{"uid":"cap_RaAE9a1-LnAd6WvmIZprw","slug":"text-chunking-semantic-mcp-edcaed62","name":"Text Chunking Semantic 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/text-chunking-semantic-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":"0.06","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":"$0.06/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.06","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.06","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm__oG_JixA_nNRk9ARk65at","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.06","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Splits input text into semantically coherent chunks suitable for downstream NLP, embedding, or RAG pipelines.","exampleAgentPrompt":"Can you split this article into semantically coherent chunks so I can embed and index them in my vector database? Here's the text: 'Artificial intelligence is transforming industries. From healthcare to finance, AI applications are growing rapidly. Machine learning models require large datasets...'","exampleUseCases":[{"title":"RAG pipeline document ingestion","prompt":"I need to chunk this 5-page research paper into semantically meaningful segments before I embed and store them — here's the full text of the paper."},{"title":"Vector database preprocessing","prompt":"Take this product documentation and break it into semantic chunks so I can load them into my vector store for Q&A retrieval."},{"title":"LLM context window preparation","prompt":"This transcript is too long to fit in a single prompt — can you split it into semantically coherent pieces so I can feed them one at a time to the model?"}],"resultDescription":"Returns the input text divided into an array of semantically meaningful chunks, where each chunk preserves coherent meaning and context, optimized for downstream embedding, indexing, or retrieval tasks.","failureModes":["Empty or missing 'data' field returns an error","Extremely short input may produce a single chunk with no splitting","Very large text inputs may time out or be truncated","Non-textual binary data in the data field may produce garbled output","Payment failure (402) if USDC balance is insufficient"],"whenToPreferThis":"Choose this endpoint when you need to preprocess long-form text for embedding pipelines, RAG architectures, or vector databases and want semantic-aware chunking rather than naive fixed-size splitting. Prefer it over character-count splitters when preserving meaning across chunk boundaries is important.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-12T06:49:28.321Z","isFirstParty":false,"canonicalSlug":"text-chunking-semantic-mcp-edcaed62"}