# Resources

The Tesser API is designed for both human developers and AI agents. All endpoints are accessible via standard REST calls and via the [Model Context Protocol (MCP)](https://modelcontextprotocol.io), allowing AI coding assistants to discover and invoke Tesser operations as tools.

## AI-Friendly Resources

| Resource | URL |
| --- | --- |
| **llms.txt** | [https://docs.tesser.xyz/llms.txt](https://docs.tesser.xyz/llms.txt) |
| **Full docs for LLMs** | [https://docs.tesser.xyz/llms-full.txt](https://docs.tesser.xyz/llms-full.txt) |
| **OpenAPI Schema** | [https://docs.tesser.xyz/api/v1/schema.json](https://docs.tesser.xyz/api/v1/schema.json) |
| **MCP Endpoint** | `https://sandbox.tesserx.co/v1/mcp` |
| **OAuth Resource Metadata** | `https://sandbox.tesserx.co/.well-known/oauth-protected-resource` |

## Authentication

Both REST and MCP use the same Auth0 OAuth 2.0 credentials. You need a `TESSER_API_KEY` and `TESSER_API_SECRET` (issued as Auth0 client credentials).

MCP clients that support OAuth discovery can read the resource-metadata endpoint above to locate our Auth0 authorization server and authenticate automatically — see [MCP Integration](/agentic/mcp-integration#oauth-discovery). Otherwise, obtain a bearer token manually as below.

### Obtain a Token

```bash
curl --request POST \
  --url https://dev-awqy75wdabpsnsvu.us.auth0.com/oauth/token \
  --header 'content-type: application/json' \
  --data '{
    "client_id": "'$TESSER_API_KEY'",
    "client_secret": "'$TESSER_API_SECRET'",
    "audience": "https://sandbox.tesserx.co",
    "grant_type": "client_credentials"
  }'
```

The response contains an `access_token` to use as a Bearer token in all subsequent requests.

### Environment Setup

Create a `.env` file in your project root:

```bash
TESSER_API_KEY=your-api-key
TESSER_API_SECRET=your-api-secret
```