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by mnthe

reference_search

Answer a research question using live web sources, returning a synthesized answer with organized citations and source links.

Instructions

AI-assisted reference search: answer a question from live web sources using Gemini's Google Search grounding, and return organized citations. Unlike the OpenAI-spec 'search'/'fetch' connector tools, this composes a synthesized answer AND returns the source links plus claim->source supports (citations) in one call. Returns: answer (synthesized text), citations (deduped {index,title,uri,domain} sources), supports (answer segments mapped to citation indices with confidence scores), searchQueries (the queries the model actually ran), and searchSuggestionsHtml (Google's required Search Suggestions markup to display alongside the answer). Search-scope tuning is backend-specific: Vertex AI supports excludeDomains (skip up to 2000 domains) and blockingConfidence (block risky/low-quality sites); Google AI Studio supports timeRange (restrict to a publish-time window) and grounding on explicit urls via URL context (Gemini API only, not Vertex AI). Both backends support includeImages.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsNoSpecific http(s) URLs to ground the answer on via URL context (max 20). Google AI Studio backend only; the URL context tool is not available on Vertex AI.
modelNoOptional Gemini model override; must support Google Search grounding (default: server model).
promptYesResearch question or topic to answer from live web sources.
timeRangeNoRestrict results to a publish-time window for recency tuning (both fields required). Google AI Studio backend only.
includeImagesNoAlso enable image-search grounding in addition to web search.
thinkingLevelNoOptional Gemini 3 thinking level override for the reasoning depth of the answer.
excludeDomainsNoDomains to exclude from results, e.g. ['reddit.com','pinterest.com'] (search-scope tuning; max 2000). Vertex AI backend only.
systemInstructionNoOptional system instruction to steer the tone, depth, or scope of the composed answer.
blockingConfidenceNoBlock risky/low-quality sites at or above this confidence ('low' is most aggressive). Vertex AI backend only.
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It exhaustively discloses return fields (answer, citations, supports, searchQueries, searchSuggestionsHtml) and backend-specific behaviors for parameters like excludeDomains, blockingConfidence, timeRange, and URL context constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense paragraph. It is front-loaded with purpose and all information is relevant, but it could be slightly more scannable with bullet points. Nonetheless, every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description thoroughly explains all return fields and their structure. It covers both backends, parameter constraints, and required markup. The tool is complex (9 params, nested objects), and the description fully informs the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

While schema coverage is 100%, the description adds crucial context beyond schema: it explains backend-specific availability for parameters like urls, timeRange, excludeDomains, blockingConfidence, and the meaning of includeImages. It also details the behavior of each parameter beyond the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'AI-assisted reference search: answer a question from live web sources using Gemini's Google Search grounding, and return organized citations.' It distinguishes itself from sibling tools like 'search'/'fetch' by noting it composes a synthesized answer and returns structured citations in one call.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly contrasts this tool with 'search'/'fetch' connector tools, explaining that this composes a synthesized answer with citations. It also provides backend-specific guidance for Vertex AI vs Google AI Studio, helping the agent choose appropriate parameters.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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