| name | gemini-google-search |
| description | Drive the google_search tool of the mcp-gemini-search MCP server — a Google-Search-grounded Gemini call that returns a Markdown answer with inline [n] citations and a numbered Sources list. Use this skill whenever the user asks to search the web, look up current events, verify a fact against live sources, check the latest version/release/pricing/news of anything, or when any claim needs verifiable citations — even if they never say "search" or "google_search". Also use it to decide when a request should instead go to the deep_research tool. |
google_search (mcp-gemini-search)
google_search runs one Google-Search-grounded Gemini interaction and returns
a Markdown answer with inline [n] citation markers and a numbered
## Sources list. Depending on how the server is registered, the tool may
appear as mcp__mcp-gemini-search__google_search or under another server
alias — match on the trailing google_search.
The tool's own schema documents its parameters (query, url_context,
code_execution) and when to flip each flag — trust it. This skill covers
what the schema cannot: choosing this tool, composing queries, and relaying
results without destroying their verifiability.
Answer vs. report
Use google_search when the user wants an answer: current events, fresh
facts, version/release checks, any claim that needs verifiable sources.
Prefer it over unsourced recall for anything time-sensitive — training data
is stale, the grounded answer is not.
When the user wants a document — a multi-source comparison, survey, or
due-diligence investigation — use the deep_research tool instead (see the
gemini-deep-research skill).
Every query must be self-contained
The server is stateless (store=false): there is no conversation memory, and
the model never sees your previous queries.
- Never write "the library mentioned above" — name it every time.
- Pack the disambiguators into the query itself: exact version, year, product
name, the verbatim error string.
- A miss is normal: refine the query and call again. A second, sharper query
beats guessing — but keep queries purposeful; grounding on Gemini 3 models
is billed per executed search.
Fan out multi-facet questions
One facet per query, issued as parallel calls — not one overloaded query.
"Compare X's and Y's latest releases" is two searches (X's release, Y's
release), not one. Calls are independent, so parallel fan-out costs no
correctness and saves wall-clock time.
Preserve citations when relaying — the step most often skipped
The text's inline [n] markers map to the numbered ## Sources entries,
mirrored in structured content as {index, title, uri} objects. When you
relay the answer, keep the claim→source mapping intact: preserve the markers
with their links, or attach each uri next to the claim it supports. Never
strip the citations and present the text as your own knowledge —
verifiability is the entire point of a grounded search, and it is the first
thing lost when answers get paraphrased.
Errors
Failures come back as tool errors with readable text, not protocol errors —
read the message and self-correct:
Invalid arguments for google_search: … — you violated the input schema;
fix the arguments, never retry verbatim.google search failed: … — backend/API failure; retry once, then surface
the error to the user instead of looping.
