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research

Read-only

Search the web, fetch top results, and return a research brief with source references in one call. Ideal for open-ended questions requiring both search and reading.

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

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Beyond annotations (readOnlyHint, openWorldHint), the description explains caching behavior with max_age_hours, how depth affects fetches, the meaning of fetch=False, and the two output formats. It adds rich behavioral context without contradicting annotations.

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?

Every section earns its place: purpose, best/not recommended, returns format, common mistakes, then args. It is well-organized and front-loaded with the most important information, with no wasted sentences.

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 13 parameters, 1 required, and the presence of an output schema, the description covers all behavioral aspects: input details, output format, caching, and edge cases like force-refreshing. It is fully complete for a tool of this complexity.

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?

The 'Args' section adds complete meaning for all 13 parameters, including defaults, valid ranges (depth 1-8), behavioral nuances (max_age_hours honor change), and enum values (format). Since schema descriptions are missing, this fully compensates.

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 starts with a specific verb-resource pair: 'One-shot research: search the web, fetch the top results, return both.' It clearly distinguishes this tool from siblings like search, fetch, and cache_search by explaining what it does 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 lists 'Best for', 'Not recommended for', and 'Common mistakes'. It gives concrete examples of when to use research vs alternatives like search, fetch, and cache_search, providing excellent guidance.

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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