gemini-deep-research

name

gemini-deep-research

description

Start an asynchronous Gemini Deep Research run with the deep_research tool of the mcp-gemini-search MCP server — an autonomous agent that searches, reads sources, and writes a long citation-rich Markdown report over several minutes. Use this skill whenever the user wants a comprehensive report, competitive analysis, technology comparison, literature survey, market/landscape overview, or any multi-source investigation — even if they just say "research X thoroughly" or "write me a report on Y". Also read it before ever calling deep_research, because a careless call starts a duplicate billed multi-minute run.

deep_research (mcp-gemini-search)

deep_research starts a background Deep Research agent run and returns immediately with {interaction_id, status} — it never waits for the report. The run continues server-side for several minutes and is billed per run. The report is fetched separately with deep_research_result (see the gemini-deep-research-result skill).

The one iron rule

Call deep_research exactly once per research question, and save the returned interaction_id the moment you get it. Re-issuing the same question — to "retry", to "check progress", because the first call felt slow — starts a second full billed run that will also take minutes. Progress is checked only with deep_research_result. The only legitimate reasons to call deep_research again are a genuinely new question, or a deliberate plan-approval / follow-up call that sets previous_interaction_id.

When to use it (and when not)

• Use for questions whose answer is a document: comparisons with tradeoffs, state-of-the-art surveys, due-diligence style investigations.
• Do NOT use it for quick factual lookups — google_search answers those in seconds for a fraction of the cost (see the gemini-google-search skill).
• Confirm with the user before starting a run when the request is ambiguous: a run is minutes of wall-clock time and real money, so "did you want a full research report on this?" is a fair question.

Calling contract

Input (additionalProperties: false):

field	type	meaning
query (required)	string	The research brief.
plan_only	boolean (default false)	Return a research plan for review instead of running the research.
previous_interaction_id	string	Id of an earlier run: approve/refine its plan, or ask a follow-up building on its findings.
agent	string enum	deep-research-preview-04-2026 (faster) or deep-research-max-preview-04-2026 (deeper, slower). Omit to use the server default (GEMINI_DEEP_RESEARCH_AGENT).

Output: {"interaction_id": "...", "status": "in_progress"}. The interaction_id is durable — persist it AND tell the user immediately, before doing anything else. A crashed or interrupted polling loop kills your process state, and the id is the only handle that survives; losing it orphans a billed run.

Write a rich brief

The agent works from your query alone, so treat it as a brief, not a search query. Include:

• Scope: what is in and out ("managed Kubernetes offerings on GCP and AWS, not on-prem").
• Constraints: timeframe, region, language, source preferences ("papers from 2024 onward", "prefer primary sources").
• Desired output shape: "a comparison table plus recommendation", "an annotated bibliography", "an executive summary followed by deep-dives".

Richer briefs produce measurably better reports; a one-line query wastes the run on the agent guessing your intent. When the user's request is terse, do not pass it through verbatim — expand it into a brief yourself, stating the scope and output shape you inferred (and confirm them with the user first when the run is expensive enough to matter).

Plan-review workflow (optional but recommended for expensive questions)

1. Call deep_research with plan_only=true — the run returns a research plan instead of executing.
2. Fetch the plan with deep_research_result and show it to the user.
3. Approve or refine with a second deep_research call that sets previous_interaction_id to the plan run's id and puts the approval ("proceed with this plan") or the corrections in query.

Follow-up questions after a completed run use the same mechanism: set previous_interaction_id so the new run builds on the previous findings instead of starting from zero.

Choosing the agent

Default (omit agent) unless the user signals otherwise. Pick deep-research-max-preview-04-2026 when the user asks for exhaustiveness ("be thorough", "leave no stone unturned") and accepts a longer wait; pick deep-research-preview-04-2026 explicitly when the server default is Max but the user wants a faster turnaround.

Privacy and retention

Background runs require server-side storage (store=true); on the paid tier interactions are retained ~55 days. This differs from the stateless google_search tool — mention it if the research brief contains sensitive material.

Errors

• Invalid arguments for deep_research: … — schema violation; fix the call.
• research query cannot be empty — send a real brief.
• deep research failed: … — the start itself failed; it is safe to retry once (no run was started when there is no interaction_id), then surface the error.