Running Multiple MCP Tools in Parallel
Ask any agent to “research a competitor” and watch what happens. It searches the web. It looks up Crunchbase. It checks Twitter. It reads documentation. One at a time.
The Serial Trap
Current MCP clients execute tool calls sequentially by default. The agent says: call Tool A, wait for the result, process it, call Tool B, wait, process, call Tool C...
The problem is that most tool calls are I/O-bound. They wait on network requests, database queries, or API responses. During that wait, the agent and the runtime are doing nothing useful.
For a task that requires 5 independent tool calls, each taking 3 seconds, the total wall time is 15 seconds. But the actual compute time is a few milliseconds. The user waits 15 seconds for what could be 5 seconds of wall time.
Why Agents Don't Parallelize
Agents don't parallelize tool calls because the MCP protocol doesn't require it. Each tool call is a request-response cycle. The agent has to issue one call, get the result, then decide what to do next.
Some agents could theoretically batch their calls, but in practice:
- The agent doesn't know which calls are independent until it sees the first result
- Batching logic adds complexity to the agent's reasoning loop
- Most MCP clients don't expose a “run these in parallel” API
How a Runtime Layer Changes the Equation
A runtime layer like AnyMCP sits between the agent and the MCP servers. It can observe the call pattern, identify independent calls, and execute them concurrently without the agent doing anything special.
Here is how it works in practice:
- The agent decides it needs to call three tools: search the web, search GitHub, and check documentation.
- These calls are submitted to the runtime layer, which recognizes they are independent.
- The runtime executes all three via Promise.all or equivalent.
- Results are collected and returned to the agent as a batch.
The agent sees a 3x speedup with zero code changes. It still thinks it called tools one at a time. The parallelism happens transparently underneath.
Real-World Impact
In our benchmarks, parallel execution reduces end-to-end latency by 60-70% for multi-tool tasks. A research query that takes 15 seconds serially completes in 5 seconds with parallelism.
For agent developers, this is the difference between a tool that feels slow and one that feels instant. And the implementation complexity is hidden in the runtime — the agent developer doesn't need to think about it.
The Bottom Line
Serial execution is the default because it is simple. But in a world where agents routinely call 5-10 tools per task, it is the single biggest source of unnecessary latency. A runtime layer that parallelizes transparently is the simplest path to dramatically faster agents.