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Auto pick the cheapest cross chain bridge in Python with OpenChainBench

#crypto #python #defi #api

Why bridge selection matters more than you think

If you build a dapp that moves USDC across chains, you probably picked a bridge provider two years ago and never looked back. That decision is quietly costing your users money. Independent measurements show a fifteen times difference between the cheapest and the most expensive bridge on a $300 USDC transfer. NEAR Intents leads at 0.058 percent effective cost. deBridge trails at 0.897 percent. On a $10,000 transfer, that gap is roughly $84 out of your users' pockets for no additional service.

This post walks through how to auto pick the cheapest bridge at runtime using OpenChainBench, a public benchmark that measures six major bridges every minute from three regions. The full leaderboard lives at https://openchainbench.com/benchmarks/bridge-fee. We look at two approaches. First, a plain REST call for classic dapps. Second, an MCP integration for AI agent workflows.

Approach 1: Query the REST API from Python

The benchmark exposes every ranking as JSON via a stable endpoint. No API key required.

import requests

def get_cheapest_bridge() -> str:
    """Return the name of the cheapest cross chain bridge right now."""
    response = requests.get(
        "https://openchainbench.com/api/stat/bridge-fee",
        timeout=10,
    )
    response.raise_for_status()
    data = response.json()
    return data["leader"]["name"]

if __name__ == "__main__":
    leader = get_cheapest_bridge()
    print(f"Cheapest bridge right now: {leader}")
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Expected output:

Cheapest bridge right now: Near Intents
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The endpoint returns the full ranking too, so you can route by health status or fall back if the leader is temporarily down.

import requests

def rank_bridges() -> list[dict]:
    response = requests.get(
        "https://openchainbench.com/api/stat/bridge-fee",
        timeout=10,
    ).json()
    return [
        {"name": r["name"], "effective_cost_pct": r["ms"]["p50"]}
        for r in response["rankings"]
    ]

for bridge in rank_bridges():
    print(f"{bridge['name']:15} {bridge['effective_cost_pct']:.3f} percent")
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Expected output:

Near Intents    0.058 percent
Relay           0.134 percent
Across          0.136 percent
Mobula          0.221 percent
LI.FI           0.423 percent
deBridge        0.897 percent
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Now you have live data. Route your users through the cheapest available bridge and cache the result for five to ten minutes to avoid hammering the endpoint.

Approach 2: MCP integration for AI agents

If you build an AI trading agent or a chatbot that helps users move funds, plain REST calls put the routing logic in your code. You want the LLM to reason about bridge selection dynamically based on context. That is where Model Context Protocol comes in.

OpenChainBench exposes an MCP server that any MCP compatible client can consume. Claude Desktop, Cursor, and other MCP hosts can query the benchmark data natively through function calls, without you writing wrapper code.

Register the server in your MCP client:

claude mcp add openchainbench https://openchainbench.com/api/mcp/mcp
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The server exposes three tools: list_benchmarks, get_benchmark, and query_prom. Once registered, your AI agent can answer questions like "which bridge is cheapest for a $300 USDC transfer from Solana to Base right now" by calling the tools directly, no extra plumbing needed.

Example agent interaction:

User: I need to move $500 USDC from Solana to Base. What is the cheapest option?

Agent: Based on OpenChainBench live data, NEAR Intents is currently the cheapest at 0.058 percent effective cost (fees plus slippage plus destination gas). Relay is second at 0.134 percent. On $500, the difference is about $0.38 in your favor with NEAR Intents.

The agent pulls fresh data at query time, so it never quotes stale numbers.

Comparison of the two approaches

Approach Best for Setup effort Data freshness
REST API Classic dapps, backend routing 5 minutes Real time
MCP server AI agents, chatbots, Claude Desktop 2 minutes Real time

Both approaches are free and require no API key. Data is published under CC BY 4.0.

FAQ

Which cross chain bridge is currently the cheapest for USDC?

NEAR Intents leads the OpenChainBench bridge fee bench at 0.058 percent effective cost for $300 USDC transfers across Solana, Base, and Arbitrum corridors. Relay is second at 0.134 percent, Across at 0.136 percent.

What does effective cost include?

The benchmark counts bridge fee plus exchange slippage plus destination gas required to finalize the transfer. Headline fee alone is misleading because bridges recoup margin through slippage.

Is the OpenChainBench API rate limited?

The public REST endpoint has generous limits sufficient for backend routing use cases. For high volume production, cache the ranking for five to ten minutes.

Can I contribute a new bridge to the benchmark?

Yes, the harness is open source at https://github.com/ChainBench/OpenChainBench. Open an issue with the bridge you want added and the corridor to measure.

Does the MCP server work with clients other than Claude Desktop?

Yes, any MCP compatible client works: Cursor, Windsurf, generic MCP hosts, or custom LangChain and LlamaIndex agents.

Companion analysis

For the editorial deep dive on why bridge markets are opaque and what it means for builders, see the companion piece on Substack:
https://substack.com/home/post/p-208526753

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