real-time user intervention and feedback management during AI agent interactions.
Renders Markdown content in the Web UI for displaying agent messages and user feedback with code highlighting and math rendering support.
Uses Mermaid for architecture diagrams in documentation to visualize the system's component relationships and data flow.
Click on "Install 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., "@AI Intervention AgentI need to review the agent's current approach before it continues."
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.
Ever had your AI agent confidently walk off in the wrong direction mid-task? AI Intervention Agent gives you a Web UI to pause the agent at key moments, review what it's about to do, type a course-correction, attach screenshots, and resume — all through the MCP interactive_feedback tool, without ending the conversation.
Works with Cursor, VS Code, Claude Code, Augment, Windsurf, Trae, and more.
Quick start
Point your AI tool at the MCP server via uvx (installs and runs the latest version automatically):
{
"mcpServers": {
"ai-intervention-agent": {
"command": "uvx",
"args": ["ai-intervention-agent"],
"timeout": 600,
"autoApprove": ["interactive_feedback"]
}
}
}
Then add the prompt snippet below to your agent rules / system prompt, so the agent asks you through interactive_feedback instead of finishing tasks silently.
- Only ask me through the MCP `ai-intervention-agent` tool; do not ask directly in chat or ask for end-of-task confirmation in chat.
- If a tool call fails, keep asking again through `ai-intervention-agent` instead of making assumptions, until the tool call succeeds.
ai-intervention-agent usage details:
- If requirements are unclear, use `ai-intervention-agent` to ask for clarification with predefined options.
- If there are multiple approaches, use `ai-intervention-agent` to ask instead of deciding unilaterally.
- If a plan/strategy needs to change, use `ai-intervention-agent` to ask instead of deciding unilaterally.
- Before finishing a request, always ask for feedback via `ai-intervention-agent`.
- Do not end the conversation/request unless the user explicitly allows it via `ai-intervention-agent`.Install the package (remember to pip install --upgrade ai-intervention-agent periodically):
pip install ai-intervention-agentThen configure your AI tool to launch the installed entry point:
{
"mcpServers": {
"ai-intervention-agent": {
"command": "ai-intervention-agent",
"args": [],
"timeout": 600,
"autoApprove": ["interactive_feedback"]
}
}
}If your IDE/CLI has an AI agent (Cursor, Claude Code, VS Code, Windsurf, Trae, Augment, ...), paste this prompt in chat and let it write the config:
Please configure my IDE / AI tool to use the `ai-intervention-agent` MCP server:
1. Locate the correct MCP config file for my current IDE
(e.g. `.cursor/mcp.json` or `~/.cursor/mcp.json` for Cursor,
`~/.claude.json` for Claude Code,
`.vscode/mcp.json` for VS Code).
2. Add this entry under `mcpServers`:
- command: `uvx`
- args: `["ai-intervention-agent"]`
- timeout: 600
- autoApprove: `["interactive_feedback"]`
3. Append the project's recommended prompt rules
(the "Prompt snippet (copy/paste)" block in this README)
to my agent rules / system prompt, so the agent always asks me
through `interactive_feedback` instead of ending tasks silently.
4. Verify by listing MCP servers and confirming `ai-intervention-agent` is loaded.interactive_feedback is a long-running tool; some clients enforce a hard request timeout. The Web UI ships a countdown + auto re-submit (feedback.frontend_countdown, default 240s, range 0 or [10, 3600]) to keep sessions alive — the default stays under the common 300s hard timeout.
Related MCP server: ai-intervention-agent
Screenshots
Key features
Real-time intervention — the agent pauses and waits for your input via
interactive_feedbackWeb UI — Markdown, code highlighting, and math rendering out of the box
Multi-task tabs — concurrent requests with independent countdowns, per-task draft autosave, and auto re-submit that keeps long sessions alive (your typed text and checked options are submitted at zero, never an empty prompt)
Typing-hold — the countdown auto-extends while you type and never fires mid-input (web page and VS Code extension alike)
Agent-loop ergonomics — per-task
header_labelcontext chips,question_type='yesno'one-click decisions, andfeedback_placeholderhintsNotifications — web / sound / system / Bark (iOS push), plus custom notification sound upload
SSH / LAN friendly — works behind port forwarding; mDNS publishes a
<host>.localURL when supportedi18n — Web UI + VS Code extension shipped in
en/zh-CN/zh-TWPWA, offline-aware, WCAG 2.1 AA accessible — installable from the browser, with contrast / focus / reduced-motion audited and locked by invariant tests
Stable install — built on Flask 3.x with conservative dependency pins; immune to the Starlette 1.0 breaking change that broke several MCP feedback servers in early 2026
Architecture overview
AIIA runs as a single Python process bridging three surfaces: an MCP
stdio server exposing interactive_feedback, a Flask web server with
an SSE event bus, and a persistent task queue feeding the notification
stack. The component diagram, the interaction and failure-recovery
sequence diagrams, the agent-side MCP parameter table, and the runtime
invariant catalogue live in docs/architecture.md.
VS Code extension (optional)
Embeds the interaction panel into VS Code's sidebar so you never switch to a browser.
Install: Open VSX, VS Code Marketplace, or download the VSIX from GitHub Releases
Key setting:
ai-intervention-agent.serverUrl— must match your Web UI URL (e.g.http://localhost:8080; change the port viaweb_ui.portinconfig.toml.default)More:
ai-intervention-agent.logLevel, macOS native notifications (on by default, toggle in the sidebar's Notification Settings panel) — full settings list and the AppleScript executor security model inpackages/vscode/README.md
Configuration
On first run, config.toml is created from config.toml.default in your OS user config directory — the full TOML reference is in docs/configuration.md:
OS | User config directory |
Linux |
|
macOS |
|
Windows |
|
For uvx, Docker, systemd, or SSH-remote runtimes where editing the file is awkward, the most-used web_ui settings can be overridden by env var at startup (invalid values log a WARNING and fall back safely; full surface in docs/configuration.md#environment-variable-overrides):
export AI_INTERVENTION_AGENT_WEB_UI_HOST=0.0.0.0 # default 127.0.0.1
export AI_INTERVENTION_AGENT_WEB_UI_PORT=8181 # default 8080, range [1, 65535]
export AI_INTERVENTION_AGENT_WEB_UI_LANGUAGE=en # auto / en / zh-CN / zh-TW
uvx ai-intervention-agentCLI inspection: --version, --help, and --print-config (dumps the effective merged config as jq-friendly JSON, with secret-like fields redacted — answers "is my port from env or from config.toml?" in one pipeline).
On iPhone, the smoothest setup wraps the Web UI in a Shortcuts automation and points Bark notification taps at it — step-by-step guide in docs/configuration.md#recommended-iphone-setup-shortcuts--bark.
Documentation
Docs index (by audience):
docs/README.md·docs/README.zh-CN.mdArchitecture (diagrams + agent workflow):
docs/architecture.mdMCP tool reference:
docs/mcp_tools.md·docs/mcp_tools.zh-CN.mdAPI docs:
docs/api/index.md·docs/api.zh-CN/index.mdTroubleshooting / FAQ:
docs/troubleshooting.md·docs/troubleshooting.zh-CN.mdRelease notes:
CHANGELOG.md· VS Code marketplace listing:packages/vscode/CHANGELOG.mdContributing:
CONTRIBUTING.md·CODE_OF_CONDUCT.md· scripts index:scripts/README.md· i18n guide:docs/i18n.mdRelease recovery runbook:
docs/release-recovery.md·docs/release-recovery.zh-CN.mdDeepWiki Q&A — AI-augmented Q&A over the repo:
Related projects
Project | Stars (approx.) | Focus |
mcp-feedback-enhanced (Minidoracat) | ~3.8k | Largest sibling; Web UI + Tauri desktop app, auto-command execution, SSH Remote / WSL detection. |
cunzhi (imhuso) | ~1.4k | Chinese-language project focused on preventing premature task completion. |
Relay (andeya) | new | Multi-IDE relay, multi-tab session merging, native desktop window, Cursor usage monitoring. |
new | Node.js port with WebSocket UI and Speech-to-Text via OpenAI Whisper. | |
interactive-feedback-mcp (junanchn) | ~50 | Win32-native always-on-top window, auto-reply rules. |
interactive-feedback-mcp (poliva) | ~310 | Direct ancestor fork (see Acknowledgements); minimal Python MCP, single feedback dialog. |
interactive-feedback-mcp (Pursue-LLL) | ~30 | Independent smaller-scale fork emphasising minimal dependencies. |
Where AIIA sits on the spectrum: AIIA targets the operationally deep end — Web UI + VS Code extension sharing one backend, production-grade observability (/metrics Prometheus endpoint + a reference Grafana dashboard), bilingual i18n + docs, strict invariant test discipline (8,200+ tests + 1,050+ subtests across 40 audit cycles), and a 5-job release pipeline. Want the smallest drop-in? poliva's fork. A desktop app? mcp-feedback-enhanced. Voice / multi-tab UI? Relay or the Node.js fork. Full-stack operational integration? AIIA.
Feature gap callouts (contributions welcome): Speech-to-Text input, always-on-top native window, Cursor usage monitoring, multi-tab session merging UI.
Star counts are approximate snapshots (last reviewed 2026-06); check each upstream for current numbers. Submit a PR if you'd like another related project listed.
Acknowledgements
This project's heritage traces back to Fábio Ferreira (2024) and Pau Oliva (2025), whose original noopstudios/interactive-feedback-mcp and poliva/interactive-feedback-mcp seeded the MCP interactive_feedback tool surface. Their copyright notices are preserved in LICENSE per the MIT license terms. The v1.5.x line is a substantial rewrite — Web UI, VS Code extension, i18n, notification stack, CI/CD pipeline — owned and maintained by @xiadengma (PyPI / Open VSX / VS Code Marketplace publisher).
License
MIT License
Available Tools
1 toolinteractive_feedbackInteractive Feedback (人机协作反馈)A
Ask the human user for interactive feedback through the Web UI.
Use this tool whenever you need a human decision, clarification, confirmation, plan approval, design review, or final sign-off before continuing — especially when the next step has multiple valid approaches, irreversible side effects, or significant trade-offs.
Behavior:
Renders the resolved message (Markdown) and an optional list of options in a Web UI; the user submits text + selected options + optional images.
The call blocks until the user submits, the auto-resubmit countdown expires, or the configured backend timeout is reached.
On success, returns a list of MCP content blocks (text + image) that include the user reply, selected options, and an optional prompt suffix.
On parameter validation failure, raises
ToolErrorso the agent can retry with corrected arguments. On service / task failure, returns a configurable resubmit prompt instructing the agent to call this tool again, instead of silently dropping the request.
Cross-tool compatibility:
summary/promptare accepted as aliases formessageso the samemcp.jsonconfig can target other feedback MCP variants without retraining the agent.optionsis an alias forpredefined_options.project_directory,submit_button_text,timeout,timeout_seconds,feedback_type,priority,language,tags,user_id,task_idare accepted but ignored. They prevent the first-call validation failures observed when an agent reuses arguments shaped for a different feedback MCP server.
Note: this function is not the MCP registration site itself; server.py
wraps it with mcp.tool() to expose it to MCP clients.
R25.2: 函数体首行 import httpx 让下面 except httpx.HTTPError 在运行时
解析符号——本工具被 MCP 客户端首次调用时一次性付 ~55 ms 加载费,而 MCP server
cold-start 路径完全不会进入此函数(server.py 顶层 import 时只是定义而已)。
R44 FastMCP 最佳实践:ctx 关键字参数(FastMCP 自动注入)让本函数可以走
await _emit_ctx_info(ctx, ...) 把 task lifecycle 事件回送给 client
(Cursor / Claude Desktop / ChatGPT Desktop)。client 收到后会在 chat
sidebar 渲染一行进度日志,让人类用户能"看到工具确实在工作、正在等真人
回复",而不是猜"agent 是不是 hung 住了"。ctx 永远 keyword-only 且
默认 None,所以本工具被通过别的入口(pytest 直接调)调用时不会因为缺
ctx 而崩;具体安全语义见 _emit_ctx_info 的 docstring。
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | Accepted for compatibility; ignored by this server. | |
| prompt | No | Compatibility alias for `message`. Ignored when `message` is provided. | |
| loop_id | No | Loop engineering: optional stable identifier shared by every feedback round that belongs to the same goal / outer loop (agent-chosen, e.g. 'auth-refactor-2026-07'). Rounds that carry the same loop_id are grouped in the UI so the human reviewer can replay 'which rounds did this objective go through, and what was decided each time'. Length: clamped to 64 characters server-side. Omit for standalone one-shot questions (default behavior unchanged). | |
| message | No | Question, summary, or proposal to display to the human user. MUST be a non-empty string. Supports CommonMark / GitHub-Flavored Markdown (headings, lists, tables, fenced code blocks, links, inline code). Recommended length: 1-2000 characters; soft cap 1,000,000 characters (~1 MB UTF-8, R166); inputs longer than the cap are truncated with a trailing ellipsis marker. Best practices: (1) state the question clearly in the first line; (2) include the recommended/default answer when proposing options; (3) escape special characters properly in JSON (use \" for quotes, \n for newlines). If omitted, the server falls back to `summary` or `prompt` for cross-tool compatibility. | |
| options | No | Compatibility alias for `predefined_options`. Ignored when `predefined_options` is provided. | |
| summary | No | Compatibility alias for `message` (used by noopstudios/Minidoracat interactive-feedback-mcp variants). Ignored when `message` is provided. | |
| task_id | No | Accepted for compatibility (some agents pre-generate a trace ID and pass it through); this server always auto-generates an internal task ID and ignores the externally supplied value. Useful when the same `mcp.json` config also points at MCP variants that *do* honour an externally supplied task ID. | |
| timeout | No | Accepted for compatibility; this server uses its own configured backend timeout and auto-resubmit countdown. | |
| user_id | No | Accepted for compatibility; ignored by this server. | |
| language | No | Accepted for compatibility; UI language follows the user's saved settings. | |
| priority | No | Accepted for compatibility; ignored by this server. | |
| loop_phase | No | Loop engineering: optional free-form phase tag for this round, e.g. 'investigate' / 'implement' / 'verify' / 'review'. Helps the reviewer see where in the inner loop the agent currently is. Length: clamped to 32 characters server-side. | |
| header_label | No | Optional short chip / tag rendered above the prompt in the task pane to give a one-word context cue (e.g. 'Auth', 'DB', 'Layout', 'CSS', 'i18n'). Length: clamped to 16 characters server-side; single-word recommendation, no spaces if avoidable. Especially useful in multi-task mode where the user juggles 3+ concurrent feedback requests — the chip lets them visually distinguish task domains at a glance. If omitted or empty, no chip is shown (default existing layout). (mining-cycle-3 §2.1 — borrowed from gemini-cli ``ask_user.header`` schema.) | |
| feedback_type | No | Accepted for compatibility; ignored by this server. | |
| question_type | No | Optional UI mode hint: when ``'yesno'``, the frontend hides the free-text textarea and renders a single-row Yes/No button pair. User's click submits the literal string 'yes' or 'no' as the feedback result — saves typing + Submit-button click for binary decisions (approve/reject, proceed/abort, etc.). Allowed values: ``'yesno'`` (current) or ``None`` (default: keep textarea + optional ``predefined_options`` checkboxes). Unknown values silently treated as None (forward-compat for future types like ``'choice'`` / ``'rating'`` once the frontend supports them). (mining-cycle-3 §2.1 — borrowed from gemini-cli ``ask_user`` schema.) | |
| loop_objective | No | Loop engineering: optional one-sentence description of the loop's goal (e.g. 'Migrate auth/session.py to PyJWT 2.x with green integration tests'). Pass it on the first round of a loop_id; later rounds may omit it. Shown to the reviewer as loop context above the prompt. Length: clamped to 500 characters server-side. | |
| iteration_label | No | Loop engineering: optional round label such as 'iter-3' or 'attempt-2'. Shown with the loop context so multiple rounds of the same loop are distinguishable at a glance. Length: clamped to 32 characters server-side. | |
| timeout_seconds | No | Compatibility alias for `timeout` (used by some MCP clients that explicitly suffix the unit). Both fields are accepted for compatibility — this server ignores them and uses its own configured backend timeout / auto-resubmit countdown. When both are provided, this server logs a debug line and discards both, since neither overrides server config. | |
| success_criteria | No | Loop engineering: optional verifiable completion criteria the human should judge the evidence against (e.g. 'pytest all green + no new ruff warnings + docs regenerated'). Rendered alongside the loop context so the verdict is made against an explicit baseline. Length: clamped to 500 characters server-side. | |
| project_directory | No | Accepted for compatibility with other feedback MCP variants; this server ignores it (project context is taken from the running Web UI / config). | |
| predefined_options | No | Optional list of predefined choices the user can pick from (rendered as multi-select checkboxes alongside a free-text reply). Two canonical input shapes (v1.6.0+ — the legacy parallel-array shape `predefined_options_defaults` was removed in R167; use the dict form below to mark recommended options): (a) **RECOMMENDED** list[dict] of shape {"label": str, "default": bool} — mark the recommended option with `default: true` so the UI shows a pre-checked checkbox (field aliases accepted: "label"/"text"/"value", "default"/"selected"/"checked"); (b) list[str] — simple labels, all initially unchecked (use this when no recommendation is needed). Non-string and non-{label,...} items are silently dropped. Each option max length: 10000 characters (longer items truncated). Tips: (1) keep options short, action-oriented and mutually distinguishable; (2) PREFER the dict form for ANY recommended option — `{"label": "Apply", "default": true}`. The UI renders real pre-checked checkboxes, so do NOT use text-prefix hacks (adding marker words to the label) for marking recommendations; (3) the user may also ignore options and reply with free text. If omitted, the server falls back to `options` for cross-tool compatibility. | |
| submit_button_text | No | Accepted for compatibility; this server uses its own UI labels. | |
| feedback_placeholder | No | Optional textarea placeholder hint shown to the user when waiting for free-text feedback. Per-task override of the global ``page.feedbackPlaceholder`` i18n string. Examples: 'Paste the error stack trace', 'Describe the visual glitch', 'Reply 'ok' to approve or 'no' + reason to reject'. Length: clamped to 200 characters server-side (single-line placeholders only; longer text is silently truncated; the response includes ``placeholder_truncated: true`` + ``placeholder_original_length`` + ``placeholder_max_length`` when clamping activates so callers can warn). If omitted or empty, the UI uses its default i18n placeholder. (mining-cycle-3 §2.1 — borrowed from gemini-cli ``ask_user`` schema.) |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description extensively explains behavior beyond annotations: blocks until user submits, returns MCP content blocks, raises ToolError on validation, and documents accepted/ignored parameters for cross-tool compatibility. Annotations (readOnlyHint=false, etc.) are consistent with the description; no contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is excessively long and includes internal implementation details (import statement, cold-start cost, FastMCP context injection, Python code comments). These are not essential for an AI agent selecting or invoking the tool. The structure is organized, but the verbosity reduces conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (23 optional parameters, aliases, loop engineering features, output behavior), the description is remarkably complete. It covers expected behavior, parameter interactions, error handling, and output format. No gaps are apparent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although schema coverage is 100%, the description adds significant value by explaining aliases, ignored parameters, best practices (e.g., 'prefer the dict form for predefined_options'), truncation behavior, and loop engineering semantics. This goes far beyond the schema's basic descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Ask the human user for interactive feedback') and the resource ('through the Web UI'). It lists multiple specific use cases (decision, clarification, confirmation, plan approval, design review, final sign-off), making the purpose unmistakable. No siblings exist, so differentiation is not needed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells when to use the tool: 'whenever you need a human decision... especially when the next step has multiple valid approaches, irreversible side effects, or significant trade-offs.' This is concrete and actionable. It does not discuss when not to use, but given the lack of siblings, the guidance is sufficient.
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 tool update
v1.8.3- Changed
interactive_feedback5 fields changed- added
Input schema / properties / iteration_labelAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Loop engineering: optional round label such as 'iter-3' or 'attempt-2'. Shown with the loop context so multiple rounds of the same loop are distinguishable at a glance. Length: clamped to 32 characters server-side." +} - added
Input schema / properties / loop_idAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Loop engineering: optional stable identifier shared by every feedback round that belongs to the same goal / outer loop (agent-chosen, e.g. 'auth-refactor-2026-07'). Rounds that carry the same loop_id are grouped in the UI so the human reviewer can replay 'which rounds did this objective go through, and what was decided each time'. Length: clamped to 64 characters server-side. Omit for standalone one-shot questions (default behavior unchanged)." +} - added
Input schema / properties / loop_objectiveAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Loop engineering: optional one-sentence description of the loop's goal (e.g. 'Migrate auth/session.py to PyJWT 2.x with green integration tests'). Pass it on the first round of a loop_id; later rounds may omit it. Shown to the reviewer as loop context above the prompt. Length: clamped to 500 characters server-side." +} - added
Input schema / properties / loop_phaseAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Loop engineering: optional free-form phase tag for this round, e.g. 'investigate' / 'implement' / 'verify' / 'review'. Helps the reviewer see where in the inner loop the agent currently is. Length: clamped to 32 characters server-side." +} - added
Input schema / properties / success_criteriaAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Loop engineering: optional verifiable completion criteria the human should judge the evidence against (e.g. 'pytest all green + no new ruff warnings + docs regenerated'). Rendered alongside the loop context so the verdict is made against an explicit baseline. Length: clamped to 500 characters server-side." +}
1 tool update
v1.7.13- Changed
interactive_feedback3 fields changed- added
Input schema / properties / feedback_placeholderAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional textarea placeholder hint shown to the user when waiting for free-text feedback. Per-task override of the global ``page.feedbackPlaceholder`` i18n string. Examples: 'Paste the error stack trace', 'Describe the visual glitch', 'Reply 'ok' to approve or 'no' + reason to reject'. Length: clamped to 200 characters server-side (single-line placeholders only; longer text is silently truncated; the response includes ``placeholder_truncated: true`` + ``placeholder_original_length`` + ``placeholder_max_length`` when clamping activates so callers can warn). If omitted or empty, the UI uses its default i18n placeholder. (mining-cycle-3 §2.1 — borrowed from gemini-cli ``ask_user`` schema.)" +} - added
Input schema / properties / header_labelAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional short chip / tag rendered above the prompt in the task pane to give a one-word context cue (e.g. 'Auth', 'DB', 'Layout', 'CSS', 'i18n'). Length: clamped to 16 characters server-side; single-word recommendation, no spaces if avoidable. Especially useful in multi-task mode where the user juggles 3+ concurrent feedback requests — the chip lets them visually distinguish task domains at a glance. If omitted or empty, no chip is shown (default existing layout). (mining-cycle-3 §2.1 — borrowed from gemini-cli ``ask_user.header`` schema.)" +} - added
Input schema / properties / question_typeAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional UI mode hint: when ``'yesno'``, the frontend hides the free-text textarea and renders a single-row Yes/No button pair. User's click submits the literal string 'yes' or 'no' as the feedback result — saves typing + Submit-button click for binary decisions (approve/reject, proceed/abort, etc.). Allowed values: ``'yesno'`` (current) or ``None`` (default: keep textarea + optional ``predefined_options`` checkboxes). Unknown values silently treated as None (forward-compat for future types like ``'choice'`` / ``'rating'`` once the frontend supports them). (mining-cycle-3 §2.1 — borrowed from gemini-cli ``ask_user`` schema.)" +}
1 tool update
v1.6.3- Added
interactive_feedback
1 tool update
v1.6.2- Removed
interactive_feedback
1 tool update
v1.6.0- Changed
interactive_feedback20 fields changed- added
Input schema / properties / feedback_typeAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Accepted for compatibility; ignored by this server." +} - added
Input schema / properties / languageAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Accepted for compatibility; UI language follows the user's saved settings." +} - added
Input schema / properties / message / anyOfAdded value: +[ + { + "type": "string" + }, + { + "type": "null" + } +] - added
Input schema / properties / message / defaultAdded value: +null - changed
Input schema / properties / message / descriptionPrevious value: -"向用户展示的具体问题/提示(支持 Markdown)"New value: +"Question, summary, or proposal to display to the human user. MUST be a non-empty string. Supports CommonMark / GitHub-Flavored Markdown (headings, lists, tables, fenced code blocks, links, inline code). Recommended length: 1-2000 characters; hard limit 10000 (longer input is truncated). Best practices: (1) state the question clearly in the first line; (2) include the recommended/default answer when proposing options; (3) escape special characters properly in JSON (use \\\" for quotes, \\n for newlines). If omitted, the server falls back to `summary` or `prompt` for cross-tool compatibility." - removed
Input schema / properties / message / typeRemoved value: -"string" - added
Input schema / properties / optionsAdded value: +{ + "anyOf": [ + { + "items": {}, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Compatibility alias for `predefined_options`. Ignored when `predefined_options` is provided." +} - changed
Input schema / properties / predefined_options / descriptionPrevious value: -"可选的预定义选项列表,供用户单选/多选"New value: +"Optional list of predefined choices the user can pick from (rendered as multi-select checkboxes alongside a free-text reply). Three input shapes are accepted (v1.5.20+): (a) list[str] — simple labels, all initially unchecked; (b) list[dict] of shape {\"label\": str, \"default\": bool} — let the recommended option start pre-checked without any extra param (aliases: \"label\"/\"text\"/\"value\", \"default\"/\"selected\"/\"checked\"); (c) list[str] paired with the sibling param `predefined_options_defaults` (parallel boolean array). Non-string and non-{label,...} items are silently dropped. Each option max length: 500 characters (longer items are truncated). Tips: (1) keep options short, action-oriented and mutually distinguishable; (2) prefer the dict form `{\"label\": \"Apply\", \"default\": true}` to mark the recommended/default answer — the UI now renders real pre-checked checkboxes, so do NOT rely on text-prefix hacks for marking recommended options; (3) the user may also ignore options and reply with free text. If omitted, the server falls back to `options` for cross-tool compatibility." - added
Input schema / properties / predefined_options_defaultsAdded value: +{ + "anyOf": [ + { + "items": {}, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional sibling array (v1.5.20+) for the `list[str]` shape of `predefined_options`: each element decides whether the corresponding checkbox starts pre-checked. Truthy aliases (case-insensitive, trimmed): True / 1 / 1.0 / \"true\" / \"yes\" / \"on\" / \"selected\"; everything else (including None / 0 / lists / dicts) → False. Length is silently truncated when longer than `predefined_options` and padded with False when shorter. Ignored when `predefined_options` already uses the {\"label\", \"default\"} dict form (which takes precedence)." +} - added
Input schema / properties / priorityAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Accepted for compatibility; ignored by this server." +} - added
Input schema / properties / project_directoryAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Accepted for compatibility with other feedback MCP variants; this server ignores it (project context is taken from the running Web UI / config)." +} - added
Input schema / properties / promptAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Compatibility alias for `message`. Ignored when `message` is provided." +} - added
Input schema / properties / submit_button_textAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Accepted for compatibility; this server uses its own UI labels." +} - added
Input schema / properties / summaryAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Compatibility alias for `message` (used by noopstudios/Minidoracat interactive-feedback-mcp variants). Ignored when `message` is provided." +} - added
Input schema / properties / tagsAdded value: +{ + "anyOf": [ + { + "items": {}, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Accepted for compatibility; ignored by this server." +} - added
Input schema / properties / task_idAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Accepted for compatibility (some agents pre-generate a trace ID and pass it through); this server always auto-generates an internal task ID and ignores the externally supplied value. Useful when the same `mcp.json` config also points at MCP variants that *do* honour an externally supplied task ID." +} - added
Input schema / properties / timeoutAdded value: +{ + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Accepted for compatibility; this server uses its own configured backend timeout and auto-resubmit countdown." +} - added
Input schema / properties / timeout_secondsAdded value: +{ + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Compatibility alias for `timeout` (used by some MCP clients that explicitly suffix the unit). Both fields are accepted for compatibility — this server ignores them and uses its own configured backend timeout / auto-resubmit countdown. When both are provided, this server logs a debug line and discards both, since neither overrides server config." +} - added
Input schema / properties / user_idAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Accepted for compatibility; ignored by this server." +} - removed
Input schema / requiredRemoved value: -[ - "message" -]
TDQS
Only one tool exists, so there is no ambiguity with other tools. The tool's purpose is clearly described.
With a single tool, naming consistency is moot. The name `interactive_feedback` is descriptive and follows a clear pattern.
The server has only one tool, which is too few for a server named 'AI Intervention Agent' that suggests a broader scope of interventions.
The single comprehensive tool covers feedback collection, but the server name implies additional intervention capabilities are missing, making the tool surface incomplete.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Human-in-the-loop review and approval for AI agents. Audit trail, approval policies, native MCP.
Human-in-the-loop for AI agents over MCP: durable approvals with a hosted review page & audit trail
Zero-secret MCP gateway for AI agents: risk-scored, audited calls with human-in-the-loop approval.
Live data grids for AI agents. Push structured data; humans review, agents read back via MCP.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceHuman-in-the-Loop authorization gateway for AI Agents. Securely pause MCP workflows and route high-risk actions to human approvers via Slack or Email.1151MIT
- AlicenseNot gradedqualityDmaintenanceEnables real-time user intervention for MCP agents via a Web UI and interactive_feedback tool, allowing users to review context and send instructions when agents drift from intent.MIT

Datashift MCP Serverofficial
AlicenseNot gradedqualityDmaintenanceEnables AI agents to submit tasks for human or AI review and receive decisions via MCP tools, adding human review checkpoints to workflows.MIT- AlicenseAqualityDmaintenanceRuntime quality validation for AI agent outputs. Detect hallucinations, enforce scope compliance, and score output quality — all via MCP.657MIT
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/XIADENGMA/ai-intervention-agent'
If you have feedback or need assistance with the MCP directory API, please join our Discord server