Give AI safe, scoped access to real-world systems - fast.

Turn your existing APIs into MCP tools with hosted runtime and scoped access built in. No servers to run. No custom MCP infrastructure to maintain.

Giving AI access to tools is harder than it sounds.

It's not just exposing an endpoint. To make models useful safely, you need:

  • Authentication across users and projects
  • Fine-grained permission boundaries
  • Safe tool execution with audit trails
  • Isolation between environments
  • Visibility into usage and failures

This is where most implementations break down.

Agent systems don't need rigid workflow graphs. They need guardrails.

AI systems don't require predefined flows. They require safe, authenticated access to tools - enforced at runtime.

What they need is:

  • Tools discovered dynamically
  • Permissions enforced per account, project, and user
  • Execution monitored and logged

The missing piece is not orchestration.

It is safe, authenticated access to tools.

Hosted MCP Runtime

The fastest way to get MCP into production.

Hosted runtime. Scoped access. Control plane included.

  • No MCP server to deploy or maintain
  • Built-in scoped authentication (account -> project -> user)
  • Tool and permission management in one place
  • Usage visibility and execution logs

Built for startups and SaaS teams that ship fast.

How it works

  1. 1

    Create users and projects

    Define isolation boundaries for tools and permissions.

  2. 2

    Connect your API

    Define tools in the dashboard and point them at your REST endpoints.

  3. 3

    Enforce scoped access

    Permissions are applied automatically at account, project, and user levels.

  4. 4

    Connect using your model's SDK

    Models connect to TengineAI via MCP through the SDKs you already use.

  5. 5

    Let the model reason

    The model decides at runtime. TengineAI enforces safety and execution.

Connect using your model's SDK via MCP

TengineAI does not require a custom client library. AI models connect directly using MCP through the SDKs you already use.

Below is an example using the Anthropic Python SDK.

Python (Anthropic SDK)
1from anthropic import AsyncAnthropic
2
3client = AsyncAnthropic(api_key=CLAUDE_API_KEY)
4
5mcp_servers = [
6    {
7        "type": "url",
8        "url": "https://app.tengine.ai/mcp",
9        "name": "tengineai-mcp",
10        "authorization_token": TENGINEAI_MCP_API_KEY,
11    }
12]
13
14response = await client.beta.messages.create(
15    model="claude-sonnet-4-5",
16    max_tokens=2048,
17    messages=[
18      {
19        "role": "user",
20        "content": """
21          You are managing our AI-driven blog.
22
23          Your task is to:
24          1. Find trending posts on technology-related subreddits
25          2. Analyze the posts and generate three blog topic ideas
26          3. Select the strongest topic and create a draft blog post
27          4. Generate a featured image and update the draft
28          5. Write SEO metadata and update the draft
29          6. Publish the blog post
30
31          Proceed step by step.
32        """
33      }
34    ],
35    mcp_servers=mcp_servers,
36    betas=["mcp-client-2025-04-04"],
37)
  • No custom infrastructure required
  • Tools are discovered automatically via MCP
  • Permissions are enforced by TengineAI
  • The model decides what to call at runtime

This is how agentic systems should be built.

Goals in the prompt. Tools discovered at runtime. Permissions enforced by infrastructure.

Get started freeView Documentation
Running in production

We run production AI systems on TengineAI.

Our AI-driven blog runs entirely on TengineAI. No external orchestration. No manual workflows.

There is no predefined workflow graph. The model reasons through the task at runtime.

THE AI:

  • Finds trending content
  • Reasons about what matters
  • Uses tools to create and update drafts
  • Generates images and SEO metadata
  • Publishes posts automatically

LIVE TOOL CALLS

POST /mcp/tools/create_draft
POST /mcp/tools/generate_image
POST /mcp/tools/publish_post

TengineAI controls authentication, permissions, and tool execution.

View our blog →

Built for startups and SaaS teams shipping real AI systems.

This is for you if:

  • You are building AI products or agentic systems
  • You need AI to interact with real APIs
  • You care about authentication, permissions, and security
  • You don't want to host or maintain an MCP server

This is probably not for you if:

  • You are only experimenting with prompts
  • You want no-code automation
  • You are not working with real backend systems
  • You need an enterprise self-hosted MCP platform

You could build this yourself.

Building a production-grade MCP infrastructure means:

  • Running and maintaining your own MCP server
  • Designing and enforcing scoped authentication
  • Building permission boundaries that don't leak
  • Logging, monitoring, and failure handling
  • Hardening it for scale and reliability

Most teams end up stitching together a fragile version of this - custom auth layers, ad-hoc tool wrappers, and brittle permission logic that breaks as they scale.

Building this properly is complex and time-consuming.

Instead plug in TengineAI and ship this week.

Start building with TengineAI.

Give AI safe, scoped access to your real systems - without running your own MCP infrastructure.

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