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deep-research

Perform comprehensive research on complex topics using expert analysis with detailed facts, in-depth analysis, and cited sources. Get structured results for thorough investigation needs.

Instructions

Perform comprehensive deep research on any topic using AgentOracle Sonar Pro. Returns expert-level analysis with 10-15 detailed facts, in-depth analysis paragraph, cited sources, and confidence score. Costs $0.10 USDC per query via x402 on Base mainnet. Use for complex topics requiring thorough analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language research question for deep analysis. Examples: 'Comprehensive analysis of x402 protocol adoption and market impact', 'Detailed comparison of AI agent frameworks in 2026 with pros and cons'

Implementation Reference

  • The 'deep-research' tool is registered and implemented directly in src/index.ts using the server.tool() method, which defines the tool schema and handler logic.
    server.tool(
      "deep-research",
      "Perform comprehensive deep research on any topic using AgentOracle Sonar Pro. Returns expert-level analysis with 10-15 detailed facts, in-depth analysis paragraph, cited sources, and confidence score. Costs $0.10 USDC per query via x402 on Base mainnet. Use for complex topics requiring thorough analysis.",
      {
        query: z
          .string()
          .max(4000)
          .describe(
            "Natural language research question for deep analysis. Examples: 'Comprehensive analysis of x402 protocol adoption and market impact', 'Detailed comparison of AI agent frameworks in 2026 with pros and cons'"
          ),
      },
      async ({ query }) => {
        try {
          const response = await fetch(DEEP_RESEARCH_ENDPOINT, {
            method: "POST",
            headers: { "Content-Type": "application/json" },
            body: JSON.stringify({ query }),
          });
    
          if (response.status === 402) {
            const paymentInfo = await response.json();
            return {
              content: [
                {
                  type: "text",
                  text: JSON.stringify(
                    {
                      status: "payment_required",
                      message: "This deep research query requires a $0.10 USDC payment on Base mainnet via x402 protocol.",
                      instructions: "Use an x402-compatible client to sign a USDC payment and include it in the X-PAYMENT header.",
                      payment_details: paymentInfo,
                      endpoint: DEEP_RESEARCH_ENDPOINT,
                      manifest: MANIFEST_ENDPOINT,
                    },
                    null,
                    2
                  ),
                },
              ],
            };
          }
    
          if (!response.ok) {
            const errorData = await response.json().catch(() => ({}));
            return {
              content: [{ type: "text", text: `Deep research request failed (HTTP ${response.status}): ${JSON.stringify(errorData)}` }],
              isError: true,
            };
          }
    
          const data = await response.json();
          return { content: [{ type: "text", text: JSON.stringify(data, null, 2) }] };
        } catch (error) {
          return {
            content: [{ type: "text", text: `Error connecting to AgentOracle: ${error instanceof Error ? error.message : String(error)}` }],
            isError: true,
          };
        }
      }
    );
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: the specific return format (10-15 facts, analysis paragraph, sources, confidence score), payment requirement ($0.10 USDC via x402), and that it's for complex topics. It doesn't mention rate limits or error conditions, keeping it from a perfect score.

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 perfectly concise and front-loaded: the first sentence states the core purpose, subsequent sentences add crucial details (output format, cost, usage context). Every sentence earns its place with no wasted words.

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?

For a single-parameter tool with no annotations and no output schema, the description provides excellent context about behavior, outputs, and usage. It falls short of perfect because it doesn't explicitly describe error cases or response structure details, though the output format is well-specified.

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 the schema already fully documents the single 'query' parameter. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced 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 the tool's purpose with specific verbs ('perform comprehensive deep research') and resource ('any topic using AgentOracle Sonar Pro'). It distinguishes from the sibling 'research' tool by emphasizing 'comprehensive' analysis with specific outputs (10-15 facts, analysis paragraph, sources, confidence score).

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?

Explicit guidance is provided on when to use this tool ('for complex topics requiring thorough analysis') and when not to use it (implied: simpler topics should use the sibling 'research' tool). The cost disclosure ($0.10 per query) also helps determine appropriate usage contexts.

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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