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Wang-JQ77

deepseek-litresearch

by Wang-JQ77

set_settings

Modify plugin settings and save them to the configuration file with immediate effect. Toggle research features, set depth/breadth, configure API keys, proxies, and report formats.

Instructions

修改插件设置并写入 ~/.deepseek-litresearch/settings.json(热生效)。

Args: litresearch_enabled: Deep LitResearch 功能总开关(默认开)。聊天框技能可切换此开关。 breadth: 检索广度(子问题数 1-6),默认 3。 depth: 检索深度(递归层级 1-3),默认 2。 scihub_enabled: 是否启用 Sci-Hub 兜底(默认关闭)。开启需 acknowledge_scihub_risk=true。 scihub_mirrors: 镜像站列表(完整 URL),覆盖默认镜像;可自行添加。 scihub_min_interval: 请求最小间隔(秒)。 scholar_enabled: 是否启用 Google Scholar 增强源(默认关闭)。 scholar_provider: Scholar 接入方式(scraperapi=ScraperAPI HTML 解析 / serpapi=SerpAPI JSON)。 scraperapi_url: ScraperAPI 代理入口 URL(默认 https://api.scraperapi.com)。 scraperapi_key: ScraperAPI Key(按成功响应计费,请确认后启用)。 serpapi_key: SerpAPI Key(原有通道,按次计费)。 ezproxy_base_url: 校园 EZProxy 地址(如 https://ezproxy.your-university.edu)。 ezproxy_username / ezproxy_password: EZProxy 账密(校外访问模式)。 ezproxy_proxy_template: 自研门户网关的代理 URL 模板(可选)。变量: {scheme} {host} {port} {domain_reversed} {path} {query} {url} {encoded_url}。 例(yitlink 路径式): https://gw.univ.edu:8444/https/{port}/{domain_reversed}/yitlink{path} ezproxy_cookies: 门户会话 Cookie(用户从浏览器 DevTools 自行复制, 形如 "CLICKIT_SESSION=...; x-yit-token=...")。插件不会自动采集。 elsevier_api_key / elsevier_insttoken: Elsevier TDM API 凭证。 wiley_tdm_token: Wiley TDM 令牌。 report_language: 报告语言 zh | en。 report_format: 报告格式 markdown | html | both | json。 session_follow: 是否默认跟随当前 DSH 会话的 API/模型(默认 true)。 设 false 关闭跟随,回到 agent-default 层(等价于 session_llm.enabled=false)。 session_provider: 自定义 LLM provider 名(DSH settings.yaml 里的 provider 名, 如 mm-api / just);传空字符串清除自定义、恢复自动跟随。 session_model: 自定义模型 id(如 glm-5.3-flash / claude-opus-5); 传空字符串清除自定义、恢复自动跟随。 session_fallback_provider: 唯一备选 provider 名(DSH settings.yaml 里的 provider 名,如 tokenrhythm)。主 Provider 失败时自动切换到它; 传 "none" 或空串清除备选。2026-08-30 起默认链不再自动扫描全部 provider(会话跟随 + 唯一显式备选)。 session_fallback_model: 备选模型 id;留空用备选 provider 的默认模型。 outline_confirm_strict: 大纲确认门禁强化(默认开):大纲生成后 outline_confirm_min_seconds 秒内的执行请求一律退回等待确认。 outline_confirm_min_seconds: 上述时限秒数(默认 8,0 = 关闭)。 acknowledge_scihub_risk: 开启 Sci-Hub 时须显式确认已知悉法律风险。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNo
breadthNo
serpapi_keyNo
report_formatNo
session_modelNo
scihub_enabledNo
scihub_mirrorsNo
scraperapi_keyNo
scraperapi_urlNo
session_followNo
ezproxy_cookiesNo
report_languageNo
scholar_enabledNo
wiley_tdm_tokenNo
elsevier_api_keyNo
ezproxy_base_urlNo
ezproxy_passwordNo
ezproxy_usernameNo
scholar_providerNo
session_providerNo
elsevier_insttokenNo
scraperapi_countryNo
litresearch_enabledNo
scihub_min_intervalNo
ezproxy_proxy_templateNo
outline_confirm_strictNo
session_fallback_modelNo
acknowledge_scihub_riskNo
session_fallback_providerNo
outline_confirm_min_secondsNo
Install Server

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It clearly states the tool writes to ~/.deepseek-litresearch/settings.json and applies changes hot, and it explains important operational details such as defaults, prerequisite flags, and that cookies are not auto-collected. This is well beyond a generic 'updates settings' statement.

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 description is long but appropriately so for 30 parameters. It front-loads the core purpose first and then uses a clear, scannable list. Some entries, such as session_fallback_provider, are verbose, but the extra context is genuinely useful rather than padding.

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?

For a tool with no annotations, no output schema, and zero schema-level parameter descriptions, this description is nearly complete: it covers defaults, constraints, prerequisites, and side effects. The main gaps are the undocumented scraperapi_country parameter and the lack of any explicit statement about the tool's return value or acknowledgment behavior.

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?

Schema description coverage is 0%, and the description compensates strongly by documenting nearly every parameter with defaults, allowed values, and domain-specific meaning (e.g., proxy template variables, fallback behavior, report formats). However, the schema includes scraperapi_country, which is not mentioned anywhere in the description, leaving one parameter undocumented.

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 a specific verb and resource: '修改插件设置' (modify plugin settings) and adds the concrete destination file and hot-reload behavior. This clearly distinguishes it from the sibling get_settings and the research-execution tools.

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

Usage Guidelines3/5

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

The description implies the tool is used whenever plugin settings must be changed, and it documents per-field prerequisites such as acknowledge_scihub_risk. However, it never explicitly says when to use this tool versus alternatives like get_settings, nor does it provide exclusions or caveats about when not to call it.

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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