Keep tool results small enough that an agent can hold what matters, through pagination, projection, and summarisation at the server. Use when tool output floods the context window.
Scanned 9/5/2026
Install to Claude Code
npx -y skills add Amey-Thakur/AI-SKILLS --skill mcp-context-budgeting --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: mcp-context-budgeting
description: Keep tool results small enough that an agent can hold what matters, through pagination, projection, and summarisation at the server. Use when tool output floods the context window.
---
# MCP context budgeting
Every token a tool returns is a token unavailable for reasoning. Servers
that return complete records because they are available push agents into
truncation and forgetting, and the fix belongs at the server rather than
in the client.
## Method
1. **Return what the next step needs.** A list operation returns
identifiers and labels; detail comes from a follow-up call on the one
that matters.
2. **Paginate by default with a modest page size.** Unbounded lists are
the most common source of context floods, and the default should be
safe rather than complete.
3. **Offer field selection.** Letting the caller request specific fields
turns a large record into a small one without a new tool.
4. **Summarise server-side where the detail is rarely needed.** A
summary with a way to fetch the full text is usually better than
returning everything and hoping.
5. **Truncate explicitly and say so.** Silent truncation makes an agent
reason over partial data believing it is complete, which is worse
than an honest marker.
6. **Prefer references to embedded content.** An identifier or URI the
agent can fetch on demand costs a fraction of the content itself.
7. **Measure real response sizes.** Tools are usually built against
small test data and meet production volumes later (see
mcp-server-testing).
## Boundaries
Budgeting shapes what a server returns; the client still controls the
window and may truncate anyway. Aggressive summarisation loses detail
that some tasks need, so a full-detail path must remain. Compact
responses are harder for humans to debug, making logging more important.
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