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Interactive Feedback (人机协作反馈)

interactive_feedback

Requests human feedback via web UI for decisions, clarifications, or approvals, blocking until user responds. Use when multiple valid approaches exist or actions have irreversible effects.

Instructions

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 ToolError so 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 / prompt are accepted as aliases for message so the same mcp.json config can target other feedback MCP variants without retraining the agent.

  • options is an alias for predefined_options.

  • project_directory, submit_button_text, timeout, timeout_seconds, feedback_type, priority, language, tags, user_id, task_id are 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。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageNoQuestion, 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.
predefined_optionsNoOptional 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.
summaryNoCompatibility alias for `message` (used by noopstudios/Minidoracat interactive-feedback-mcp variants). Ignored when `message` is provided.
promptNoCompatibility alias for `message`. Ignored when `message` is provided.
optionsNoCompatibility alias for `predefined_options`. Ignored when `predefined_options` is provided.
project_directoryNoAccepted for compatibility with other feedback MCP variants; this server ignores it (project context is taken from the running Web UI / config).
submit_button_textNoAccepted for compatibility; this server uses its own UI labels.
timeoutNoAccepted for compatibility; this server uses its own configured backend timeout and auto-resubmit countdown.
feedback_placeholderNoOptional 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.)
question_typeNoOptional 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.)
header_labelNoOptional 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_typeNoAccepted for compatibility; ignored by this server.
priorityNoAccepted for compatibility; ignored by this server.
languageNoAccepted for compatibility; UI language follows the user's saved settings.
tagsNoAccepted for compatibility; ignored by this server.
user_idNoAccepted for compatibility; ignored by this server.
timeout_secondsNoCompatibility 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.
task_idNoAccepted 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.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Annotations are minimal (readOnlyHint=false, destructiveHint=false, etc.) but the description adds extensive behavioral context: blocking behavior, auto-resubmit, timeout, return types, error handling, and compatibility with other MCP variants. No contradictions with annotations.

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

Conciseness2/5

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

The description is excessively long with implementation details (import httpx, FastMCP ctx injection, R-number references) that are irrelevant for an agent deciding tool selection. While structured, the verbosity undermines conciseness; every sentence should earn its place, and many do not.

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

Completeness5/5

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

Given the complexity (18 optional parameters, no required, output schema exists but not shown), the description covers all necessary aspects: behavior, cross-tool compatibility, error handling, and technical constraints. The agent has sufficient information to use the tool correctly.

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

Parameters5/5

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

Schema description coverage is 100%, yet the description adds significant value beyond schema: explains compatibility aliases, provides best practices for message and predefined_options, describes UI behavior of question_type and header_label, and gives examples. This greatly aids correct parameter usage.

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 asks for human feedback via Web UI and lists specific use cases (decision, clarification, confirmation, etc.). The verb 'ask' and resource 'human user' are specific, and no sibling tools exist, so differentiation is not an issue.

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

Usage Guidelines5/5

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

Explicit guidance on when to use: 'whenever you need a human decision, clarification, confirmation... especially when next step has multiple valid approaches, irreversible side effects, or significant trade-offs.' This is comprehensive and actionable.

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

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