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Decision guide

MCP token optimization

Every tool in an MCP server adds its full schema to every request. As agents get more capable, they need more tools. More tools means linearly increasing cost per call.

Hidden cost driver | Five strategies | Scaling problem

What MCP tools cost

Every tool in an MCP server sends its complete JSON schema -- name, description, parameter definitions, return types -- with every request. Ten tools might add 2,000 tokens. Fifty tools add 10,000 or more. Unlike conversation history, tool schemas are not summarized or truncated. They are sent in full, every time.

The scaling problem

As AI agents become more capable, they need access to more tools. A customer support agent might start with 5 tools and grow to 50. Each tool added increases the per-call cost linearly. This is the opposite of the efficiency curve teams expect when scaling AI systems.

At GPT-4o pricing ($2.50 per million input tokens), 50 tools at 200 tokens each costs $0.025 per call. At 10,000 calls per day, that is $7,500 per month in tool schema tokens alone. And this cost grows every time a new tool is added.

Five optimization strategies

Each approach has different tradeoffs. The right choice depends on your tool count, call volume, and engineering capacity.

Lazy loading

Only send tools after intent classification. The agent first determines what category of action is needed, then loads only the relevant tool subset. Reduces tokens significantly but adds a classification step and latency.

Tool grouping

Organize tools into logical subsets and send related groups together. Simpler than full intent classification but still requires manual grouping and maintenance as tools change.

Schema compression

Reduce the token count per schema while preserving function signatures and parameter definitions. Automated, no routing logic needed, highest reduction ceiling at 85-97 percent.

Caching

Reuse compressed or resolved schemas across calls. Effective when the same tool set is used repeatedly. Does not help with cold starts or frequently changing tool registries.

Hybrid approach

Combine tool filtering with schema compression. Filter first to reduce the set, then compress what remains. This produces the highest total reduction: fewer tools, each one smaller.

Where NOVA fits

NOVA Token Optimizer works as an MCP server itself, sitting between the client and your tool servers. It compresses schemas inline with 85-97 percent reduction and less than 50ms latency. No changes to your existing tools or MCP configuration required.

For teams that also need intelligent tool selection (the lazy loading approach, automated), DeepNova handles grounded retrieval of the right tools for each request. The NOVA Platform bundles both products at 20% off.

For a broader look at how to reduce AI API costs beyond MCP optimization, see our cost reduction guide.

Frequently asked questions

Why are MCP tools expensive?
Every tool in an MCP server sends its complete JSON schema with every request. Ten tools might add 2,000 tokens. Fifty tools add 10,000 or more. Unlike conversation history, tool schemas are not summarized or truncated.
How can I reduce MCP token costs?
Five strategies: lazy loading (send tools after intent classification), tool grouping (send related subsets), schema compression (85-97% reduction), caching (reuse compressed schemas), and hybrid approaches combining filtering with compression.
What is the best MCP optimization strategy?
Schema compression offers the highest reduction ceiling at 85-97 percent with the lowest implementation effort. A hybrid approach combining tool filtering with schema compression produces the highest total reduction.