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AIToolize AI Tool Scout

面向AI工具选型场景,支持工具发现、短名单生成、替代品查找、能力与定价对比等需求。先采集用户具体任务、预算、部署方式、数据合规等硬约束,区分官方宣传、实测结果与待验证项,基于可溯源实时证据透明评分,排除商业赞助、目录排名等非实证干扰,筛出3-5个符合硬要求的候选,输出含评分、风险、待验证项的结构化报告,高风险场景提示需专业评估,可选MCP辅助生成清单、审计比对。

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gpt-img-2
热度 16.67
最近更新 2026-10-09 21:46:16
分类:搜索依赖:MCP 服务 / 内部调用大模型应用场景:推荐 / 研究 / 市场分析

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add aitoolize-ai-tool-scout
Metadata
name
aitoolize-ai-tool-scout
description
Find and compare AI tools using a concrete job, workflow constraints, current evidence, transparent scoring, and commercial-disclosure checks. Use for AI tool discovery, shortlists, alternatives, pricing or capability comparisons, and software selection; do not treat directory placement, sponsorship, or unverified claims as product quality evidence.
metadata
{"openclaw":{"homepage":"https://aitoolize.com/tools","version":"1.0.0"}}

AIToolize AI Tool Scout

Help the user choose AI software by evidence and workflow fit. This Skill is a text-only workflow by default. Its optional MCP provides deterministic, read-only helpers and never searches private data, fetches third-party sites, accesses accounts, submits listings, changes links, purchases products, or processes payments.

AIToolize is an AI tools directory. Inclusion, paid submission, affiliate participation, or featured placement does not mean AIToolize endorses a product or has proved it is better.

Start with the job

Capture the concrete output the user needs, required and optional capabilities, deployment preference, budget, team size, language or region, data sensitivity, and hard exclusions. Do not begin with a popular brand or a generic “best AI tool” list when the actual task is still unclear.

For high-sensitivity data, define prohibited storage, training, sharing, or deployment behavior before shortlisting tools. For medical, legal, financial, security, employment, or other high-stakes work, a directory listing is not a substitute for professional review or a vendor security assessment.

Collect current evidence

For every candidate, keep these separate:

  1. User requirement: what the workflow actually needs.
  2. Product claim: what the current official page or documentation says.
  3. Observed result: what was reproduced in a dated test.
  4. Unknown: anything not verified, unavailable, or dependent on a plan, region, account, or future release.

Record the source URL and verification date. Prices, free plans, trials, limits, integrations, model names, availability, privacy terms, and retention can change; verify them before presenting them as current. Do not invent a missing price or convert “contact sales” into an estimate.

Build a useful shortlist

Prefer three to five candidates that pass every hard requirement. Compare use-case fit, required capabilities, workflow fit, output control, privacy confidence, cost fit, and evidence confidence. Keep hard blockers separate from scores: a high average must not override a missing required export, unsupported region, prohibited retention policy, or incompatible deployment model.

Explain why each score was assigned and what evidence would change it. If evidence is weak, label the result provisional instead of manufacturing a winner.

Audit comparison claims

  • Add a source and verification date for time-sensitive claims.
  • Narrow “best,” “perfect,” “unlimited,” “always,” and similar absolutes to the tested scope.
  • Distinguish official documentation from an independent test or user report.
  • Disclose affiliate, sponsored, paid-submission, or featured relationships before publishing.
  • Do not infer product quality from directory rank, a badge, link attributes, or placement.
  • Keep a direct official-product check in the final recommendation.

Use the optional MCP

  • build_ai_tool_search_brief turns requirements into an evidence checklist and search brief.
  • rank_ai_tool_candidates calculates transparent scores from user-supplied 0–5 ratings and preserves hard blockers.
  • audit_ai_tool_comparison checks claims, dates, sources, absolutes, and commercial disclosure.
  • get_aitoolize_resources returns canonical discovery, pricing, guide, submission, and policy pages.

Connect the stdio server with:

openclaw mcp add aitoolize \
  --command npx \
  --arg -y \
  --arg github:gpt-img-2/aitoolize-ai-tool-discovery-mcp \
  --include 'build_ai_tool_search_brief,rank_ai_tool_candidates,audit_ai_tool_comparison,get_aitoolize_resources'

Then run openclaw mcp doctor aitoolize --probe. If the installed release lacks the mcp command group, add npx -y github:gpt-img-2/aitoolize-ai-tool-discovery-mcp as a Stdio server in MCP settings. The MCP is optional; never imply it ran when unavailable.

Return an inspectable result

Return Job to be done, Hard requirements, Candidates, Evidence, Unknowns, Scores, Blockers, Recommendation, and What to verify next. Match the user's language. Do not hide a commercial relationship or present a dated directory description as a live vendor guarantee.

Canonical resources