Blog · 2026-07-17

A Free LLM Backend for MCP Servers and Agents

The Model Context Protocol (MCP) is great at connecting your agent to tools, files, and data, but it does not think for you. Something still has to reason over those tools and decide what to call next, and that something is a language model. If you are prototyping and do not want to wire up a paid account yet, a free LLM API for MCP lets you fill the model slot with a free key and no billing setup. This post explains how the MCP roles fit together, where the LLM lives, and how to point your host or client at an OpenAI-compatible endpoint with real config and code.

How the MCP roles fit together

MCP is an open standard for connecting AI applications to external tools, data, and prompts. It defines three roles that work together on every turn:

MCP standardizes how the host and servers talk. It deliberately does not ship a model. That is the gap DreamPrompting fills. Anywhere your host or client lets you set an OpenAI-compatible base_url, you can point it at our gateway and get a working MCP LLM backend with one free key and no billing.

Where the free LLM API for MCP plugs in

There are two common places to drop in a free LLM API for MCP. The first is the host or client itself: most agent frameworks read an OPENAI_BASE_URL and OPENAI_API_KEY or expose the same two fields in a config file. The second is MCP sampling, where a server asks the host to run a completion on its behalf. Both cases just need an OpenAI Chat Completions endpoint, which is exactly what this gateway speaks.

The base URL is https://dreamprompting.com/api/v1. Send requests to POST /api/v1/chat/completions, list models at GET /api/v1/models, and check usage at GET /api/v1/quota. Every endpoint needs an API key, and it is free: sign in with Google, copy the key, and use it wherever a key is required. Here is a generic OpenAI-compatible model config that most MCP hosts and agent tools accept:

{
  "provider": "openai",
  "base_url": "https://dreamprompting.com/api/v1",
  "api_key": "YOUR_API_KEY",
  "model": "auto",
  "stream": true
}

The model value can be auto to let the gateway route with automatic failover, or a pinned provider/model string. If your MCP flow relies on function calling, pin a tool-capable model such as groq/openai/gpt-oss-120b so tool calls behave predictably. Browse the current options on the models page.

Driving a model with MCP tool results

When your host runs an MCP tool, it appends the result to the message list and asks the model to continue. That is standard OpenAI Chat Completions, so the free LLM API for MCP handles it with the ordinary OpenAI SDK. This example feeds a tool result back in and lets the model produce the final answer:

from openai import OpenAI

client = OpenAI(
    base_url="https://dreamprompting.com/api/v1",
    api_key="YOUR_API_KEY",
)

# messages built up by your MCP host across the turn
messages = [
    {"role": "system", "content": "You are an agent with MCP tools."},
    {"role": "user", "content": "What is the weather in Oslo?"},
    {"role": "assistant", "content": None, "tool_calls": [
        {"id": "call_1", "type": "function",
         "function": {"name": "get_weather",
                      "arguments": '{"city": "Oslo"}'}}
    ]},
    # result returned by the MCP server, fed back to the model
    {"role": "tool", "tool_call_id": "call_1",
     "content": '{"temp_c": 17, "sky": "cloudy"}'},
]

resp = client.chat.completions.create(
    model="groq/openai/gpt-oss-120b",
    messages=messages,
)
print(resp.choices[0].message.content)

The same call works from the command line, which is handy for smoke-testing your MCP LLM backend before wiring it into the host:

curl https://dreamprompting.com/api/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "auto",
    "messages": [
      {"role": "user", "content": "Say hello from my MCP agent"}
    ]
  }'

Set "stream": true to stream tokens back to the host as they arrive. The rate limit is 100 requests per minute per IP, plus a rolling 24 hour account quota of 5,000 requests and 500,000 tokens, which is plenty for development and light agent traffic. Full request and response details live in the API docs, and the MCP integration page covers host-specific setup.

Tool calling and sampling considerations

Tool calling is a property of the underlying model, not the gateway. When you use model: auto, routing may land on a model that does not emit function calls, so pin a tool-capable model for any MCP flow that depends on them. For MCP sampling, treat the server request as an ordinary completion and forward it to this endpoint through your host. Because the free tier is fair-use, a production MCP deployment with heavy, sustained traffic should bring its own upstream provider keys rather than lean on the free pool.

FAQ

Does it support tool calling? Yes, when the selected model supports it. Pin a tool-capable model like groq/openai/gpt-oss-120b instead of auto so function calls are reliable across your MCP turns.

What about MCP sampling? Sampling is just a completion the server asks the host to run. Point that completion at https://dreamprompting.com/api/v1 and it works like any other Chat Completions call.

Is it really free? Yes, on a fair-use basis, and the API key is free too. Sign in with Google and copy one in seconds, no credit card. For heavy production MCP usage, bring your own upstream keys.

Ready to give your agent a brain? Read the MCP setup guide and the API documentation, then drop the base URL and your free key into your host and start reasoning over tools for free.

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