agent-web-search

web_search

Answer natural-language questions by searching the web across multiple providers and returning normalized, grounded results. Pass full questions, not keyword fragments, to get grounded evidence.

Instructions

Search the web using agent-native semantic search and LLM-grounding backends. Optimized for complete natural-language questions rather than traditional keyword queries. Enabled providers: DuckDuckGo web search; Exa web search; Parallel LLM-optimized web search. Use a complete natural-language question. Failed providers are omitted from successful responses. If every enabled provider fails, the call returns a tool error with code all_providers_failed and per-provider diagnostics.

Input Schema

TableJSON Schema

Name	Required	Description
`query`	Yes	A complete, detailed natural-language question or intent. Do NOT use fragmented keyword queries (e.g. avoid 'NVDA earnings'); instead pass the full question (e.g. 'What were the key highlights and market reactions to NVIDIA latest earnings?'). Model-native and semantic providers reason over full sentences to retrieve, read, and synthesize grounded evidence.
`providers`	No	Optional subset of the providers enabled at startup.
`time_range`	No	Optional time filter: past day, week, month, or year.
`max_results`	No	Desired maximum number of results. Providers enforce this natively or as a best-effort prompt constraint.

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv0.8.0Oct 1, 2026

changedInput schema / properties / max_results / default
```
Previous value: -10New value: +5
```

changedInput schema / properties / query / description

Previous value: -"A complete natural-language search question."New value: +"A complete, detailed natural-language question or intent. Do NOT use fragmented keyword queries (e.g. avoid 'NVDA earnings'); instead pass the full question (e.g. 'What were the key highlights and market reactions to NVIDIA latest earnings?'). Model-native and semantic providers reason over full sentences to retrieve, read, and synthesize grounded evidence."

Changed3 schema fields changedv0.7.2Sep 3, 2026

removedInput schema / properties / max_keyword

Removed value: -{
-  "default": 3,
-  "description": "Desired maximum number of distinct search queries or keywords. Providers without an equivalent control ignore it.",
-  "maximum": 10,
-  "minimum": 1,
-  "type": "integer"
-}

changedInput schema / properties / max_results / description

Previous value: -"Desired maximum number of results or cited sources. Providers enforce this natively or as a best-effort prompt constraint."New value: +"Desired maximum number of results. Providers enforce this natively or as a best-effort prompt constraint."

addedInput schema / properties / query / maxLength
```
Added value: +4000
```

Changed1 schema field changedv0.3.0Aug 26, 2026

changedInput schema / properties / providers / items / enum

Previous value: -[
-  "ark",
-  "ddgs",
-  "exa"
-]New value: +[
+  "ddgs",
+  "exa",
+  "parallel"
+]

First observedv0.1.0Aug 21, 2026

TDQS

A4.1/5.0

Behavior4/5

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

No annotations are present, so the description carries the full burden, and it delivers real behavioral detail: failed providers are silently dropped from successful responses, and total failure surfaces as a tool error with code all_providers_failed plus per-provider diagnostics. It does not disclose result ordering, latency, rate limits, or the shape of successful results.

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 purpose and provider list, then failure semantics, with every sentence carrying content. Slightly dense in the middle where the natural-language guidance is repeated from the schema, which is mild redundancy rather than padding.

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?

With no output schema, the description would ideally explain the return structure; it hints at aggregation ('omitted from successful responses') and error diagnostics but never describes what a successful result contains. For a multi-provider aggregation tool this is a modest gap, offset by unusually thorough failure-mode disclosure.

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

Parameters3/5

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

Schema description coverage is 100%, so all four parameters are already documented, including the natural-language versus keyword guidance and the providers enum. The description largely restates what the schema's query description already says, adding the provider names but no new parameter-level semantics. Baseline 3 is appropriate.

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?

States a specific verb+resource ('Search the web') and immediately qualifies it with the mechanism (agent-native semantic search and LLM-grounding backends) plus the three concrete providers enabled (DuckDuckGo, Exa, Parallel). An agent knows exactly what this does without opening the schema.

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?

Gives an explicit usage instruction repeated twice ('Use a complete natural-language question', 'Optimized for complete natural-language questions rather than traditional keyword queries'), which steers invocation strongly. There are no sibling tools, so no when-not-to-use or alternative routing exists to state; the only gap is that exclusions are impossible to provide.

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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Other Tools

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