AI Agents & RAG
Give your AI agent relevant web sources
Find pages with Google Search, read public Twitter updates, and use JEV to judge which material answers the question. Connect through API or MCP.
- AI agents
- Research assistants
- MCP integration
Which new features were announced for these headphones this week?
Pass this week’s announcement and its source link to the language model.
Use cases
Find the source. Select the context. Keep the citation.
Discover material beyond the prompt
Let the agent search for relevant pages or read a selected account’s public posts. Titles, snippets, and source links help it decide what to investigate next.
Filter for the question at hand
Use JEV to classify or score candidate passages against your own criteria. For a question about this week’s release, an old review need not take up context space.
Make the answer traceable
Pass the selected text and its source link together to your language model. Your application can then associate the answer with the material it used.
WORKED EXAMPLE
Give a product research assistant the right context
A user asks, “What changed in this product’s latest release, and does it affect our integration?” The answer needs recent public information as well as the user’s own requirements. Your agent can split those needs before collecting material.
Use search to discover release information and a selected public account’s timeline to find related announcements. Retrieve the full source separately when the snippet is insufficient. Then ask JEV whether each candidate passage addresses the release or requirement in question.
| Material | Question to evaluate | How the app uses it |
|---|---|---|
| Current release announcement | Does it describe this release? | Include as release context |
| Older comparison article | Does it address the current change? | Keep only relevant background |
| Internal integration notes | Which requirement does this affect? | Compare with the public update |
Your language model receives the selected passages and source links, alongside internal notes from your own system. It composes the answer; Sukidata provides the search, public timeline, and JEV evaluation tools.
- RETRIEVAL
Find candidate material
- Current release notes
- Older product review
- Your integration notes
- JEV
Evaluate relevance
- Matches the release?
- Addresses the feature?
- Applies to this integration?
- YOUR LANGUAGE MODEL
Compose with sources
- Selected passages
- Internal requirements
- Answer with source links
How to build it
Connect web retrieval to your AI workflow.
- 01
Find sources
Search for relevant pages and public account posts through the API or MCP. Keep the source URLs and dates.
- 02
Select useful context
Fetch full pages separately where needed. Use JEV to score candidate passages against the user's question.
- 03
Compose a sourced answer
Pass selected material to your language model and include the source links in the answer.
DESIGN THE RESEARCH TASK
Make retrieval serve the question being asked
Separate discovery from evidence gathering
Search is useful when the agent does not yet know which page to read. A product name, release identifier, or feature can lead it to a shortlist of sources. Titles and snippets help prioritize that shortlist without treating every search result as equally useful.
Once a source is selected, collect enough of its content to support the answer. For a detailed comparison, this may mean retrieving full pages with your existing tools. Keep the URL attached to the text as it moves from retrieval to selection and generation.
Give relevance a specific meaning
A passage can mention the right product and still fail to answer the question. Define relevance in terms of the user’s task: the requested release, the feature under discussion, the applicable region, or the period being researched.
JEV can evaluate those criteria as separate questions. A choice can classify the subject; a score can express how directly the text addresses it. Your application decides which results to include and can send several useful passages to the language model rather than relying on a single match.
Support more than a one-off chat answer
The same retrieval steps can feed a product comparison assistant, an account research brief, or an internal knowledge assistant that needs public context. Pair external sources with the documents your application already has instead of expecting web search to supply internal facts.
Use the API when the sequence belongs in your application code, or connect a compatible agent through MCP. In either case, keep the final deliverable specific: a sourced comparison, a summary of announcements, or an explanation tied to the user’s requirements.
FAQ
AI Agents & RAG questions.
What can an AI tool do through MCP?
It can find Google Search results and read public Twitter posts to support research, compare information, and prepare summaries with source links.
Do I need to host an MCP server?
No. Sukidata hosts and maintains the MCP server. Connect a compatible AI tool by following our setup guide.
Can I filter retrieved material before generating an answer?
Yes. Use JEV to score candidate passages against the user's question, then let your application select which passages to provide to its language model. Search snippets help discover sources; retrieve full page content separately when needed.
Can an agent also create images?
Yes. Sukidata's image tools let an agent generate images from a prompt or edit reference images with GPT Image 2.5 or GPT Image 2, alongside its search and Twitter research tasks.
Get started
Connect Sukidata to your AI tools.
Bring search results and public posts into your next AI-powered research project.