Agent Prep – Text Chunking

x402

Agent Prep – Text Chunking is a paid API for AI agents from agent-prep.annushka1190.workers.dev, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-15).

Splits text into token-aware chunks of configurable size, returning each chunk with its text and token count.

Price / call$0.005

Seller volume →—

Success—

StatusUnknown

How this service works

Pre-LLM utilities for AI agents: structural JSON diff, JSON path listing, token estimation, text chunking, and truncation. Pay with USDC via x402 — no API keys.

Output

Returns a JSON object containing the total number of chunks produced and an array of chunk objects, each with the chunk's text and its token count. Example: {"count": 1, "chunks": [{"text": "…", "tokens": 10}]}.

Example use cases

RAG pipeline document ingestion

“I need to split this 10,000-word research paper into 512-token chunks with 50-token overlap so I can embed them and store them in my vector database — can you chunk it for me?”

LLM context window management

“This customer support transcript is way too long to fit in one LLM call. Can you break it into chunks of 1024 tokens so I can process each one separately?”

Batch embedding preparation

“I have a big block of product description text and I want to generate embeddings for it. Can you split it into 256-token chunks first so each piece fits cleanly into the embedding model?”

Technical details

Endpoint

POSThttps://agent-prep.annushka1190.workers.dev/v1/tokens/chunk

When to prefer

Choose this endpoint when you need a lightweight, pay-per-call text chunking utility with no API key setup — ideal for AI agent pipelines that need to preprocess text before embedding or LLM inference. Prefer it over rolling your own chunking logic when you need token-accurate splits (not character-based) and want a simple HTTP call with USDC micropayment billing via x402.

Side effects

Missing required 'input' field returns a 400 validation error
Excessively large text bodies may hit worker memory or timeout limits
Invalid chunk size or overlap parameters may return a 422 or default to service defaults
Payment not provided or insufficient USDC triggers a 402 Payment Required response
Malformed JSON body returns a 400 parse error

Input

Schema · Request body

Field	Type	Description
inputrequired	object

Output

Schema · Response

{
  "type": "json",
  "example": {
    "count": 1,
    "chunks": [
      {
        "text": "…",
        "tokens": 10
      }
    ]
  }
}

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How this service works

Pre-LLM utilities for AI agents: structural JSON diff, JSON path listing, token estimation, text chunking, and truncation. Pay with USDC via x402 — no API keys.

Output

Example use cases

RAG pipeline document ingestion

“I need to split this 10,000-word research paper into 512-token chunks with 50-token overlap so I can embed them and store them in my vector database — can you chunk it for me?”

LLM context window management

“This customer support transcript is way too long to fit in one LLM call. Can you break it into chunks of 1024 tokens so I can process each one separately?”

Batch embedding preparation

“I have a big block of product description text and I want to generate embeddings for it. Can you split it into 256-token chunks first so each piece fits cleanly into the embedding model?”

Technical details

Endpoint

POSThttps://agent-prep.annushka1190.workers.dev/v1/tokens/chunk

When to prefer

Side effects

Missing required 'input' field returns a 400 validation error
Excessively large text bodies may hit worker memory or timeout limits
Invalid chunk size or overlap parameters may return a 422 or default to service defaults
Payment not provided or insufficient USDC triggers a 402 Payment Required response
Malformed JSON body returns a 400 parse error

Input

Schema · Request body

Field	Type	Description
inputrequired	object

Output

Schema · Response

{
  "type": "json",
  "example": {
    "count": 1,
    "chunks": [
      {
        "text": "…",
        "tokens": 10
      }
    ]
  }
}