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Wang-JQ77

deepseek-litresearch

by Wang-JQ77

execute_research

Run an approved research plan asynchronously, returning a job ID for progress tracking and final report retrieval after user confirmation.

Instructions

第二步:按已确认的大纲执行深度调研(plan_research 之后调用)。

确认门禁(需求 2):大纲生成后必须经用户确认/修改才能执行—— 首次调用(confirm=false 且未提供 plan_override)只返回大纲并不执行, 状态保持 planned;用户确认后传 confirm=true,或直接以修改后的 plan_override 调用(修改即确认),任务才进入后台执行。

执行为异步(2026-08-29 修复):立即返回 job_id(status=running), 流水线在独立线程运行,客户端超时/断开不影响任务;用 get_research_job(job_id) 轮询进度与结果,cancel_research_job 可取消。

Args: job_id: plan_research 返回的 job_id。 plan_override: 用户修改后的大纲([{question, queries}]),覆盖原大纲执行; 不传则按 plan_research 生成的原大纲执行。提供它即视为已确认。 depth: 递归检索深度(1-3),覆盖 plan 阶段参数并回写任务记录。 max_papers: 文献池规模上限,覆盖 plan 阶段参数并回写任务记录。 sources: 数据源列表,覆盖 plan 阶段参数并回写任务记录。 format: 报告导出格式(markdown | html | both | json),覆盖并回写。 critique: 红队审查开关;None 时沿用任务存储的参数(不再默认 True 覆盖)。 language: 报告语言 zh | en;None 时沿用任务存储的参数。 confirm: 用户已确认大纲(首次调用必须为 true 或提供 plan_override)。 注意(2026-08-30 门禁强化):outline_confirm_strict 开启时,大纲生成后 outline_confirm_min_seconds 秒内的执行请求会被退回等待确认—— agent 必须先把大纲展示给用户并等待回复,不得跳过确认步骤。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNo
formatNo
job_idYes
confirmNo
sourcesNo
critiqueNo
languageNo
max_papersNo
plan_overrideNo
Install Server

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, the description carries the full transparency burden and succeeds: it discloses async execution, first-call no-op behavior, plan_override implying confirmation, the strict confirmation gate, None-value fallback semantics, and parameter write-back behavior.

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

Conciseness4/5

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

The content is front-loaded and structured, with purpose first, then workflow gate, async behavior, and parameter list. Minor deduction for dated change-log notes such as '2026-08-29 修复' and '2026-08-30 门禁强化', which add maintenance noise rather than essential procedural meaning.

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 tool's complexity, 9 parameters, and lack of output schema, the description provides everything needed to invoke it correctly: preconditions, confirmation behavior, async semantics, cancellation/polling paths, and parameter override effects.

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 0%, so the description must document the parameters. It covers all 9 parameters, including type domains (depth 1-3, format enum, language zh/en), override semantics, and confirmation logic.

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 opens with '第二步:按已确认的大纲执行深度调研(plan_research 之后调用)', giving a specific action, object, and workflow position. This clearly distinguishes the tool from planning and follow-up siblings.

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?

It states explicitly when to invoke the tool (after plan_research), what the confirmation precondition is, and exactly what to use for follow-up (get_research_job for polling, cancel_research_job for cancellation). The workflow guidance is unambiguous.

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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