Large model power. Small model bill.
Stop paying for bloated tool definitions. NOVA uses proprietary compression technology to reduce your tokens by 85-97%, so you pay dramatically less.
Available as REST API or MCP Server
Works with any LLM:
MCP clients:
Smaller context = Better AI performance
Less tokens = less processing time
3 clear tools vs 17 confusing ones
Less noise = clearer signal
Fit 10x more actual data
When you load 17 tools into an LLM's context, you're adding ~10,500 tokens of "noise" before the AI even sees your question. This causes attention dilution, tool confusion, and slower inference. NOVA consolidates similar tools into parameterized super-tools, reducing 17 tools to just 3 while preserving all functionality. The result: your AI is faster, smarter, and more reliable.
Before vs After NOVA optimization
Benchmarks run with 17 HomeLift tools consolidated to 3 NOVA super-tools. Run your own benchmarks →
Calculate how much you'll save with NOVA.
$45/month at 100k requests
$4.50/month - You save $40.50
Whether you call an LLM API or an MCP server sends tool definitions to your AI assistant, ALL tool definitions go with EVERY request. 20 tools × 500 tokens each = 10,000 tokens before you even say "Hello."
At $3/million tokens, that adds up fast.
Choose your integration method
POST your tool definitions to our API. One simple request.
Proprietary compression reduces tokens while preserving functionality.
Use the optimized tools with Claude, GPT-4, or any LLM. Pay less.
# Before: 15,247 tokens
response = httpx.post("https://optimizer.davisai.ai/optimize/tools", json=my_tools)
optimized = response.json()["optimized_tools"]
# After: 1,842 tokens - saved 88%
# Use with Claude
client.messages.create(tools=optimized, ...)Real headaches that agent development teams deal with every day
Every tool you register consumes tokens just by existing. A single MCP server with 20 tools eats 10,000+ tokens before you even say hello. Connect 3-5 servers and you're burning 40,000-70,000 tokens per request on metadata alone.
Tool selection accuracy drops from 95% with 5 tools to 74% with 20+. Wrong tool calls cascade into wasted tokens, retries, and production incidents. Research shows even improving model reasoning makes tool hallucination worse.
Cleaner tools = clearer signal. 3 unambiguous tools vs 17 overlapping ones.
Tool definitions compete with your actual data for context space. When 30-50% of every context window is consumed by tool metadata, your AI has less room for the conversation that matters. Performance degrades well before you hit any token limit.
That space is now yours for actual data, code, and conversation.
Token costs vary per request. When agents get stuck in retry loops, tool definition overhead multiplies the damage. Product teams can't model unit economics when tool tokens are 30-50% of every API call.
Predictable tool footprint. Unit economics you can model.
Larger payloads mean longer Lambda execution times, more network bandwidth, more log storage, and tighter rate limit windows. The infrastructure cost of bloated tool definitions often matches the token cost itself.
More requests within rate limits. Less log storage. Lower cloud bills.
Oh, and one more thing...
Because your tool tokens drop by 85-97%, your LLM API bill drops by the same amount.
For a team making 50K calls/month, that's $2,000-15,000/month in savings. Almost forgot to mention that.
What we don't do: We don't prevent infinite loops (but they cost 90% less). We don't handle auth across MCP servers. We don't do observability. We solve tool optimization — and we're the best at it.
See how much you could save with NOVA
Everything you need to optimize your AI costs
Proprietary compression preserves functionality while dramatically cutting tokens.
Claude, GPT-4, Gemini, Mistral, and any LLM that uses tool definitions.
Sub-50ms response time. With caching, repeated requests are instant and free.
Send your tools, get optimized tools back. No setup, no configuration.
Identical requests are cached. Second request onwards is instant and free.
Track your savings in real-time. See exactly how much you're saving.
First-class MCP server with 6 optimization tools. Works with Claude Code, Cursor, Windsurf, and any MCP client.
One subscription, two access methods. Same engine, same savings. Use whichever fits your workflow.
Start free, scale as you grow
500K tokens/month
Perfect for testing
10M tokens/month
For solo developers
100M tokens/month
For growing teams
1B tokens/month
For platform teams
All plans include: REST API + MCP Server • Unlimited calls • Fast support • 30-day money back
Need more? White-label and custom solutions available
No. We only compress tool definitions, not your actual messages. The AI still knows exactly what tools are available and how to use them.
Claude, GPT-4, GPT-3.5, Gemini, Mistral, and any LLM that uses tool/function definitions.
We count input tokens - what you send to us. We use tiktoken (same tokenizer as GPT-4).
Free tier and trial users must upgrade to continue. Paid tiers can upgrade or pay small overage fees. We'll warn you at 80% usage.
Yes! Start with a 14-day free trial (500K tokens, no credit card required). After the trial, continue on the Free tier (500K more tokens for the rest of the month) or upgrade to a paid plan for your full allotment.
Yes. And we offer a 30-day money-back guarantee on all paid plans.
MCP (Model Context Protocol) is a standard for connecting AI assistants to external tools. Instead of making HTTP requests, your MCP client calls our optimization tools directly. Same engine, same savings — different integration path. Use the REST API for custom backends, MCP for AI coding assistants.
Any MCP client that supports Streamable HTTP transport: Claude Code, Cursor, Windsurf, Cline, Continue, and more. One line of config is all you need.
No. Every plan includes both REST API and MCP Server access. One subscription, both access methods, shared token pool.
Fix your tool bloat, boost AI accuracy, and start saving — via REST API or MCP Server.
Get Started Free - No Credit Card Required