If the big model is responsible for "thinking", then search tools are responsible for giving AI the ability to "see the world."
When using AI Agents such as OpenClaw and Claude Code, a very real problem is that the knowledge acquired by the model itself does not equal real-time Internet information. When we need to study the latest industry trend, analyze competitors, find technical documents, or even verify a piece of news that just happened, the knowledge of the model itself is often not enough.
this is also tavily-search-pro The value of this kind of Skill.
According to CocoLoop's skills page, tavily-search-pro is an AI search and research Skill built based on Tavily's official API, integrating capabilities such as Search, News, Finance, Extract, Crawl, Map and Research into one tool.
1. What problems did it solve?
Traditional search solves "help me find web pages".
What AI Agents really need is:
Find information → understand information → extract information → organize information → complete research based on multiple sources.
There is a clear difference between the two.
For example, when you ask an Agent to investigate "Development Trends of the AI Agent Market in 2026", ordinary search can only provide a batch of web pages, while an Agent with research capabilities needs to further complete:
- Search for the latest information;
- Screening for reliable sources;
- Obtain the body of the web page;
- Cross-validation of different sources;
- Extract key data;
- Summarize different views;
- Eventually, a structured report was formed.
The goal of tavily-search-pro is to integrate several of these steps.
Therefore, it is not just a "search Skill", but closer to an information acquisition and research infrastructure for AI agents.
Two, seven capabilities, covering the complete link from search to research
1. Search: Let Agents Search the Internet
Search is the most basic and commonly used ability.
It supports basic and advanced search models, provides control capabilities such as time filtering, domain name restrictions, and country and region preferences, and can also return search results suitable for large model processing.
This means that you can let the Agent not only search for "what's there", but also further qualify:
- Only look at the recent information;
- Search only designated websites;
- Exclusion of certain domain names;
- Conduct searches for specific countries or regions;
- Adjust the quality of results based on search depth.
For AI Agents that require real-time information, these control capabilities are very important.
2. News: Quickly track news and industry trends
If the task is to track news, ordinary web searches often require themselves to determine which results are news.
The News mode is optimized for news scenarios, which can help Agents locate timely information faster.
For example:
"Organize important news from the AI Agent industry in the past week."
Agents can use news search to obtain information, and then further summarize and analyze it.
These abilities are particularly suitable for:
- Industry intelligence;
- Public opinion monitoring;
- Enterprise dynamics;
- Science and technology news;
- Market trend tracking.
3. Finance: Focus on financial and market information
For investment research, corporate analysis and market research, financial information has obvious vertical attributes.
Tavily-search-pro provides a Finance search model that can search for financial related content.
For example:
"Study a company's business performance in the most recent quarter and compile relevant public information."
Compared with letting the Agent screen financial information on a large number of ordinary web pages, a specialized financial search model can reduce the cost of information screening.
Of course, it is more suitable as a source of information during the research process, rather than directly replacing professional financial data terminals or investment decision-making systems.
3. Extract: Don't just look at the search results, read the web page directly
This is a much-needed ability for AI Agents.
Search results usually only tell you:
"This page may have the information you need."
But the truly valuable content is often hidden in the body of the web page.
Extract can obtain readable content from a specified URL, and supports output in forms such as Markdown or Text. It can also combine query terms to correlate content.
This is particularly useful for the following scenarios:
- Read technical documentation;
- Extract research reports;
- Organize web page information;
- RAG data preprocessing;
- Analyze designated articles;
- Find specific information from web pages.
Simply put:
Search is responsible for finding the information, and Extract is responsible for really getting the information back.
4. Crawl: Upgrade from "reading a page of a web page" to "studying the entire website"
If Extract solves a single-page problem, then Crawl is aimed at site-level information acquisition.
For example, a technology project has dozens or even hundreds of document pages.
If you open it one by one manually, it will not only take time, but it will also be difficult to ensure integrity.
Crawl can be crawled recursively starting from the root URL and supports controls such as depth, number, path inclusion and exclusion.
This makes it particularly suitable for:
Technical Documentation Research
For example:
"Analyze the official documentation of this project and organize its APIs, installation methods and core functions."
competitive product analysis
For example:
"Grab product introductions, function pages and pricing information from competitors 'official websites and organize them into competing product reports."
Organization of website content
For example:
"Classify the main content in this knowledge base by theme."
For Agents, this ability means that the research object is no longer limited to "one article", but can be extended to the entire information system.
5. Map: First find out what a website has
Many times, before an Agent starts crawling a website, he first needs to know:
"What pages are there on this website?"
Map solves this problem.
It helps Agents quickly discover the URL structure of a website and generate a site map.
This step may seem simple, but it is very important for large websites.
A reasonable information acquisition process can become:
Map → Find website structure → Crawl → Batch crawl → Extract → Extract → Extract key content → Research → Form report.
Compared with blindly grasping at the beginning, this method is more suitable for structured research.
6. Research: Really turn "search" into "research"
If the previous functions solve the problem of information acquisition, then Research is one of the most valuable capabilities of the entire Skill.
The Research model can automatically collect multiple sources based on research questions and generate structured research reports with citations. It also provides different model choices such as mini, pro, and auto.
This means that users do not necessarily need to design a complete search process themselves.
For example, you can directly ask:
"Study the current development of AI Agents in the field of enterprise office automation, focusing on analyzing main application scenarios, representative products, market trends and potential risks."
Agents can carry out multi-source information retrieval around this issue and then carry out comprehensive organization.
The biggest difference between this and ordinary search is:
The endpoint of search is a list of results, while the endpoint of Research is a research result.
7. Why is it particularly suitable for AI Agents?
Traditional search tools are designed for "people".
After people see more than a dozen search results, they can judge for themselves:
- Which website is trustworthy;
- Which article is important;
- What information is worth reading;
- Are there any contradictions between different sources?
AI agents require more structured tools.
The advantage of tavily-search-pro is precisely that it designs actions such as search, extraction, crawling, and research to be suitable for Agent calls.
Therefore, it is particularly suitable for the following types of tasks.
1. in-depth research
Examples include market research, industry analysis, and technology trend analysis.
2. RAG data preparation
Obtain external knowledge through search and Extract, and then enter the subsequent data processing process.
3. competitive product analysis
Obtain public information from competitors in bulk through Search, Crawl and Extract.
4. News monitoring
Use News capabilities to track industry dynamics.
5. technology research
Search official documents, GitHub projects, technical articles, and then read in depth through Extract or Crawl.
6. content creation
For article creation that requires fact verification and multi-source data, the cost of early data collection can also be significantly reduced.
The CocoLoop page also lists knowledge workers, researchers, analysts, product managers, marketers, developers, technical writers, and content creators as the main applicable groups.
8. Its advantages are not just "stronger search"
Overall, this Skill has several obvious characteristics.
First, concentration of multiple capabilities
Search, page extraction, website crawling, site maps and in-depth research are integrated without the need to switch frequently between tools for different tasks.
Second, the control parameters are rich
Parameters such as time, domain name, search depth, and region can help Agents narrow the scope of information and improve research efficiency.
Third, it is suitable for machine processing
It is not only for human reading, but also takes into account the needs of AI Agents for structured information.
Fourth, Research has its own citation ideas
For research-based tasks, where the information comes from is very important.
Research results with sources are easier to verify than "AI summaries" without sources.
9. Are there any shortcomings?
Of course there is.
The CocoLoop page also clearly lists a number of limitations.
1. cost problem
Advanced search and Research models consume more API credits.
If Agents conduct in-depth research at high frequency, costs may increase rapidly.
Therefore, a more reasonable strategy is not to "use Research for all problems", but to:
Use Search for simple questions, Extract for key web pages, Crawl for site-level tasks, and Research for complex research.
2. Internet dependence
This Skill relies on the Tavily service and is not itself a completely offline search system.
If there is a problem with the network or third-party service, the Agent's search capabilities will also be affected.
3. privacy issues
Search requests and URL content need to be sent to third-party services.
Therefore, if you are dealing with internal corporate secrets, sensitive personal information or undisclosed commercial information, special attention needs to be paid to data compliance issues.
The CocoLoop page also specifically reminds that during use, you need to pay attention to the privacy and compliance risks caused by uploading query content to the Tavily service.
4. API Key Security
The Skill relies on TAVILY_API_KEY。
API keys should not be written directly into code warehouses, logs, or public configuration files, otherwise credentials may be leaked.
10. A more reasonable AI Agent search workflow
If you put this Skill into a complete Agent workflow, you can form this pattern:
Step 1: Determine the problem
Clarify research goals rather than directly conducting aimless searches.
↓
Step 2: Search
Find relevant pages, news and materials.
↓
Step 3: Filter sources
Priority is given to official documents, authoritative organizations, raw data and high-quality media.
↓
Step 4: Extract
Read the complete content of key pages.
↓
Step 5: Map / Crawl
If the research object is a complete website, further analyze the website structure and crawl it in batches.
↓
Step 6: Cross-verification
Find multiple independent sources to verify important facts.
↓
Step 7: Research
For complex problems, let AI combine multiple sources to generate structured research reports.
↓
Step 8: Manual review
Conduct a final inspection of key data, conclusions and citations.
This is the real value that AI search should play.
11. Who is most worth installing this Skill?
If you only ask AI occasionally:
"What is a Transformer?"
Then this Skill may not be necessary.
But if your work often involves:
- Check information;
- Write research reports;
- Conduct competitive product analysis;
- Track industry news;
- Read technical documentation;
- Collect content materials;
- Analyze companies and markets;
- Build RAG;
- Let Agents complete Internet research independently;
Then the value of tavily-search-pro will be very obvious.
What it really changes is not whether AI can search, but:
Can AI move from search to research?
12. Conclusion: Search is the basic ability of AI Agents
Future AI Agents will not just stop at "answering questions."
A truly productive Agent needs to be able to:
Discover information → obtain information → understand information → verify information → synthesize information → form conclusions.
What tavily-search-pro does is to provide a relatively complete set of information acquisition tools for this link.
From Search to Extract, from Crawl to Map, to Research, it integrates the originally scattered Internet information acquisition actions, allowing Agents to transform from a "model that only answers questions based on existing knowledge" to a smart assistant that can proactively search for information, read information, and complete research work.
Of course, it does not guarantee that all search results are correct, nor does it replace human judgment. Especially in high-risk areas such as finance, medical care, and law, AI output still requires manual verification.
But for knowledge workers who need a lot of Internet information processing, the significance of this kind of Skill is very clear:
Let AI not only "know a lot", but can truly "find answers."
If you are building OpenClaw or other AI Agents and configuring search capabilities as infrastructure, tavily-search-pro deserves to be a key skill to consider.
CocoLoop currently also lists it as an editor's choice Skill and provides an installation and use portal.