Synapse/deps/db-sync/worker/entry.mjs

import { DynamicWorkerExecutor } from "@cloudflare/codemode";
import { openApiMcpServer } from "@cloudflare/codemode/mcp";
import { WebStandardStreamableHTTPServerTransport } from "@modelcontextprotocol/sdk/server/webStandardStreamableHttp.js";
import { ListToolsRequestSchema } from "@modelcontextprotocol/sdk/types.js";
import { z } from "zod";
import { chatGptToolDescriptors } from "./chatgpt_app.mjs";
import { uploadChatGptAsset } from "./chatgpt_asset_upload.mjs";
import {
  ASSET_IMAGE_RESOURCE_URI,
  assertDisplayableImageMetadata,
  assetImageResource,
  assetImageResult,
} from "./asset_image.mjs";
import { apiDocsResponse } from "./api_docs.mjs";
import { logseqIconResponse, setLogseqMcpServerInfoIcons } from "./logseq_icon.mjs";
import { ensureMcpAcceptHeader } from "./mcp_request.mjs";
import { openAiAppsChallengeResponse } from "./openai_app_challenge.mjs";
import { semanticRequestBody, semanticRequestUrl } from "./semantic_request.mjs";
import apiDocsHtml from "./dist/api-docs.generated.mjs";
import worker, { SyncDO } from "./dist/worker/main.js";

function protectedResourceMetadata(request, env) {
  const url = new URL(request.url);
  return Response.json({
    resource: `${url.origin}/mcp`,
    authorization_servers: [env.COGNITO_ISSUER],
    scopes_supported: ["logseq/read", "logseq/write"],
    bearer_methods_supported: ["header"],
  });
}

async function semanticSpec(request, env, ctx) {
  const url = new URL("/openapi.json", request.url);
  const response = await worker.fetch(new Request(url), env, ctx);
  if (!response.ok) {
    throw new Error(`OpenAPI request failed: ${response.status}`);
  }
  return response.json();
}

async function handleMcp(request, env, ctx) {
  const authorization = request.headers.get("authorization");
  const server = openApiMcpServer({
    spec: await semanticSpec(request, env, ctx),
    executor: new DynamicWorkerExecutor({ loader: env.LOADER }),
    name: "logseq",
    version: "1.0.0",
    request: async (options) => {
      if (!options.path.startsWith("/api/v1/")) {
        throw new Error("Only Logseq server API paths are allowed");
      }
      const url = semanticRequestUrl(options, request.url);
      const headers = {};
      if (authorization) headers.authorization = authorization;
      if (options.contentType) headers["content-type"] = options.contentType;
      else if (options.body !== undefined) headers["content-type"] = "application/json";

      const response = await worker.fetch(
        new Request(url, {
          method: options.method,
          headers,
          body: semanticRequestBody(options, url),
        }),
        env,
        ctx,
      );
      if (response.status === 204) return null;
      const result = (response.headers.get("content-type") ?? "").includes("application/json")
        ? await response.json()
        : await response.text();
      if (!response.ok) {
        throw new Error(`Logseq API request failed: ${response.status} ${JSON.stringify(result)}`);
      }
      return result;
    },
  });
  setLogseqMcpServerInfoIcons(server, request);

  server.registerResource("asset-image", ASSET_IMAGE_RESOURCE_URI, {
    title: "Logseq image asset",
    description: "Renders a Logseq image asset returned by the image asset tool.",
    mimeType: "text/html;profile=mcp-app",
  }, assetImageResource);

  server.registerTool("upload_asset", {
    title: "Upload Logseq asset",
    description: "Upload an original ChatGPT attachment to a Logseq DB graph.",
    inputSchema: {
      graphId: z.string(),
      file: z.object({
        download_url: z.string(),
        file_id: z.string(),
        mime_type: z.string().optional(),
        file_name: z.string().optional(),
      }),
      pageId: z.string().optional(),
      title: z.string().optional(),
    },
    outputSchema: {
      uuid: z.string(),
      title: z.string(),
      type: z.string(),
      size: z.number().int().nonnegative(),
      checksum: z.string(),
    },
    annotations: {
      readOnlyHint: false,
      openWorldHint: true,
      destructiveHint: false,
      idempotentHint: false,
    },
    _meta: {
      "openai/fileParams": ["file"],
    },
  }, (input) => uploadChatGptAsset(input, {
    origin: new URL(request.url).origin,
    authorization,
    env,
    ctx,
    workerFetch: worker.fetch.bind(worker),
  }).then((result) => ({
    structuredContent: result,
    content: [{ type: "text", text: `Uploaded Logseq asset ${result.title} (${result.uuid}).` }],
  })));

  server.registerTool("get_asset_image", {
    title: "Display Logseq image asset",
    description: "Return a Logseq image asset as MCP image content for direct display.",
    inputSchema: {
      graphId: z.string(),
      assetBlockId: z.string(),
    },
    outputSchema: {
      uuid: z.string(),
      title: z.string(),
      mimeType: z.string(),
      size: z.number().int().nonnegative(),
    },
    annotations: {
      readOnlyHint: true,
      openWorldHint: false,
      destructiveHint: false,
      idempotentHint: true,
    },
    _meta: {
      ui: { resourceUri: ASSET_IMAGE_RESOURCE_URI },
      "openai/outputTemplate": ASSET_IMAGE_RESOURCE_URI,
    },
  }, async ({ graphId, assetBlockId }) => {
    const metadataUrl = new URL(
      `/api/v1/graphs/${encodeURIComponent(graphId)}/assets/${encodeURIComponent(assetBlockId)}`,
      request.url,
    );
    const metadataResponse = await worker.fetch(new Request(metadataUrl, {
      headers: authorization ? { authorization } : {},
    }), env, ctx);
    const metadata = await metadataResponse.json();
    if (!metadataResponse.ok) {
      throw new Error(`Logseq API request failed: ${metadataResponse.status} ${JSON.stringify(metadata)}`);
    }
    assertDisplayableImageMetadata(metadata);
    const imageResponse = await worker.fetch(new Request(metadata.url), env, ctx);
    return assetImageResult(imageResponse, metadata);
  });

  const transport = new WebStandardStreamableHTTPServerTransport();
  await server.connect(transport);
  server.server.setRequestHandler(ListToolsRequestSchema, () => ({
    tools: chatGptToolDescriptors(),
  }));
  return transport.handleRequest(request);
}

export default {
  async fetch(request, env, ctx) {
    const path = new URL(request.url).pathname;
    const challengeResponse = openAiAppsChallengeResponse(request, env);
    if (challengeResponse) return challengeResponse;
    const docsResponse = apiDocsResponse(path, apiDocsHtml);
    if (docsResponse) return docsResponse;
    if (path === "/.well-known/oauth-protected-resource" ||
        path === "/.well-known/oauth-protected-resource/mcp") {
      return protectedResourceMetadata(request, env);
    }
    if (path === "/mcp/icon.png") return logseqIconResponse();
    if (path === "/mcp") return handleMcp(ensureMcpAcceptHeader(request), env, ctx);
    return worker.fetch(request, env, ctx);
  },
};

export { SyncDO };