Tool Discovery. Context Compression. Parallel Execution. Smart Caching.
The MCP Scalability Problem
The Problems
Your agent pays the price.
Your agent doesn't need 10,000 tools. It only needs the right one.
Stop sending 50,000 rows into your context window.
Run MCP tools in parallel. Not one by one.
Stop repeating expensive tool calls from scratch.
AnyMCP Impact
The Solution
Sits between your agent and MCP servers. Handles discovery, compression, caching, and orchestration so your agent stays lean and fast.
Search tools dynamically. Load only what matters.
Prevent oversized tool outputs from polluting context.
Large responses saved as files. Agents receive metadata instead.
Reuse expensive tool calls automatically.
Run independent tool calls concurrently.
Architecture
See The Difference
The same 500,000-row query. Two very different outcomes for your agent.
{
"rows": 500000
}
// 500,000 rows dumped
// into agent context
// Token cost: ~$2.50
// Window: nearly full{
"file": "query.parquet",
"rows": 500000,
"summary":"Top 10 customers..."
}
// Metadata only in context
// Full data on disk
// Token cost: ~$0.01
// Agent stays focusedBlog
Dynamic discovery keeps context lean and selection fast.
Compression and materialization protect your agent's reasoning.
Parallelize execution and cut response times by 3x.
Protocol vs runtime — and why the distinction matters.
The next frontier for agent infrastructure.
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