invoking-tools-and-prompts/token-footprint
Per-Call Token Footprint
The Tokens tab on every result shows what that specific call costs in context-window terms.
Every result in MCPFlo carries a Tokens tab, showing what that specific call costs in context-window terms — separate from the context-budget estimate for the tool’s definition (see Server Detail View & Token Estimator).
What it measures
The Tokens tab on a given result breaks down two figures:
- Response cost — how many tokens the content returned by this call would consume if fed into a model’s context (text, JSON, or any other content block in the result).
- Definition cost — a reminder of what the tool’s own schema/description costs just by being connected, shown alongside the response so you can see both sides of the ledger for that capability in one place.
Why per-call, not just per-tool
A tool’s definition cost is fixed — it’s paid once, just by connecting to
the server. But its response cost varies every time you call it,
depending on the arguments you pass and what the server returns. A
search tool called with a broad query might return a huge result; the
same tool called with a narrow one might return almost nothing. The
per-call footprint lets you see that variance directly, call by call.
Why it’s useful
- Sizing responses before production use — if you’re about to wire a tool into an agent, check whether a typical call returns a response that fits comfortably in your context budget, or whether it needs pagination/truncation on the server side.
- Debugging bloated responses — if an agent using this server is burning through context unexpectedly, replay the same call in MCPFlo (see Replaying Past Calls) and check its token footprint directly.
- Comparing arguments — try the same tool with different inputs to see how the response size (and cost) changes.
How it’s calculated
Token counts are estimated using gpt-tokenizer. Since MCPFlo has no model in the loop, this is always an estimate — actual token usage may differ slightly depending on the specific model and tokenizer used in production.
Related
- See Understanding Responses for how the underlying content is rendered.
- See Server Detail View & Token Estimator for the fixed, per-server definition cost this complements.