How big is your context window — and what do 178 tool definitions cost?

Your context window is the model’s working memory: the maximum number of tokens it can hold and reason over in a single request. Everything — the system prompt, your messages, retrieved documents, and every connected tool’s definition — has to fit inside it. A current frontier model commonly has a window around 200K tokens. Big, but finite.

Here’s the part that surprises people: a large chunk of that window can be gone before you say a word.

Where the tokens go

When a host connects an MCP server, it takes each tool’s definition — the name, the description, and the JSON-Schema for its input — and serializes all of it into the model’s context so the model knows the tool exists and how to call it. That’s not free. A single richly-documented tool with a nested input schema can easily be a few hundred tokens. Multiply by the number of connected tools and the bill adds up fast.

A heavily-connected agent with ~178 tools can spend roughly 30–45K tokens — 15–25% of a 200K window — purely on tool definitions, before the first user message.

That’s the headline mcp-glimpse renders: “your 178 tools consume ~40K of your 200K before you say a word.” It’s computable from the snapshot alone, because the snapshot already contains every tool’s name, description, and schema.

How to estimate it — honestly

Token counting is approximate, and being honest about how approximate is part of doing it right.

• Tokenizers differ by model family. Claude, tiktoken (OpenAI), and SentencePiece (many open models) split text differently — but on prose and JSON the spread is roughly ±10–25%, not orders of magnitude. A sound estimator uses one computational base (e.g. tiktoken’s o200k_base, which is cheap and pure-Python) and applies a per-family correction, then shows the result as a range (“~38–46K”) with a method footnote — never a false-precision single number.
• Hosts reformat definitions before the model sees them. The host wraps tool definitions in its own prompt syntax, so any estimate is approximate regardless of tokenizer. State that caveat rather than hide it.
• Caching reduces cost, not occupancy. Prompt caching makes re-sending unchanged tokens cheaper and faster — but those tokens still occupy window space. A cache hit doesn’t give you the room back.

Why the number matters

Every token spent on a tool you never call is a token unavailable for the actual task — the document you’re analyzing, the conversation history, the reasoning room the model needs. Context-cost accounting turns “I connected a lot of servers” into a concrete, per-server number you can act on: prune the servers you don’t use, and you get real window back.

To see your own figure, connect mcp-glimpse and run introspect_mcp_context with your full inventory. It renders a headline tile — total estimated cost against your window — plus a per-server breakdown, so you can see exactly which connectors are the expensive ones. For how that fits into the whole report, see Reading an introspection report.