Jina AI Remote MCP Server

Instructions

Implementation Reference

• src/tools/jina-tools.ts:524-645 (registration)

  Registers the 'deduplicate_strings' tool on the MCP server, defining its description, input schema, and handler function.
  server.tool( "deduplicate_strings", "Get top-k semantically unique strings from a list using Jina embeddings and submodular optimization. Use this when you have many similar strings and want to select the most diverse subset that covers the semantic space. Perfect for removing duplicates, selecting representative samples, or finding diverse content. Returns the selected strings with their indices.", { strings: z.array(z.string()).describe("Array of strings to deduplicate"), k: z.number().optional().describe("Number of unique strings to return. If not provided, automatically finds optimal k by looking at diminishing return") }, async ({ strings, k }: { strings: string[]; k?: number }) => { try { const props = getProps(); const tokenError = checkBearerToken(props.bearerToken); if (tokenError) { return tokenError; } if (strings.length === 0) { return { content: [ { type: "text" as const, text: "No strings provided for deduplication", }, ], isError: true, }; } if (k !== undefined && (k <= 0 || k > strings.length)) { return { content: [ { type: "text" as const, text: `Invalid k value: ${k}. Must be between 1 and ${strings.length}`, }, ], isError: true, }; } // Get embeddings from Jina API const response = await fetch('https://api.jina.ai/v1/embeddings', { method: 'POST', headers: { 'Accept': 'application/json', 'Content-Type': 'application/json', 'Authorization': `Bearer ${props.bearerToken}`, }, body: JSON.stringify({ model: 'jina-embeddings-v3', task: 'text-matching', input: strings }), }); if (!response.ok) { return handleApiError(response, "Getting embeddings"); } const data = await response.json() as any; if (!data.data || !Array.isArray(data.data)) { return { content: [ { type: "text" as const, text: "Invalid response format from embeddings API", }, ], isError: true, }; } // Extract embeddings const embeddings = data.data.map((item: any) => item.embedding); // Use submodular optimization to select diverse strings let selectedIndices: number[]; let optimalK: number; let values: number[]; if (k !== undefined) { // Use specified k selectedIndices = lazyGreedySelection(embeddings, k); values = []; } else { // Automatically find optimal k using saturation point const result = lazyGreedySelectionWithSaturation(embeddings); selectedIndices = result.selected; values = result.values; } // Get the selected strings const selectedStrings = selectedIndices.map(idx => ({ index: idx, text: strings[idx] })); return { content: [ { type: "text" as const, text: yamlStringify({ // values: values, deduplicated_strings: selectedStrings, }), }, ], }; } catch (error) { return { content: [ { type: "text" as const, text: `Error: ${error instanceof Error ? error.message : String(error)}`, }, ], isError: true, }; } }, );
• src/tools/jina-tools.ts:531-644 (handler)

  The handler function that implements the tool logic: validates input, fetches semantic embeddings using Jina API, applies submodular greedy selection for diversity, and returns the deduplicated strings with indices.
  async ({ strings, k }: { strings: string[]; k?: number }) => { try { const props = getProps(); const tokenError = checkBearerToken(props.bearerToken); if (tokenError) { return tokenError; } if (strings.length === 0) { return { content: [ { type: "text" as const, text: "No strings provided for deduplication", }, ], isError: true, }; } if (k !== undefined && (k <= 0 || k > strings.length)) { return { content: [ { type: "text" as const, text: `Invalid k value: ${k}. Must be between 1 and ${strings.length}`, }, ], isError: true, }; } // Get embeddings from Jina API const response = await fetch('https://api.jina.ai/v1/embeddings', { method: 'POST', headers: { 'Accept': 'application/json', 'Content-Type': 'application/json', 'Authorization': `Bearer ${props.bearerToken}`, }, body: JSON.stringify({ model: 'jina-embeddings-v3', task: 'text-matching', input: strings }), }); if (!response.ok) { return handleApiError(response, "Getting embeddings"); } const data = await response.json() as any; if (!data.data || !Array.isArray(data.data)) { return { content: [ { type: "text" as const, text: "Invalid response format from embeddings API", }, ], isError: true, }; } // Extract embeddings const embeddings = data.data.map((item: any) => item.embedding); // Use submodular optimization to select diverse strings let selectedIndices: number[]; let optimalK: number; let values: number[]; if (k !== undefined) { // Use specified k selectedIndices = lazyGreedySelection(embeddings, k); values = []; } else { // Automatically find optimal k using saturation point const result = lazyGreedySelectionWithSaturation(embeddings); selectedIndices = result.selected; values = result.values; } // Get the selected strings const selectedStrings = selectedIndices.map(idx => ({ index: idx, text: strings[idx] })); return { content: [ { type: "text" as const, text: yamlStringify({ // values: values, deduplicated_strings: selectedStrings, }), }, ], }; } catch (error) { return { content: [ { type: "text" as const, text: `Error: ${error instanceof Error ? error.message : String(error)}`, }, ], isError: true, }; } },
• src/tools/jina-tools.ts:527-530 (schema)

  Zod schema defining the input parameters: array of strings and optional k.
  { strings: z.array(z.string()).describe("Array of strings to deduplicate"), k: z.number().optional().describe("Number of unique strings to return. If not provided, automatically finds optimal k by looking at diminishing return") },
• src/utils/submodular-optimization.ts:35-92 (helper)

  Helper function performing lazy greedy submodular optimization to select exactly k diverse embeddings based on cosine similarity.
  export function lazyGreedySelection(embeddings: number[][], k: number): number[] { const n = embeddings.length; if (k >= n) return Array.from({ length: n }, (_, i) => i); const selected: number[] = []; const remaining = new Set(Array.from({ length: n }, (_, i) => i)); // Pre-compute similarity matrix const similarityMatrix: number[][] = []; for (let i = 0; i < n; i++) { similarityMatrix[i] = []; for (let j = 0; j < n; j++) { // Clamp to non-negative to ensure monotone submodularity of facility-location objective const sim = cosineSimilarity(embeddings[i], embeddings[j]); similarityMatrix[i][j] = sim > 0 ? sim : 0; } } // Maintain current coverage vector (max similarity to selected set for each element) const currentCoverage = new Array(n).fill(0); // Priority queue implementation using array (simplified) const pq: Array<[number, number, number]> = []; // Initialize priority queue for (let i = 0; i < n; i++) { const gain = computeMarginalGainDiversity(i, currentCoverage, similarityMatrix); pq.push([-gain, 0, i]); } // Sort by gain (descending) pq.sort((a, b) => a[0] - b[0]); for (let iteration = 0; iteration < k; iteration++) { while (pq.length > 0) { const [negGain, lastUpdated, bestIdx] = pq.shift()!; if (!remaining.has(bestIdx)) continue; if (lastUpdated === iteration) { selected.push(bestIdx); remaining.delete(bestIdx); // Update coverage in O(n) const row = similarityMatrix[bestIdx]; for (let i = 0; i < n; i++) { if (row[i] > currentCoverage[i]) currentCoverage[i] = row[i]; } break; } const currentGain = computeMarginalGainDiversity(bestIdx, currentCoverage, similarityMatrix); pq.push([-currentGain, iteration, bestIdx]); pq.sort((a, b) => a[0] - b[0]); } } return selected; }
• src/utils/submodular-optimization.ts:94-170 (helper)

  Helper function that automatically determines optimal k by detecting saturation point in submodular objective and returns selected indices.
  export function lazyGreedySelectionWithSaturation( embeddings: number[][], threshold: number = 1e-2 ): { selected: number[], optimalK: number, values: number[] } { const n = embeddings.length; const selected: number[] = []; const remaining = new Set(Array.from({ length: n }, (_, i) => i)); const values: number[] = []; // Pre-compute similarity matrix const similarityMatrix: number[][] = []; for (let i = 0; i < n; i++) { similarityMatrix[i] = []; for (let j = 0; j < n; j++) { const sim = cosineSimilarity(embeddings[i], embeddings[j]); similarityMatrix[i][j] = sim > 0 ? sim : 0; } } const currentCoverage = new Array(n).fill(0); // Priority queue implementation using array (simplified) const pq: Array<[number, number, number]> = []; // Initialize priority queue for (let i = 0; i < n; i++) { const gain = computeMarginalGainDiversity(i, currentCoverage, similarityMatrix); pq.push([-gain, 0, i]); } // Sort by gain (descending) pq.sort((a, b) => a[0] - b[0]); let earlyStopK: number | null = null; for (let iteration = 0; iteration < n; iteration++) { while (pq.length > 0) { const [negGain, lastUpdated, bestIdx] = pq.shift()!; if (!remaining.has(bestIdx)) continue; if (lastUpdated === iteration) { selected.push(bestIdx); remaining.delete(bestIdx); // Compute current function value (coverage) const row = similarityMatrix[bestIdx]; for (let i = 0; i < n; i++) { if (row[i] > currentCoverage[i]) currentCoverage[i] = row[i]; } const functionValue = currentCoverage.reduce((sum, val) => sum + val, 0) / n; values.push(functionValue); // Early stop when the marginal gain (delta of normalized objective) falls below threshold if (values.length >= 2) { const delta = values[values.length - 1] - values[values.length - 2]; if (delta < threshold) { earlyStopK = values.length; // k is count of selected items } } break; } const currentGain = computeMarginalGainDiversity(bestIdx, currentCoverage, similarityMatrix); pq.push([-currentGain, iteration, bestIdx]); pq.sort((a, b) => a[0] - b[0]); } if (earlyStopK !== null) break; } // Choose k: prefer early stop detection; otherwise, use all collected values const optimalK = earlyStopK ?? values.length; const finalSelected = selected.slice(0, optimalK); return { selected: finalSelected, optimalK, values }; }