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Create Deep Research Task

createDeepResearch

Creates a Deep Research task for comprehensive, single-topic research with citations. USE THIS for analyst-grade reports, NOT for batch data enrichment. Use Parallel Search MCP for quick lookups. After calling, share the URL with the user and STOP. Do not poll or check results unless otherwise instructed.

Multi-turn research: The response includes an interaction_id. To ask follow-up questions that build on prior research, pass that interaction_id as previous_interaction_id in a new call. The follow-up run inherits accumulated context, so queries like "How does this compare to X?" work without restating the original topic. Note: the first run must be completed before the follow-up can use its context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesNatural language research query or objective. Be specific and detailed for better results.
processorNoOptional processor override. Defaults to 'pro'. Only specify if user explicitly requests a different processor (e.g., 'ultra' for maximum depth).
source_policyNoOptional source policy governing preferred and disallowed domains in web search results.
previous_interaction_idNoChain follow-up research onto a completed run. Set this to the interaction_id returned by a previous createDeepResearch call. The new run inherits all prior research context. The previous run must have status 'completed' before this can be used.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / previous_interaction_id
      Added value: +{
+  "description": "Chain follow-up research onto a completed run. Set this to the interaction_id returned by a previous createDeepResearch call. The new run inherits all prior research context. The previous run must have status 'completed' before this can be used.",
+  "nullable": true,
+  "type": "string"
+}
  2. First observed

TDQS

A4.6/5.0
Behavior4/5

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

The description adds significant behavioral context beyond annotations: it explains the multi-turn research capability with interaction_id inheritance, specifies that the tool returns a URL to share, and instructs not to poll results unless instructed. Annotations cover basic hints (readOnlyHint=false, etc.), but the description enriches this with practical workflow details like stopping after calling and context inheritance for follow-ups.

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?

The description is well-structured and front-loaded with key usage guidelines. Each sentence adds value, such as contrasting with alternatives, post-call instructions, and multi-turn research details. It could be slightly more concise by integrating some details more tightly, but overall, it avoids waste and is appropriately sized for the tool's 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 the tool's complexity (multi-turn research, multiple parameters) and lack of output schema, the description is highly complete. It covers purpose, usage guidelines, behavioral traits, parameter semantics, and workflow instructions. It compensates for the missing output schema by explaining what to do with the response (share URL) and how to handle follow-ups, making it sufficient for an agent to use the tool effectively.

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?

With 100% schema description coverage, the baseline is 3. The description adds value by clarifying parameter usage: it explains that 'input' should be 'specific and detailed for better results,' advises on 'processor' usage ('Only specify if user explicitly requests'), and details how 'previous_interaction_id' enables multi-turn research with context inheritance. This provides semantic context beyond the schema's technical descriptions.

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 the tool 'Creates a Deep Research task for comprehensive, single-topic research with citations,' specifying both the verb (creates) and resource (Deep Research task). It distinguishes from siblings by explicitly contrasting with 'Parallel Search MCP for quick lookups' and 'batch data enrichment,' making the purpose specific and differentiated.

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 provides explicit guidance on when to use this tool ('USE THIS for analyst-grade reports, NOT for batch data enrichment') and when not to ('Use Parallel Search MCP for quick lookups'). It also includes detailed instructions on post-call behavior ('share the URL with the user and STOP') and multi-turn usage scenarios, offering clear alternatives and context.

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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