import { z } from "zod";
import {
GenerateResearchQuestionsSchema,
GenerateResearchQuestionsSchemaBase,
} from "../schemas/index.js";
import { makeInfraNodusRequest } from "../api/client.js";
import { fetchUrlContentAsText } from "../utils/urlContent.js";
import { generateResearchQuestions } from "../utils/transformers.js";
function errorContent(message: string) {
return {
content: [
{ type: "text" as const, text: JSON.stringify({ error: message }) },
],
isError: true,
};
}
export const generateResearchQuestionsTool = {
name: "generate_research_questions",
definition: {
title: "Generate Research Questions from Text",
description:
"Analyze text or an existing graph and generate innovative research questions based on the content gaps identified between the topical clusters. Provide either text, url, or graphName. Can be used to improve the text and the discourse it relates to",
inputSchema: GenerateResearchQuestionsSchemaBase.shape,
annotations: {
"readOnlyHint": true,
"idempotentHint": true,
"destructiveHint": false
},
},
handler: async (params: z.infer<typeof GenerateResearchQuestionsSchema>) => {
try {
// Build query parameters
const queryParams = new URLSearchParams({
doNotSave: "true",
addStats: "true",
optimize: "gap",
includeStatements: "false",
includeGraphSummary: "false",
extendedGraphSummary: "false",
includeGraph: "false",
aiTopics: "true",
extendedAdvice: params.useSeveralGaps ? "true" : "false",
gapDepth: params.gapDepth ? params.gapDepth.toString() : "0",
});
const endpoint = `/graphAndAdvice?${queryParams.toString()}`;
let requestBody: { text?: string; name?: string; aiTopics: string; requestMode: string; modelToUse: string };
if (params.graphName?.trim()) {
requestBody = {
name: params.graphName,
aiTopics: "true",
requestMode: "question",
modelToUse: params.modelToUse ?? "gpt-4o",
};
} else {
let contentText: string;
if (params.url) {
const result = await fetchUrlContentAsText(params.url);
if (!result.ok) return errorContent(result.error);
contentText = result.contentText;
if (!contentText?.trim())
return errorContent("URL did not return any text content");
} else if (params.text?.trim()) {
contentText = params.text;
} else {
return errorContent("Provide either text, url, or graphName for analysis");
}
requestBody = {
text: contentText,
aiTopics: "true",
requestMode: "question",
modelToUse: params.modelToUse ?? "gpt-4o",
};
}
const response = await makeInfraNodusRequest(endpoint, requestBody);
const researchQuestions = generateResearchQuestions(response);
if (response.error) {
return {
content: [
{
type: "text" as const,
text: `Error: ${response.error}`,
},
],
isError: true,
};
}
return {
content: [
{
type: "text" as const,
text: JSON.stringify(researchQuestions, null, 2),
},
],
};
} catch (error) {
return {
content: [
{
type: "text" as const,
text: JSON.stringify({
error: error instanceof Error ? error.message : String(error),
}),
},
],
isError: true,
};
}
},
};
