A reported measurement of Chrome DevTools MCP put its tool definitions at roughly 18,000 tokens—about 9% of a 200,000-token context window—before the agent did useful work. That figure, attributed to Pi creator Mario Zechner by The New Stack, describes one server and measurement context, not every MCP setup. Pi’s workaround is to defer most tool definitions: the agent discovers and calls tools through JavaScript when needed instead of loading every schema into the prompt up front.
What the 18,000-token figure means
The figure is the reported cost of Chrome DevTools MCP’s tool definitions, not the total cost of using the server. It concerns the descriptions of available tools entering the model’s context before useful work begins. Tool results and the user’s task are separate sources of tokens.
The New Stack report attributes the approximate measurement to Mario Zechner. It frames 18,000 tokens as about 9% of a 200,000-token context window. The same article reports that Playwright MCP used about 13,700 tokens to describe 21 tools, or roughly 6.8% of that same-sized context window. These are reported examples, not a controlled survey or figures that apply to every client, model, server version, or configuration.
How Pi keeps tool definitions out of the prompt
Pi’s Codemode approach changes when tool schemas are exposed. By default, MCP tool definitions stay out of the model’s prompt. The system prompt instead gives a one-line description of each server, and the agent can use Codemode to discover and call tools from JavaScript as needed, returning selected output to the model.
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This means the agent does not need every tool declaration loaded at the start of a task. It can first identify a relevant capability, invoke it, and pass back useful results. The aim is to reduce up-front schema overhead; it does not mean tool calls or their results have no context cost.
Choose which tools are direct, deferred, or blocked
Pi also supports selective exposure, so developers can decide which tools deserve direct prompt access and which should remain discoverable through Codemode. The New Stack gives a GitHub server as an example:
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- Expose directly:
search_code, when it is useful to have immediately available. - Defer through Codemode:
get_*tools, which can be found and called when a task requires them. - Block:
delete_*tools, when they should not be available to the agent.
The report gives Codemode a default tool-declaration budget of 3,000 tokens. Tools beyond that budget remain discoverable, while MCP tools at default exposure do not count against it. This is a Pi-specific setting, not a general MCP limit or a guarantee of a particular context reduction.
How this compares with Pi’s CLI approach
Before Codemode, Zechner’s approach used Bash and a small set of scripts for browser work. The New Stack says the CLI-based tools needed a 225-token README, and that their output could be piped, filtered, or saved without first passing through the model. That is a reported comparison rather than an independently verified benchmark, but it illustrates the architectural difference: a CLI can let scripts process data outside the model, while Codemode keeps tools available through deferred discovery and calls.
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A separate Pi prompt-reduction example
The report also cites Pi 1.0 release notes describing a GPT-5.6 request with default tools and Codemode. In that Pi-specific comparison, the prompt fell from roughly 5,300 tokens to 3,300 after Pi shortened the Codemode description, moved model API documentation out of the prompt, and stopped repeating declarations for tools that scripts could already access. This is a separate request comparison; it does not measure the Chrome DevTools MCP definition cost or promise similar savings in other platforms.
What Codemode does not solve
Deferring definitions addresses how much tool description enters the prompt up front, but it does not settle every concern raised about MCP. The New Stack report specifically notes composability: returned results still have to pass through the agent’s context if the agent is to persist them to disk or combine them with other results. It also describes Pi’s implementation as running scripts inside QuickJS without Node APIs, filesystem access, network access, or timers. Those are details of Pi’s environment, not universal properties of MCP or Codemode.
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How to evaluate the approach
When deciding whether deferred discovery suits an agent workflow, focus on where schemas and results travel, not just the headline token number:
- Up-front context: How many tool schemas enter the prompt before a task starts?
- Discovery: Can the agent find a relevant tool only when needed, or must it load the full catalog first?
- Result handling: Can scripts filter, combine, or save output without routing it through the model?
- Access control: Can individual tools be made directly available, deferred, or blocked?
- Runtime constraints: What APIs and resources can the tool-calling environment actually use?
The available figures are reported examples rather than a controlled head-to-head evaluation. The practical lesson is that MCP context overhead depends on the server and the agent’s exposure strategy. Pi’s Codemode offers one way to delay most declarations while retaining selective direct access, but it does not eliminate result-context costs or resolve every composability trade-off.
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