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novada_research

Read-onlyIdempotent

Automates multi-source research by running parallel searches across Google, Bing, and DuckDuckGo, extracting full content, and synthesizing a cited report for complex questions.

Instructions

The most powerful research tool in any MCP server. One call → 3-10 parallel searches across Google/Bing/DuckDuckGo → dedup → extract full content from top 5 sources → synthesized cited report. No other MCP server can do this.

Use for: Any complex question needing multiple sources. Comparative analysis, market research, technical deep dives, competitive intelligence. Replaces 5-10 manual search+extract calls. Not for: Single fact lookup (novada_search) or reading one URL (novada_extract). Depth: "quick" (3 queries), "deep" (5-6), "comprehensive" (8-10), "auto" (default). Key advantage: Agents call this ONCE instead of orchestrating search→extract→synthesize manually. Saves tokens, time, and complexity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionNo
queryNoAlias for 'question' — use either
depthYes'quick'=3 searches, 'deep'=5-6, 'comprehensive'=8-10, 'auto'=server decides based on question complexity.auto
focusNoOptional focus area to guide sub-query generation. E.g. 'technical implementation', 'business impact', 'recent news only'.
Behavior5/5

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

Annotations already declare readOnlyHint, destructiveHint, idempotentHint. Description adds concrete details: 3-10 parallel searches, dedup, top 5 sources, synthesized cited report, depth options.

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?

Well-structured with sections for use/not-use/depth/advantage, but each sentence earns its place. Slightly verbose but justified by complexity.

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 complexity of the tool and no output schema, description fully explains behavior and output (synthesized cited report). No gaps.

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?

Schema coverage is 75% (3 of 4 params have descriptions). Description adds value by explaining query as alias, depth options with search counts, focus with examples.

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?

Clearly states it performs parallel searches, extracts content, and synthesizes a cited report. Distinguishes from sibling tools novada_search and novada_extract.

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?

Explicitly describes when to use (complex questions, comparative analysis) and when not (single fact lookup, reading one URL), with alternatives named.

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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