MCP Token Limits: The Hidden Cost of Tool Overload
Source: Dev.to
The Hidden Cost of Adding More MCP Servers
You add a few MCP servers—GitHub for code, Notion for docs, maybe Slack for notifications. Suddenly Claude feels slower, less helpful, and starts missing the context you explicitly provided. It gives generic answers to specific questions.
A Striking Statistic
- GitHub MCP server alone: ~55 000 tokens across its 93 tool definitions.
- Scott Spence’s measurement: 66 000 tokens consumed before any conversation starts—about one‑third of Claude Sonnet’s 200 k token window.
“Most of us are now drowning in the context we used to beg for.” – CodeRabbit team
Why This Happens
Every MCP server you connect loads its tool definitions into Claude’s context. The formula is brutal:
servers × tools per server × tokens per tool = context consumedReal Numbers from Popular MCP Servers
| MCP Server | Tokens (approx.) | # Tools |
|---|---|---|
| GitHub MCP | 55 000 | 93 |
| Notion MCP | ~8 000 | 15+ |
| Filesystem MCP | ~4 000 | 10 |
| Average tool definition | 300‑600 tokens (name, description, schema, examples) | — |
Typical Power‑User Setup
10 servers × 15 tools avg × 500 tokens ≈ 75 000 tokensThat’s > ⅓ of the context window spent on tool descriptions you may never use.
The Tipping Point
- Cursor enforces a hard limit of 40 tools; more causes problems.
- Claude’s output quality visibly degrades after 50+ tools – the model starts chasing tangents, referencing tools instead of your actual question.
Result: “Forgot” what you told it three messages ago.
Money Matters (Jan 2026)
- Claude Opus 4.5 cost: $5 per million input tokens.
- Team: 5 developers, each with a 75 k‑token MCP load, 10 conversations/day.
| Metric | Calculation | Tokens | Cost |
|---|---|---|---|
| Daily token usage | 75 000 × 5 devs × 10 conv. | 3.75 M | $18.75 |
| Monthly (20 work days) | 3.75 M × 20 | 75 M | $375 |
| With hierarchical routing (1.4 k tokens) | 1.4 k × 5 devs × 10 conv. × 20 | 1.4 M | $7 |
| Savings | — | — | $368 / month (≈ 98 % reduction) |
Token bloat isn’t just expensive—it actively makes your AI worse.
The Real Damage: Relevance Decay
When 100 tool definitions compete with your actual prompt, the signal drowns:
- Irrelevant context (e.g.,
create_github_issue,update_notion_page) dilutes the important code‑bug description. - Model confusion: LLMs have finite attention; processing 75 k tokens of schemas leaves less “mental bandwidth” for your question.
Developer Jamie Duncan: “Treating context windows as infinite resources creates unsustainable systems, just as infinite dependency installation historically bloated software.”
The Team‑Level Problem
Solo‑Developer Solutions
- code‑mode, ToolHive, Lazy Router – expose only two meta‑tools, cutting token usage 90‑98 %.
Scaling to a Team
| Issue | Description |
|---|---|
| Configuration drift | 5 devs → 5 different MCP versions, credentials scattered in Slack, .env files, sticky notes. |
| Onboarding pain | New hire spends ≥ 2 hrs replicating the MCP setup; breaks are inevitable. |
| Security risk | Departing dev leaves API keys for GitHub, Notion, Slack, internal tools. No rotation, no visibility. |
| Lack of governance | No credential vault, RBAC, audit logging, or team isolation. |
Every token‑reduction tool solves the individual problem, but none address the team‑management gap.
The Market Gap: Team‑Centric MCP Management
The Elegant Technical Fix
Instead of loading all tools into context, expose just two meta‑tools:
discover_mcp_tools(query)– searches across all MCP servers for relevant tools.execute_mcp_tool(tool_path, args)– runs the specific tool you need.
Token Math After the Fix
| Before | After |
|---|---|
| 10 servers × 15 tools × 500 tokens = 75 000 tokens | 2 meta‑tools × ~700 tokens = 1 400 tokens |
| 98 % reduction in MCP token usage | — |
Summary
- MCP token bloat eats up context, degrades model performance, and inflates costs.
- Solo solutions (hierarchical routing, lazy loading) cut token usage dramatically but don’t solve team‑level chaos.
- Team‑focused management—centralized config, credential vaults, RBAC, audit logs, and the two‑meta‑tool approach—eliminates both token waste and operational risk.
Bottom line: Reduce the upfront tool load, centralize configuration, and let the AI fetch only the tools it truly needs. This restores context, improves relevance, and saves hundreds of dollars each month.
Context
The “next‑window reclaimed” method is now table stakes – every modern tool implements it.
DeployStack’s implementation is documented in detail at:
https://docs.deploystack.io/development/satellite/hierarchical-routerWhile token reduction helps, it doesn’t solve the team‑level problems that arise when multiple developers share MCP (Managed Cloud Platform) resources.
What Makes MCP Tooling Team‑Ready
| Feature | Why it matters |
|---|---|
| Credential vault | API keys are stored encrypted and auto‑injected at runtime – no more hard‑coded tokens in Slack or source code. |
| One URL for the whole team | Add a single endpoint to your config and everyone gets the same servers, same settings, same tools. |
| Role‑based access | Control who can use which MCP servers. Interns, for example, don’t need production‑database access. |
| Audit logging | Know which tool accessed what data, when, and by whom. |
Individual developers can survive with local configs and manual credential management, but teams cannot.
Options by Team Size
-
Solo developers hitting MCP token limits:
- Use Code‑mode or ToolHive – whichever fits your workflow.
-
Teams (5, 10, 20+ developers):
- Token reduction alone isn’t enough.
- You need credential management, access control, and visibility into what’s happening across your MCP setup.
One URL. Everyone gets the same setup.
No more “works on my machine” for MCP.
Bottom Line
- MCP token limits are a solved problem.
- Team MCP management has been the missing piece—until now.