octagon-deep-research-agent
Conduct comprehensive research and analysis across any topic by aggregating multi-source data, synthesizing findings, and generating detailed reports for informed decision-making.
Instructions
A specialized agent for deep research and comprehensive analysis across any topic or domain. Capabilities: Multi-source data aggregation, web scraping, academic research synthesis, competitive analysis, market intelligence, technical analysis, policy research, trend analysis, and comprehensive report generation. Best for: Any research question requiring comprehensive, multi-source analysis and synthesis. Example queries: 'Research the current state of quantum computing development and commercial applications', 'Analyze the competitive landscape in the electric vehicle market focusing on battery technology and supply chains', 'Investigate recent developments in AI regulation across different countries and their potential impact', 'Research sustainable agriculture practices and their adoption rates globally', 'Analyze the gig economy's impact on traditional employment models', 'Study the evolution of remote work policies post-pandemic and their effectiveness', 'Research breakthrough medical treatments for Alzheimer's disease in the last 3 years', 'Investigate cybersecurity threats in IoT devices and mitigation strategies', 'Analyze renewable energy adoption trends and policy drivers worldwide', 'Research the impact of social media algorithms on information consumption patterns'.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Your natural language query or request for the agent |
Implementation Reference
- src/index.ts:94-124 (handler)The main handler function for the 'octagon-deep-research-agent' tool. It invokes the Octagon API's chat completions endpoint with the 'octagon-deep-research-agent' model, streams the response, processes it using the helper function, and returns the result as MCP content.async ({ prompt }: PromptParams) => { try { const response = await octagonClient.chat.completions.create({ model: "octagon-deep-research-agent", messages: [{ role: "user", content: prompt }], stream: true, metadata: { tool: "mcp" } }); const result = await processStreamingResponse(response); return { content: [ { type: "text", text: result, }, ], }; } catch (error) { console.error("Error calling Deep Research agent:", error); return { isError: true, content: [ { type: "text", text: `Error: Failed to process deep research query. ${error}`, }, ], }; } }
- src/index.ts:89-125 (registration)Registers the 'octagon-deep-research-agent' tool with the MCP server, including the tool name, detailed description, input schema, and inline handler function."octagon-deep-research-agent", "A specialized agent for deep research and comprehensive analysis across any topic or domain. Capabilities: Multi-source data aggregation, web scraping, academic research synthesis, competitive analysis, market intelligence, technical analysis, policy research, trend analysis, and comprehensive report generation. Best for: Any research question requiring comprehensive, multi-source analysis and synthesis. Example queries: 'Research the current state of quantum computing development and commercial applications', 'Analyze the competitive landscape in the electric vehicle market focusing on battery technology and supply chains', 'Investigate recent developments in AI regulation across different countries and their potential impact', 'Research sustainable agriculture practices and their adoption rates globally', 'Analyze the gig economy's impact on traditional employment models', 'Study the evolution of remote work policies post-pandemic and their effectiveness', 'Research breakthrough medical treatments for Alzheimer's disease in the last 3 years', 'Investigate cybersecurity threats in IoT devices and mitigation strategies', 'Analyze renewable energy adoption trends and policy drivers worldwide', 'Research the impact of social media algorithms on information consumption patterns'.", { prompt: z.string().describe("Your natural language query or request for the agent"), }, async ({ prompt }: PromptParams) => { try { const response = await octagonClient.chat.completions.create({ model: "octagon-deep-research-agent", messages: [{ role: "user", content: prompt }], stream: true, metadata: { tool: "mcp" } }); const result = await processStreamingResponse(response); return { content: [ { type: "text", text: result, }, ], }; } catch (error) { console.error("Error calling Deep Research agent:", error); return { isError: true, content: [ { type: "text", text: `Error: Failed to process deep research query. ${error}`, }, ], }; } } );
- src/index.ts:92-92 (schema)Input schema for the tool, defining a single 'prompt' parameter of type string.prompt: z.string().describe("Your natural language query or request for the agent"),
- src/index.ts:48-76 (helper)Helper function used by the handler to process and concatenate streaming responses from the Octagon API chat completions.async function processStreamingResponse(stream: any): Promise<string> { let fullResponse = ""; let citations: any[] = []; try { // Process the streaming response for await (const chunk of stream) { // For Chat Completions API if (chunk.choices && chunk.choices[0]?.delta?.content) { fullResponse += chunk.choices[0].delta.content; // Check for citations in the final chunk if (chunk.choices[0]?.finish_reason === "stop" && chunk.choices[0]?.citations) { citations = chunk.choices[0].citations; } } // For Responses API if (chunk.type === "response.output_text.delta") { fullResponse += chunk.text?.delta || ""; } } return fullResponse; } catch (error) { console.error("Error processing streaming response:", error); throw error; } }