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serpdive

SERPdive

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by serpdive

SERPdive Web Search

serpdive_search
Read-only

Search the live web and return page content with extracted text, ready for LLM use. Supports concise key sentences or full page text for fact-checking and research.

Instructions

Search the live web and get back answer-ready page content, not a list of links. Each result carries the actual text of the page (url, title, date, content), already extracted, cleaned and trimmed for LLM use, so facts can be quoted and cited straight from the response. Use it for anything that needs current or post-training information: news, prices, releases, docs, sports, niche facts. Write the query the way a person would type it, in any language: localization is automatic. The 'mako' model (default) returns the fact-carrying sentences of each page, fast and concise, right for most questions. The 'krill' model is free and unlimited under fair use: the shortest useful answer set (~700 tokens, fewer sources, no written answer), served at low priority — right when token budget matters more than depth. The 'moby' model returns full page text: slower (~1s more) and several times more tokens, so use it only when mako's key sentences are insufficient (long-document analysis, deep research).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNo'mako' (default): the key sentences of each page, concise and fast. 'krill': free and unlimited under fair use — the shortest useful set (~700 tokens, fewer sources, no written answer), at low priority. 'moby': full page text — slower (~1s more) and returns several times more tokens; use only when mako's key sentences are insufficient (long-document analysis, deep research).
queryYesThe search query, in any language, phrased like a real web search.
max_resultsNoMaximum number of results to return (1 to 10). Omit to let the engine pick its calibrated mix.
Behavior5/5

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

Annotation readOnlyHint and openWorldHint are complemented by detailed behavioral disclosure: returns cleaned text, model trade-offs (speed, token count, free tier), and automatic localization. No contradictions.

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?

Front-loaded with core value, then use cases, then model details. Each sentence earns its place; slightly verbose but well-structured.

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?

Despite no output schema, description specifies result fields (url, title, date, content) and model token ranges. Covers query phrasing, model selection, and use cases comprehensively for a search tool.

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 covers 100% of parameters with descriptions. Description adds context on query phrasing ('write as a person would') and max_results omission behavior, enhancing understanding beyond schema.

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 it searches the live web and returns page content (not just links), specifying the unique value of cleaned, LLM-ready text. It distinguishes from typical search tools.

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

Usage Guidelines4/5

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

Provides explicit use cases (current info, news, prices, releases, docs, sports, niche facts) and model selection guidance. Lacks 'when not to use' but no siblings make it less necessary.

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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