Finds the right servers & tools
Semantic + keyword search over a registry of MCP servers and their tools.
// AI MIDDLEWARE · MCP CONTROL PLANE
MCPBank
Discovery & Routing Layer for AI Agents
We tame the chaos of AI tooling, cut inference costs, and scale agents — lazy-loading the right tools on demand instead of flooding the context window.
Too many tools is the new context bottleneck.
AI agents are drowning in hundreds of MCP tools. Limited LLM context windows drive up inference cost, slow responses, and trigger hallucinations.
- Models have a limited context window
- More MCP servers and integrations every week
- Loading every tool upfront is expensive
- The agent struggles to pick the right tool
- Inference cost and deployment complexity rise
0
tools flooding the context of a single agent across 6 MCP servers — before it does anything useful.
A Discovery + Routing layer between your agent and MCP.
MCPBank sits between the AI agent and your MCP servers. It lazy-loads tools on demand, cuts token overhead by ~92%, and stays fully compatible with existing MCP servers out of the box.
Loads only on demand
Lazy schema loading — the model pulls a tool's schema only when it actually needs it.
Cuts token overhead
Keeps the input context tiny, so inference is cheaper and tool selection is sharper.
Works with existing MCP
No rewrites. Drop it in as middleware between your agent and the integration ecosystem.
Two modes. One shared core.
A real MCP server speaking stdio / JSON-RPC 2.0 — with a Discovery Mode for browsing and a Dynamic Mode for just-in-time tool loading.
LLM / AI Client
MCP · stdio / JSON-RPC 2.0
Discovery Mode
mcpd_find
mcpd_list
mcpd_get_schema
Dynamic Mode
find_tools
LazyPool
Shared Core
Registry (mcpd-registry)
KeywordSearchEngine
ToolSearchEngine
MCPBankConnector
HybridSearch (TF-IDF + sentence-transformers)
Transport
stdio
streamable-http · SSE
~92% less token overhead — before the first tool call.
In the Lazy Schema Loading scenario (v0.3.0) the model fetches only the schemas it needs, instead of the full toolset of every connected MCP server.
0
fewer tokens of input context — 6 servers loaded directly vs MCPBank Discovery Mode before connect.
~4,778all 6 servers loaded
~305MCPBank Discovery
| Scenario | Tools in context | ~Tokens |
|---|---|---|
| All 6 servers loaded directly | 78 | ~4,778 |
| Discovery Mode (before connect) | 4 | ~305 |
| Discovery Mode (after connect — 1 server) | 4 + 20 | ~1,578 |
| Scenario | Tools in context | ~Tokens |
|---|---|---|
| All 6 servers loaded directly | 78 | ~4,778 |
| Dynamic Mode (before find) | 1 | ~100 |
| Dynamic Mode (2 found tools) | 3 | ~300 |
Value proposition
Lower token cost
Pay for the tools you use, not the ones you might.
Smaller input context
Keep the window lean and the model focused.
Faster, sharper tool selection
Fewer distractors means the right call, more often.
Less chaos at scale
Hundreds of integrations stop fighting for context.
Enterprise agent scaling
A control plane your fleet of agents can grow into.
Why now?
An explosion of B2B AI agents. MCP became a global standard under the Linux Foundation — with OpenAI, Microsoft, Google and AWS behind it. The orchestration layer is still unclaimed.
- Explosion of AI agents
- Integration standardized by MCP
- Surge in tool-based workflows
- Mounting pressure on inference cost
- Agent infrastructure / orchestration is the new battleground
Why “AI Middleware” commands premium valuations.
In every platform shift, the biggest, most durable returns came from infrastructure — the pickaxe sellers — not the end apps. MCPBank is a gateway, not another AI wrapper.
$625M
$2.0B
$1.25B
$300M
Open at the base. Paid at the top.
Open tool for everyone
The core Discovery & Routing layer, open-source, free for individual developers — driving adoption.
Corporate & enterprise license
Control plane, security, analytics and support — the paid layer for companies running agents at scale.
- 01Development
- 02Early investor
- 03Marketing & expansion
- 04Acquisition by big tech
Stop drowning your agents in tools.
- ~92% LLM token savings on tool queries
- Enterprise-ready scalability
- Full compatibility with the MCP ecosystem