{"uid":"cap_g40qEW8aGqsbnjAT0gas2","slug":"vector-dimension-compressor-mcp-a0a78b6c","name":"Vector Dimension Compressor MCP","description":"The premier global index of 1,069 monetized MCP nodes across 205 specialized subdomains. Gasless USDC runtime settlements via x402 V2 Spec on Base L2. Save 95% token context.","url":"https://api.m2mcent.com/vector-dimension-compressor-mcp/api/process?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":null,"responseSchema":{"type":"json","example":{"success":true}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.12","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.12/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.12","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.12","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_VIvbgEODY3XdXnNoHOfSb","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.12","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Compresses high-dimensional vector data into lower-dimensional representations for efficient storage, retrieval, and processing.","exampleAgentPrompt":"Can you compress these high-dimensional vector embeddings for me so they take up less space and are faster to search through? Here's the data: [0.23, 0.87, 0.12, ...]","exampleUseCases":[{"title":"Compress embeddings for vector database storage","prompt":"I have a batch of high-dimensional embeddings from my text encoder that are 1536 dimensions each — can you compress them down so they're cheaper to store and query in my vector database? Here's the raw data."},{"title":"Speed up semantic search pipeline","prompt":"My semantic search is too slow because my vectors are huge. Can you reduce the dimensionality of these embeddings so similarity lookups are faster? I'll paste the vector data now."},{"title":"Optimize ML feature vectors for deployment","prompt":"Before I deploy my recommendation model, I need to compress these feature vectors to reduce latency at serving time. Can you run dimension compression on this vector data?"}],"resultDescription":"Returns a compressed, lower-dimensional representation of the submitted vector data, suitable for more efficient storage, similarity search, or downstream ML inference.","failureModes":["Invalid or malformed data string returns a 400 error","Empty data field causes processing failure","Data format not recognized as valid vector representation","Payment not completed results in 402 response","Server timeout on very large vector payloads"],"whenToPreferThis":"Use this endpoint when you need to reduce the size or dimensionality of high-dimensional vector embeddings or feature vectors, especially before storing them in vector databases or using them in latency-sensitive retrieval pipelines. Prefer this over manual PCA or UMAP scripts when you need a fast, API-accessible compression step in an agentic workflow.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-10T06:51:25.679Z","isFirstParty":false,"canonicalSlug":"vector-dimension-compressor-mcp-a0a78b6c"}