How to Scale MCP to Thousands of Tools Without Destroying Your Budget
Your MCP just became a memory hog. And it’s quietly burning your budget.
If your Model Context Protocol (MCP) catalog is growing into the hundreds—or thousands—of tools, you’re already facing the next invisible scalability wall: token bloat.
And it’s not a theory anymore.
Claude shipped its Tool Search Tool — a long-requested feature that dynamically discovers and loads tools on demand. The MCP community is actively debating lazy loading, dynamic discovery, and context minimization. There’s even a formal proposal now: 👉 SEP-1576: Mitigating Token Bloat in MCP
We talked about this issue back in August 2025: why too many tools can break your AI, and now it’s time to get practical. This is the moment where MCP moves from “cool demo tech” to real platform engineering.
If you don’t solve token bloat early, your MCP will become:
- Expensive to run
- Slow to reason
- Hard to govern
- Risky for enterprise compliance
- Hallucination-prone
Let’s break down what’s happening — and how to design MCP systems that scale cleanly.
Token Bloat in MCP: Why Your Tool Catalog Is Becoming a Cost, Latency, and Security Problem
Business impact first
Token bloat creates some immediate problems:
| Problem | Business Impact |
|---|---|
| Cost explosion | Every request loads thousands of unused tool schemas |
| Latency | Larger prompts = slower inference |
| Reasoning degradation | LLMs perform worse with noisy context |
| Security risk | Tools leak into contexts they should never be in |
| Compliance failure | Shadow tools get injected into AI workflows |
At enterprise scale, token bloat is not a technical issue. It’s a platform liability.
What Is Token Bloat in MCP?
Simple definition
Token bloat happens when your MCP client sends too many tool definitions into the model context — even when only one tool is needed.
Instead of:
“Here is the one tool you need.”
You send:
“Here are 4,200 tools. Pick one.”
Each tool contains:
- Name
- Description
- JSON schema
- Input types
- Output types
- Metadata
Multiply that by thousands.
Now your model prompt is tens of thousands of tokens before reasoning even starts.
That’s token bloat.
Why MCP Is Uniquely Vulnerable?
MCP is powerful because it standardizes tools.
But that same power creates a new failure mode:
MCP encourages tool catalogs
- Public registries
- Internal enterprise registries
- Department registries
- Vendor registries
- Marketplace registries
Soon you have:
- CRM tools
- ERP tools
- Cloud tools
- DevOps tools
- Security tools
- Finance tools
- HR tools
All MCP-compatible.
Now your agent needs to choose one.
So naïve implementations load everything.
The Breaking Point: Thousands of Tools
The MCP community hit this wall in 2025.
Which led to:
- SEP-1576: Mitigating Token Bloat in MCP
- Feature request: Lazy Loading for MCP Servers
- Claude shipping Tool Search Tool
This is the signal:
The ecosystem is moving from static tool injection to dynamic discovery.
Claude’s Tool Search Tool: The New MCP Pattern
Claude’s new Tool Search Tool introduces an important shift.
Instead of loading all tools into context:
- Claude receives a task
- Claude searches a tool registry
- Claude selects relevant tools
- Only those tools are loaded into context
- Execution begins
This mirrors how real software works:
- You don’t load every library
- You import what you need
This is the path to the future of MCP.
The Core MCP Scaling Problem
Let’s formalize it.
Your MCP system has four layers:
User → Agent → MCP Client → MCP Registry → MCP Servers
The failure happens at:
MCP Client → Model Context
Where:
- The MCP client injects tool schemas
- The model must reason over them
- The prompt explodes
SEP-1576: The Community’s Wake-Up Call
SEP-1576 proposes:
- Lazy tool loading
- Partial schemas
- Tool summaries
- On-demand expansion
- Context budgets
It acknowledges a core truth:
MCP systems must become context-aware platforms, not dumb tool injectors.
Why Token Bloat Breaks AI Reasoning
LLMs do not reason better with more tools.
They reason better with:
- Clear options
- Minimal noise
- Focused schemas
- Small decision trees
Token bloat causes:
- Tool confusion
- Hallucinated tool calls
- Slower planning
- Lower accuracy
- Higher cost
This is not theoretical. This is measurable.
MCP Needs Platform Architecture — Not Just Protocols
This is where most teams fail.
They treat MCP like:
“Just expose APIs as tools.”
But MCP is not just transport. It is an AI execution platform.
You need:
- Tool governance
- Context orchestration
- Discovery
- Authorization
- Policy
- Auditing
This is why naive MCP deployments become Shadow MCP.
Shadow MCP: The Silent Compliance Risk
Shadow MCP is when:
- Teams spin up MCP servers ad-hoc
- No registry
- No governance
- No audit
- No access control
- No policy
Now tools silently enter AI contexts.
This is Shadow IT — but for AI.
Token bloat is often the first symptom.
Best Practices for Token-Efficient MCP Design
Let’s get practical. Here are nine best practices to scale MCP tool catalogs without destroying your budget:
1. Never Load All Tools
Rule: No MCP client should ever inject all tools into context.
Ever.
Use:
- Search
- Discovery
- Ranking
- Filtering
2. Implement Tool Discovery
Your agent should:
- Understand the task
- Query a registry
- Receive ranked candidates
- Load only relevant tools
This is exactly what Claude’s Tool Search Tool enables.
3. Use Tool Summaries First
Instead of loading full schemas:
Start with:
- Name
- Description
- Capability tags
Then expand only the chosen tool.
4. Enforce Context Budgets
Define:
- Max tool count per request
- Max schema tokens
- Max metadata size
Reject or paginate when limits are exceeded.
5. Add Policy-Based Tool Filtering
Not every agent should see every tool.
Filter by:
- Role
- Tenant
- Department
- Environment
- Compliance level
6. Add Tool Ranking
Use:
- Semantic search
- Tags
- Domain
- Past usage
- Cost
- Latency
Return top N candidates only.
7. Add Tool Versioning
Never inject multiple versions of the same tool.
Use:
- Stable aliases
- Deprecation policies
- Controlled rollouts
8. Use Partial Schemas
Load:
- Input shape only
- Output later
- Examples on demand
9. Add Audit Trails
Track:
- Tool discovery
- Tool selection
- Tool execution
- Tool failures
This becomes your AI governance layer.
MCP Is Becoming an AI Operating System
This is the real shift.
MCP is no longer:
“A protocol for tools.”
It is becoming:
“An operating system for AI execution.”
Which means:
- Scheduling
- Discovery
- Security
- Connect Authority
- Cost control
- Governance
- Observability
Token bloat is the first scaling signal.
Where HAPI MCP Fits in This Architecture
HAPI MCP Stack was designed for exactly this future.
Not as a toy MCP wrapper. But as a Headless API platform for AI execution.
Core principles:
- API-first MCP servers
- Registry-first discovery
- Connect Authority enforcement
- Zero-trust architecture
- Enterprise-grade deployment
- Airgap-ready
- Cloud-ready
- VM-first friendly
HAPI treats MCP as platform infrastructure.
Not prompt glue.
The Future: Tool Search, Lazy Loading, and AI Gateways
We’re entering the next phase of AI platforms:
| Old | New |
|---|---|
| Static tools | Dynamic discovery |
| Prompt injection | Context orchestration |
| Manual schemas | Search-driven tools |
| Flat catalogs | Ranked registries |
| No governance | Policy enforcement |
| Shadow MCP | MCP Authority |
This is exactly the direction Claude is moving.
This is exactly what SEP-1576 proposes.
And this is exactly what enterprises need. HAPI MCP Stack is built for this future.
Final Takeaways: How to Build MCP Systems That Scale
If you're building with MCP today, here is your roadmap:
- Adopt tool discovery
- Implement lazy loading
- Use a registry
- Add a connect authority
- Enforce context budgets
- Filter by policy
- Rank tools
- Audit everything
- Kill shadow MCP
- Treat MCP as platform infrastructure
MCP Without Token Discipline Will Fail
Token bloat is not a bug. It’s a design failure.
The teams that solve it now will own the next generation of AI platforms.
The teams that ignore it will build expensive demos that never scale.
The question is simple:
Are you building MCP tools...
Or are you building an MCP platform?
If you're serious about AI at scale, start designing for token efficiency now.
Because your AI is only as smart as the context you give it.
Be HAPI, and Go Rebels! ✊🏼
FAQ: Token bloat in MCP
Q: What is token bloat in MCP?
A: Token bloat in MCP happens when an MCP client injects too many tool definitions (schemas + descriptions) into the model context, inflating prompt size before reasoning even starts.
Q: Why is token bloat a problem for MCP systems?
A: Token bloat increases cost per request, adds latency, degrades tool selection quality, and expands the security/compliance surface because more tools appear in context than are needed.
Q: What causes token bloat in MCP tool catalogs?
A: The most common cause is static tool injection: loading the full tool catalog into the prompt on every request instead of discovering and loading only what’s needed.
Q: What is lazy loading for MCP tools?
A: Lazy loading means you load tool schemas only after the agent has identified relevant tools (usually via search/ranking), rather than preloading every tool into context.
Q: What is tool discovery in MCP?
A: Tool discovery is a step where the agent queries a registry (or authority layer) to retrieve a small, ranked set of candidate tools for a task, then loads only those.
Q: How do you mitigate token bloat when you have thousands of MCP tools?
A: Use search-based discovery + ranking, enforce tool/context budgets, filter tools by policy (role/tenant), and expand schemas on demand (summaries first, full schemas later).
Q: What is a context budget for MCP tools?
A: A context budget is a hard limit on how many tools or schema tokens can be included in a request. It keeps cost and latency predictable and prevents runaway prompts.
Q: Should I use FAQPage or QAPage schema for MCP Q&A content?
A: Use FAQPage when each question has a single authoritative answer you provide. Use QAPage only when users can submit answers (like forums).
Q: What is shadow MCP and how does it relate to token bloat?
A: Shadow MCP is ungoverned MCP tooling (servers/tools) that appear without centralized discovery, policy, or audit. It often shows up first as tool sprawl and token bloat.
Q: What’s the best MCP architecture pattern to scale tool catalogs?
A: Registry-first discovery with an authority/policy layer: the agent searches a catalog, gets a small ranked shortlist, and only then loads the selected tool schemas for execution.
References:
- Feature Request: Lazy Loading for MCP Servers and Tools
- https://venturebeat.com/orchestration/claude-code-just-got-updated-with-one-of-the-most-requested-user-features
- Tool search tool
- X Post with more details.
- SEP-1576: Mitigating Token Bloat in MCP