# Cross-Modal Embedding Aligner MCP

> Cross-Modal Embedding Aligner MCP is a paid API for AI agents from api.m2mcent.com, paid per call via x402, $0.18/call, status unknown (last checked 2026-10-16).

Aligns and maps embeddings across different modalities (e.g. text, image, audio) into a shared vector space for cross-modal similarity and retrieval

## Facts

- Endpoint: POST https://api.m2mcent.com/cross-modal-embedding-aligner-mcp/api/process?utm_source=zero.xyz
- Price: $0.18/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-16
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/cross-modal-embedding-aligner-mcp-6257a602
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_2uvC46F_meJdmHkyB2Eo3

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 cross-modal-embedding-aligner-mcp-6257a602 -d '<json body>'
```

Example prompt: I have a text embedding and an image embedding from two different models — can you align them into the same shared vector space so I can compare their semantic similarity?

## When to prefer this

Choose this endpoint when you need to bridge embeddings from different modality-specific models (e.g. CLIP image embeddings vs. BERT text embeddings) into a unified vector space for cross-modal retrieval, ranking, or similarity computation. Prefer it over single-modality embedding services when your pipeline involves comparing or searching across heterogeneous data types like text, images, or audio.

## Known failure modes

- Malformed or non-numeric data string returns a 400 error
- Incompatible embedding dimensions may cause alignment failure
- Empty data field results in validation error
- Unsupported modality combinations may return a processing error
- Payment not received results in 402 Payment Required response

## 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 aligned embedding vectors projected into a shared cross-modal space, enabling direct comparison and similarity computation between embeddings originally produced by different modality-specific models.

## Response schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/cross-modal-embedding-aligner-mcp-6257a602/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)