Measure and grade (A to F) the context-token cost of an MCP server's tool surface, with CI gates to stop it bloating.
View on GitHub ↗Measure and grade the context-token cost of an MCP server's tool surface.
Every tool an MCP server exposes (its name, description and JSON input schema) is injected into the model's context on every turn. A server with forty verbose tools can quietly burn thousands of tokens before the user has typed a word. That cost is invisible in most workflows and it is the one thing a good MCP server should keep lean. mcp-context-budget makes it visible: it connects to a live server over stdio, adds up the token footprint of the whole tool surface, grades it from A to F against a budget, and tells you exactly what to trim.
Zero third-party dependencies. Pure standard-library Python. MIT licensed.
Part of a small MCP-quality toolchain: correctness (mcp-conformance-kit), surface stability (mcp-surface-diff), security hygiene (mcp-server-audit), performance (mcp-load-lab), and an aggregate grade (mcp-scorecard). This tool adds the missing dimension: context efficiency.
The 2026 MCP roadmap calls out progressive tool discovery precisely because large tool surfaces do not scale: they crowd the context window, raise latency and cost, and make the model slower to pick the right tool. Before you reach for progressive discovery, it helps to know how heavy your surface actually is, and which tools are the offenders. That is what this tool reports.
Or run straight from a checkout with no install at all:
A sample server is bundled so you can try it immediately:
From the project README.
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