Skip to main content
Glama
jina-ai

Jina AI Remote MCP Server

Official
by jina-ai

parallel_search_arxiv

Execute multiple arXiv academic paper searches simultaneously to gather comprehensive research coverage from diverse perspectives. Provide up to 5 different search queries to explore various research angles and methodologies efficiently.

Instructions

Run multiple arXiv searches in parallel for comprehensive research coverage and diverse academic angles. For best results, provide multiple search queries that explore different research angles and methodologies. You can use expand_query to help generate diverse queries, or create them yourself.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
searchesYesArray of arXiv search configurations to execute in parallel (maximum 5 searches for optimal performance)
timeoutNoTimeout in milliseconds for all searches

Implementation Reference

  • The core handler function for the 'parallel_search_arxiv' tool. It deduplicates search queries, creates a wrapper around executeArxivSearch, executes parallel searches using executeParallelSearches utility, and formats the results into MCP content items.
    async ({ searches, timeout }: { searches: SearchArxivArgs[]; timeout: number }) => {
    	try {
    		const props = getProps();
    
    		const tokenError = checkBearerToken(props.bearerToken);
    		if (tokenError) {
    			return tokenError;
    		}
    
    		const uniqueSearches = searches.filter((search, index, self) =>
    			index === self.findIndex(s => s.query === search.query)
    		);
    
    		// Use the common arXiv search function
    		const arxivSearchFunction = async (searchArgs: SearchArxivArgs) => {
    			return executeArxivSearch(searchArgs, props.bearerToken);
    		};
    
    		// Execute parallel searches using utility
    		const results = await executeParallelSearches(uniqueSearches, arxivSearchFunction, { timeout });
    
    		return {
    			content: formatParallelSearchResultsToContentItems(results),
    		};
    	} catch (error) {
    		return createErrorResponse(`Error: ${error instanceof Error ? error.message : String(error)}`);
    	}
    },
  • Zod schema defining the input parameters for the parallel_search_arxiv tool: an array of up to 5 search objects (query, num, tbs) and optional timeout.
    {
    	searches: z.array(z.object({
    		query: z.string().describe("Academic search terms, author names, or research topics"),
    		num: z.number().default(30).describe("Maximum number of academic papers to return, between 1-100"),
    		tbs: z.string().optional().describe("Time-based search parameter, e.g., 'qdr:h' for past hour")
    	})).max(5).describe("Array of arXiv search configurations to execute in parallel (maximum 5 searches for optimal performance)"),
    	timeout: z.number().default(30000).describe("Timeout in milliseconds for all searches")
    },
  • Registration of the parallel_search_arxiv tool on the MCP server using server.tool(), conditionally enabled via isToolEnabled.
    if (isToolEnabled("parallel_search_arxiv")) {
    	server.tool(
    		"parallel_search_arxiv",
    		"Run multiple arXiv searches in parallel for comprehensive research coverage and diverse academic angles. For best results, provide multiple search queries that explore different research angles and methodologies. You can use expand_query to help generate diverse queries, or create them yourself.",
    		{
    			searches: z.array(z.object({
    				query: z.string().describe("Academic search terms, author names, or research topics"),
    				num: z.number().default(30).describe("Maximum number of academic papers to return, between 1-100"),
    				tbs: z.string().optional().describe("Time-based search parameter, e.g., 'qdr:h' for past hour")
    			})).max(5).describe("Array of arXiv search configurations to execute in parallel (maximum 5 searches for optimal performance)"),
    			timeout: z.number().default(30000).describe("Timeout in milliseconds for all searches")
    		},
    		async ({ searches, timeout }: { searches: SearchArxivArgs[]; timeout: number }) => {
    			try {
    				const props = getProps();
    
    				const tokenError = checkBearerToken(props.bearerToken);
    				if (tokenError) {
    					return tokenError;
    				}
    
    				const uniqueSearches = searches.filter((search, index, self) =>
    					index === self.findIndex(s => s.query === search.query)
    				);
    
    				// Use the common arXiv search function
    				const arxivSearchFunction = async (searchArgs: SearchArxivArgs) => {
    					return executeArxivSearch(searchArgs, props.bearerToken);
    				};
    
    				// Execute parallel searches using utility
    				const results = await executeParallelSearches(uniqueSearches, arxivSearchFunction, { timeout });
    
    				return {
    					content: formatParallelSearchResultsToContentItems(results),
    				};
    			} catch (error) {
    				return createErrorResponse(`Error: ${error instanceof Error ? error.message : String(error)}`);
    			}
    		},
    	);
  • Helper function that performs a single arXiv search by calling the Jina Search API (svip.jina.ai) with domain='arxiv'.
    export async function executeArxivSearch(
        searchArgs: SearchArxivArgs,
        bearerToken: string
    ): Promise<SearchResultOrError> {
        try {
            const response = await fetch('https://svip.jina.ai/', {
                method: 'POST',
                headers: {
                    'Accept': 'application/json',
                    'Content-Type': 'application/json',
                    'Authorization': `Bearer ${bearerToken}`,
                },
                body: JSON.stringify({
                    q: searchArgs.query,
                    domain: 'arxiv',
                    num: searchArgs.num || 30,
                    ...(searchArgs.tbs && { tbs: searchArgs.tbs })
                }),
            });
    
            if (!response.ok) {
                return { error: `arXiv search failed for query "${searchArgs.query}": ${response.statusText}` };
            }
    
            const data = await response.json() as any;
            return { query: searchArgs.query, results: data.results || [] };
        } catch (error) {
            return { error: `arXiv search failed for query "${searchArgs.query}": ${error instanceof Error ? error.message : String(error)}` };
        }
    }
  • Generic helper utility for executing multiple searches in parallel with timeout handling and error catching, used by the parallel_search_arxiv handler.
    export async function executeParallelSearches<T>(
        searches: T[],
        searchFunction: (searchArgs: T) => Promise<SearchResultOrError>,
        options: ParallelSearchOptions = {}
    ): Promise<ParallelSearchResult[]> {
        const { timeout = 30000 } = options;
    
        // Execute all searches in parallel
        const searchPromises = searches.map(async (searchArgs) => {
            try {
                return await searchFunction(searchArgs);
            } catch (error) {
                return { error: `Search failed: ${error instanceof Error ? error.message : String(error)}` };
            }
        });
    
        // Wait for all searches with timeout
        const results = await Promise.allSettled(searchPromises);
        const timeoutPromise = new Promise(resolve => setTimeout(() => resolve('timeout'), timeout));
    
        const completedResults = await Promise.race([
            Promise.all(results.map(result =>
                result.status === 'fulfilled' ? result.value : { error: 'Promise rejected' }
            )),
            timeoutPromise
        ]);
    
        if (completedResults === 'timeout') {
            throw new Error(`Parallel search timed out after ${timeout}ms`);
        }
    
        return completedResults as ParallelSearchResult[];
    }
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the parallel execution nature and performance optimization ('maximum 5 searches for optimal performance'), which is useful. However, it doesn't disclose important behavioral aspects like rate limits, authentication requirements, error handling, or what the return format looks like (though there's no output schema). The description doesn't contradict any annotations since none exist.

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 efficiently structured in three sentences. The first sentence states the core purpose, the second provides usage guidance, and the third offers a helpful tip about query generation. Every sentence adds value with zero wasted words, making it appropriately front-loaded and concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (parallel execution of searches) and 100% schema coverage but no annotations or output schema, the description is adequate but has gaps. It explains the parallel nature and provides usage tips, but doesn't cover behavioral aspects like performance characteristics, error handling, or result format. For a tool executing parallel searches without output schema, more context about what to expect would be helpful.

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 documents all parameters thoroughly. The description adds minimal parameter semantics beyond the schema - it mentions 'multiple search queries' which aligns with the 'searches' array parameter, but doesn't provide additional context about parameter usage or constraints that aren't already in the schema descriptions. With high schema coverage, baseline 3 is appropriate.

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: 'Run multiple arXiv searches in parallel for comprehensive research coverage and diverse academic angles.' It specifies the verb ('run'), resource ('arXiv searches'), and scope ('in parallel'), distinguishing it from the sibling 'search_arxiv' tool which presumably handles single searches. The description explicitly mentions the parallel execution capability.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear usage context: 'For best results, provide multiple search queries that explore different research angles and methodologies.' It also suggests using 'expand_query' to generate diverse queries. However, it doesn't explicitly state when NOT to use this tool (e.g., for single searches where 'search_arxiv' might be more appropriate) or provide direct alternatives among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jina-ai/MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server