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timps_web_search

Search multiple web engines, deduplicate results, and extract content to deliver an LLM-synthesised answer with citations for live lookups, news, vendor docs, and competitive intel.

Instructions

Multi-engine web search (DuckDuckGo / Brave / SerpAPI / Bing) with deduplication, content extraction, and LLM-synthesised answer with [N] citations. Use for live lookups, news, vendor docs, and competitive intel.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestNoPlain-English task or context for the agent.
languageNoPrimary programming language (default: python).python
Behavior3/5

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

With no annotations, the description carries the transparency burden. It mentions deduplication, content extraction, and citation generation, but omits details about data usage, external API calls, or potential limitations. It implies read-only behavior but does not explicitly state it.

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 concise, consisting of two sentences that effectively separate the tool's capabilities from its recommended usage. It avoids redundancy and extraneous detail.

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?

Considering the tool's moderate complexity, the description covers primary features and use cases, including output characteristics (citations). It does not delve into error handling or edge cases, but for a search tool it is sufficiently complete. The mismatch with the 'language' parameter slightly reduces completeness.

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?

The schema provides descriptions for both parameters (request and language), achieving 100% coverage. However, the tool description adds no extra clarification about how these parameters should be used, and the 'language' parameter's meaning is ambiguous given the tool's web search nature. Baseline of 3 applies due to high schema coverage.

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 that the tool performs multi-engine web search with deduplication, content extraction, and an LLM-synthesized answer with citations. It also identifies specific engines, making it unambiguous and distinct from other sibling 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?

The description provides explicit use cases: live lookups, news, vendor docs, and competitive intel. While it doesn't contrast with alternatives, these scenarios give clear guidance on when to employ the tool.

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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