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Search and read in one call

research
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Answer open-ended questions in one step: search the web, fetch top results, and get a citable research brief with sources.

Instructions

One-shot research: search the web, fetch the top results, return both.

Best for:
- Open-ended questions that need finding sources AND reading them
  ("what's new with X", "summarize the controversy around Y").
- Replacing a `search` + N x `fetch` chain with one call.
- Producing a citable brief with [n]-style source references.

Not recommended for:
- You only need links -> `search` (cheaper, no fetching).
- You only need to read one URL you already have -> `fetch`.
- You want to query previously-fetched cached pages -> `cache_search`.

Returns:
- markdown (default): a "Research brief" with a Sources index then the
  full Markdown body of each fetched document, separated by horizontal
  rules; includes a token estimate.
- json: {question, engines, sources:[{rank,title,url,snippet,...}],
  documents:[...], tokens_estimated, errors}.

Common mistakes:
- Using `depth=8` for a quick lookup — that's 8 page fetches; 2-3 is
  almost always enough.
- Calling `research` for a known URL — that's `fetch` territory.
- Forgetting that `fetch=False` returns sources only (much cheaper if
  the LLM only needs to pick which one to read).

Args:
    question: What you want to know, in natural language.
    depth: How many top results to fetch (1-8). 3 is a good default.
    engines: Override the engine set (see `engines()` for names).
    fetch: If False, return source list without reading them.
    use_cache: Reuse cached search/page data within TTL.
    max_age_hours: Treat cached search results AND cached page bodies older
        than this as a read miss; fresh data is always written back. 0 =
        force-refresh both the engine search and every fetched page body;
        None = server default TTL (7 days). A non-zero value is honored for
        both halves (it used to be ignored for anything but 0).
    format: "markdown" or "json".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNo
fetchNo
formatNomarkdown
enginesNo
categoryNo
questionYes
freshnessNo
use_cacheNo
exclude_textNo
include_textNo
max_age_hoursNo
exclude_domainsNo
include_domainsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Beyond the spartan annotations, the description discloses cache write-back behavior, TTL semantics for `max_age_hours`, return format details, token estimation, and failure-prone usage patterns. It even notes a historically surprising behavior (non-zero `max_age_hours` now applies to both search and pages), adding real transparency.

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

Conciseness5/5

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

The description is long, but it is highly structured with scannable headings, bullets, and a clear linear flow. Every section adds distinct value: purpose, use cases, exclusions, return format, common mistakes, and parameter details. The length is justified by the tool's complexity and parameter count.

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

Completeness4/5

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

Given the tool's complexity, the description covers purpose, usage boundaries, output formats, and parameter behavior well, and the output schema exists to formalize return values. Still, the description omits several parameters (e.g., `category`, `freshness`, domain filters), which leaves some operational gaps for an agent trying to use the full feature set.

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

Parameters4/5

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

Schema description coverage is 0%, so the description carries the burden. It explains `question`, `depth`, `engines`, `fetch`, `use_cache`, `max_age_hours`, and `format` with actionable detail, but leaves `category`, `freshness`, `exclude_text`, `include_text`, `exclude_domains`, and `include_domains` unexplained. The covered parameters are handled very well, but the six omitted ones are a clear gap.

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 states a specific verb+resource: 'search the web, fetch the top results, return both.' It clearly distinguishes this tool from siblings by framing it as a combined search-and-fetch operation, with the title 'Search and read in one call' reinforcing the purpose.

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 provides explicit 'Best for' and 'Not recommended for' sections, naming concrete alternatives: `search`, `fetch`, and `cache_search`. It also includes common mistakes and specific scenarios, giving an agent clear decision criteria for when to use this tool versus siblings.

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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