Exa Deep Search
Turn Exa search into a focused, source-backed research brief. This Skill calls the named Exa capabilities in the SandBase API map through the SandBase MCP gateway. In a SandBase Agent, run the capabilities directly. In another compatible agent, require an authorized SandBase connection before starting; never request, print, or store an API key in the research output.
Read example workflows when the user needs a starting prompt or wants to understand the output.
Operating principles
- Start from the user's research question and decision context, not a generic search.
- Treat Exa results as evidence; treat model-generated synthesis, comparisons, and recommendations as judgment clearly separated from sources.
- Select search depth, time window, domains, and geography deliberately. State any assumption rather than silently defaulting.
- Optimize for source quality, recency, and relevance — not quantity.
- Cite every externally verifiable claim with a result URL and publication date (when available).
- Keep user research goals, company context, and strategy confidential unless sharing is explicitly requested.
Workflow
1. Frame the research question
Collect or infer: the topic or entity, time window, geography, trusted or excluded domains, audience for the deliverable, and how findings will be used. Classify the request as one or more of: landscape scan, deep evidence gathering, competitive intelligence, current news monitoring, or specific-source extraction.
When the research question is broad, propose 2–3 focused sub-queries and confirm scope before spending API calls.
2. Select and call SandBase capabilities
Read the SandBase API map before selecting tools. Use the listed tool_name through the SandBase gateway:
- Call
sandbase_describe_toolfor the selectedtool_nameand read its current input schema. - Call
sandbase_call_toolwith that exacttool_nameand only schema-defined arguments. - Keep the tool name, query, search parameters, and result metadata with the returned data.
3. Search with Exa
Use exa_search with parameters matched to the research need:
| Research need | Recommended parameters |
|---|---|
| Current landscape | topic: "news", bounded start_published_date/end_published_date, include_highlights: true |
| Deep evidence | search_depth: "advanced", include_summary: true, request full text only for selected sources |
| Trusted sources only | include_domains for first-party, academic, or approved publishers |
| Competitive research | exclude_domains for the target's own site; separate queries per competitor |
| Validation or quick check | search_depth: "basic", num_results: 3–5 |
Tips:
- Write queries as natural-language statements of what a good result page would say, not short keyword strings. Exa responds best to semantic queries.
- Use
categorywhen available (e.g.,"research paper","company","news") to narrow result types. - Iterate: refine by entity, product, problem, event, or time period until evidence is sufficient.
- Request
include_highlights: trueto get relevant snippets without extracting full text for every result.
4. Extract selected sources
When deeper analysis of specific pages is needed, send selected URLs to exa_contents:
- Choose
include_text: truefor full page content when analyzing structure or extracting data. - Choose
include_highlights: truewith ahighlights_queryto focus extraction on specific aspects. - Choose
include_summary: truefor concise overviews when reviewing many pages. - Use
subpagesonly for explicit documentation, pricing, or API crawl tasks. - Use
max_age_hours: 0only when freshness requires a live crawl; avoid for routine research.
If exa_contents is not yet available in the current Gateway, return the Search results and explicitly state that extraction is awaiting capability publication.
5. Synthesize findings
- Separate direct observations from interpretation.
- Group findings by theme, entity, or chronology as appropriate for the research question.
- Note disagreements between sources and evidence gaps.
- Propose follow-up queries for unresolved questions.
Query crafting tips
Good Exa queries describe the content of the ideal result page:
| Poor query | Better query |
|---|---|
AI agents | How enterprises evaluate AI agent platforms for production deployment |
observability tools | Comparison of AI agent observability and tracing solutions 2025 |
competitor pricing | Pricing page for enterprise AI agent orchestration platform |
- Add temporal context: "in 2025", "since January", "latest announcement".
- Add specificity: mention the industry, company size, technology stack, or use case.
- Use
exclude_domainsto avoid results you already know about.
Output
Return a structured research brief:
Source map
| # | Title | URL | Published | Relevance |
|---|---|---|---|---|
| 1 | ... | ... | ... | ... |
Key findings
Numbered findings, each citing source(s) by number.
Disagreements and evidence gaps
What sources disagree on, and what questions remain unanswered.
Suggested next queries
Follow-up Exa queries or alternative research paths.
Evidence rules
- Cite a result URL for every externally verifiable claim.
- Label a result's publication date as "unavailable" when Exa does not return one.
- Do not treat an Exa summary as a source quote; use it as an aid to select evidence, then cite the original URL.
- Do not call Exa Answer or Exa Agent endpoints. The user's Agent/LLM synthesizes the evidence.
- Do not copy long source passages; paraphrase and cite.
- Mark clearly when a finding is inferred from multiple sources vs. directly stated in one.
Failure handling
- If SandBase is unavailable or unauthorized, report the failed capability and ask the user to connect or authorize SandBase; do not silently substitute a direct provider API.
- If
exa_searchreturns few or no results, try: broader query, differentsearch_depth, removed domain filters, or a wider date range. Report if the topic genuinely lacks public coverage. - If
exa_contentsis unavailable, deliver search results with highlights and explicitly note the extraction gap. - If results are low-quality or off-topic, refine the query before reporting; explain what was tried.
Example tasks
- "Find the last 30 days of reliable sources about AI agent observability. Give me a five-source brief with gaps."
- "Research how enterprise teams evaluate AI agents. Prefer company and academic sources; exclude vendor blogs."
- "Compare the public arguments for and against a retrieval architecture. Use advanced search and cite each source."
- "Find recent funding announcements in the AI developer tools space. Only include sources from the last 7 days."
- "Extract the pricing and feature comparison from these three competitor pages: [URLs]."
Quality gate
Before delivering, verify that:
- Every finding cites at least one source URL.
- Observations are separated from model-generated interpretations.
- The search parameters (depth, dates, domains) match the stated research need.
- Evidence gaps and low-confidence findings are explicitly labeled.
- The deliverable format matches what the user requested.
