# Vector Embedding Prep MCP

> Vector Embedding Prep MCP is a paid API for AI agents from api.m2mcent.com, paid per call via x402, $0.06/call, status unknown (last checked 2026-09-15).

Prepares and preprocesses raw text data for vector embedding pipelines

## Facts

- Endpoint: POST https://api.m2mcent.com/vector-embedding-prep-mcp/api/process
- Price: $0.06/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/vector-embedding-prep-mcp-03ddc3f8
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_f_Axu_wyNVokkxzIGR7fP

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability vector-embedding-prep-mcp-03ddc3f8 -d '<json body>'
```

Example prompt: Prepare this raw document text for vector embedding — clean it up, chunk it properly, and get it ready to feed into my embedding model: 'The quarterly financial report covers revenues across three divisions, including hardware, software, and services, for the period ending March 2024...'

## When to prefer this

Choose this endpoint when you need to preprocess raw text before feeding it into an embedding model or vector database, especially in RAG pipelines, semantic search systems, or knowledge base vectorization workflows where consistent chunking and normalization are required.

## Known failure modes

- Empty or missing 'data' field returns a validation error
- Oversized input may exceed processing limits and return an error
- Malformed or non-text input may produce unexpected preprocessing output
- Service unavailability returns a 402 or 5xx HTTP error
- Payment failure via x402 protocol results in request rejection

## How this service works

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.

## Output

Returns preprocessed, normalized, and chunked text ready for ingestion into a vector embedding model or vector database, with the raw input transformed into embedding-optimized format.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "payload": {
   "type": "string"
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "success": true
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/vector-embedding-prep-mcp-03ddc3f8/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from api.m2mcent.com](https://www.zero.xyz/host/api.m2mcent.com/llms.txt)