@cyanheads/pubmed-mcp-server/dist/mcp-server/tools/fetchPubMedContent/registration.js

A Model Context Protocol (MCP) server enabling AI agents to intelligently search, retrieve, and analyze biomedical literature from PubMed via NCBI E-utilities. Built on the mcp-ts-template for robust, production-ready performance.

/**
 * @fileoverview Registration for the fetch_pubmed_content MCP tool.
 * @module src/mcp-server/tools/fetchPubMedContent/registration
 */
import { BaseErrorCode, McpError } from "../../../types-global/errors.js";
import { ErrorHandler, logger, requestContextService, } from "../../../utils/index.js";
import { FetchPubMedContentInputSchema, fetchPubMedContentLogic, } from "./logic.js";
/**
 * Registers the fetch_pubmed_content tool with the MCP server.
 * @param server - The McpServer instance.
 */
export async function registerFetchPubMedContentTool(server) {
    const operation = "registerFetchPubMedContentTool";
    const toolName = "fetch_pubmed_content";
    const toolDescription = "Fetches detailed information from PubMed using NCBI EFetch. Can be used with a direct list of PMIDs or with queryKey/webEnv from an ESearch history entry. Supports pagination (retstart, retmax) when using history. Available 'detailLevel' options: 'abstract_plus' (parsed title, abstract, authors, journal, keywords, DOI, optional MeSH/grant info), 'full_xml' (JSON representation of the PubMedArticle XML structure), 'medline_text' (MEDLINE format), or 'citation_data' (minimal data for citations). Returns a JSON object containing results, any PMIDs not found (if applicable), and EFetch details.";
    const context = requestContextService.createRequestContext({ operation });
    await ErrorHandler.tryCatch(async () => {
        server.tool(toolName, toolDescription, FetchPubMedContentInputSchema._def.schema.shape, async (input, toolContext) => {
            const richContext = requestContextService.createRequestContext({
                parentRequestId: context.requestId,
                operation: "fetchPubMedContentToolHandler",
                mcpToolContext: toolContext,
                input,
            });
            try {
                const result = await fetchPubMedContentLogic(input, richContext);
                return {
                    content: [{ type: "text", text: result.content }],
                    isError: false,
                };
            }
            catch (error) {
                const handledError = ErrorHandler.handleError(error, {
                    operation: "fetchPubMedContentToolHandler",
                    context: richContext,
                    input,
                    rethrow: false,
                });
                const mcpError = handledError instanceof McpError
                    ? handledError
                    : new McpError(BaseErrorCode.INTERNAL_ERROR, "An unexpected error occurred while fetching PubMed content.", {
                        originalErrorName: handledError.name,
                        originalErrorMessage: handledError.message,
                    });
                return {
                    content: [
                        {
                            type: "text",
                            text: JSON.stringify({
                                error: {
                                    code: mcpError.code,
                                    message: mcpError.message,
                                    details: mcpError.details,
                                },
                            }),
                        },
                    ],
                    isError: true,
                };
            }
        });
        logger.notice(`Tool '${toolName}' registered.`, context);
    }, {
        operation,
        context,
        errorCode: BaseErrorCode.INITIALIZATION_FAILED,
        critical: true,
    });
}