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The reported 4–32× token gap is real for five specific GitHub tasks, but it is not a fixed cost of MCP. In a 2026 benchmark reported by Firecrawl, the comparison used Claude Sonnet 4, the gh CLI, and the official GitHub Copilot MCP server, which exposed 43 tools. The main source of potential overhead is often how a client loads tool descriptions and schemas into model context—not an unavoidable per-call fee imposed by the protocol.
What the 4–32× figure measures
Firecrawl’s June 5, 2026 article reports results from 75 benchmark runs by Scalekit. The tests used identical prompts and GitHub tasks with Claude Sonnet 4, comparing the gh CLI with the official GitHub Copilot MCP server, described as exposing 43 tools. The reported task token counts were:
| GitHub task | gh CLI tokens |
MCP tokens | Reported ratio |
|---|---|---|---|
| Repository language and license | 1,365 | 44,026 | About 32× |
| Pull request details and review status | 1,648 | 32,279 | About 20× |
| Repository metadata and install | 9,386 | 82,835 | About 9× |
| Merged pull requests by contributor | 5,010 | 33,712 | About 7× |
| Latest release and dependencies | 8,750 | 37,402 | About 4× |
These are reported results for five tasks and a particular model, server, and comparison—not a protocol-wide ratio. Firecrawl also gives a derived estimate of about $3.20 for CLI versus $55.20 for MCP at 10,000 monthly operations, using the benchmark’s assumptions. That cost estimate is likewise specific to those assumptions, not a general price forecast. See Firecrawl’s report of the Scalekit benchmark.
The Tool Desk
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MCP gives clients a structured way to discover tools and their input schemas. Under the Model Context Protocol tools specification, version 2026-07-28, a server that declares the tools capability must respond to tools/list requests with the tools currently available to that client. A listing can include names, descriptions, and JSON input schemas. The specification supports pagination and caching, and notes that available tools can change over time or with authorization.
The protocol does not require a host to load every definition into an LLM prompt before each task, nor does it set a fixed token charge. Overhead depends on the size and wording of the catalog and on host behavior: whether it presents the full catalog up front, discovers tools as needed, or filters the results before they reach the model. A short shell command can have very little setup context; a broad catalog of detailed schemas can be much larger.
A separate configuration-specific measurement illustrates how much catalog size can matter. The author of an article about MCP Token Saver reported 71,929 tokens for raw JSON schemas across 255 tools from 50 MCP servers, compared with 123 tokens for a names-only compact manifest and 8,282 tokens for a slim manifest with names and parameter types. The author used tiktoken with cl100k_base; those figures describe that setup and method, not all MCP clients. Read the author’s MCP Token Saver article.
How to reduce tokens without losing useful tools
Discover definitions progressively
Use tool search or another discovery step to identify relevant tools first, then load their definitions. Anthropic describes presenting MCP servers as code APIs or adding a search tool so the agent can discover only the definitions needed for a task, rather than placing an entire catalog in context. Some clients may already do this, so verify the behavior of the specific host and version before building a duplicate layer.
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Keep intermediate data out of the prompt
Where the environment permits it, execute code against tool APIs and filter or transform results before returning them to the model. For example, code can retrieve several records, select the fields needed for the decision, and pass back a compact summary rather than the full intermediate responses. Anthropic reports that, in its Google Drive-to-Salesforce example, code-based discovery reduced token use from 150,000 to 2,000—a 98.7% reduction. That is a result for Anthropic’s particular example, not a guaranteed saving for other workflows. Read Anthropic’s explanation of code execution with MCP.
Trim and organize the catalog
Expose only tools the agent needs for the task or environment, and make descriptions and schemas concise while preserving the information necessary to call tools correctly. The MCP repository’s SEP-1576 proposal discusses schema deduplication, optional-field control, response granularity, and retrieval-based tool selection as potential ways to address token overhead. It is a proposal, not an adopted MCP requirement.
Measure what your actual host sends
Count the definitions and results that actually reach the model, not merely the number of tools on a server. For a useful comparison, keep the task and model constant and record:
- Host or agent harness and version, plus the MCP server and tool catalog.
- Token-counting method and whether counts include tool definitions, prompts, and returned results.
- Task outcome, latency, failures, and any relevant network or server conditions.
- Whether discovery is eager or on demand, and how much intermediate data is passed back to the model.
The benchmark’s task-to-task range and the configuration-specific catalog measurement show why another team’s token totals should not be treated as your expected result.
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Token count is important, but it is only one part of the decision. Compare the same workflow across the interfaces available to your agent, including:
- Context cost: Count tool definitions and returned data that actually enter model context; check whether discovery is deferred.
- Reliability and latency: Test task success and response time under comparable model, server, and network conditions. A failure may stem from the server or transport rather than the interface alone.
- Composition and data movement: Shell pipelines can filter between commands. MCP workflows may require multiple calls, while code execution can keep intermediate values outside model context.
- Identity and permissions: Compare shell credential scope with the MCP host and server’s per-tool or per-user authorization model.
- Agent environment: CLI requires shell and command-execution access. MCP can be a useful integration surface in clients where shell access is unavailable or named tool discovery is valuable.
In an October 2026 article, Arize describes a hybrid approach: CLI for scriptable shell work and MCP for integrations or clients where tool discovery or access without a shell is useful. It also says some clients load schemas on demand through tool search. These are product- and version-dependent behaviors, so check the current behavior of your chosen host rather than assuming it. Read Arize’s discussion of MCP and CLI.
Quick Recap
What to change first
- Inspect context loading. Determine whether the host sends every tool definition to the model or retrieves definitions on demand.
- Reduce what must enter context. Narrow the available catalog, use search or progressive discovery, and return only the fields and results needed for the task.
- Benchmark representative work. Compare CLI and MCP using the same model and task, and log tokens, success, latency, and failures.
- Keep the interface that fits. Use CLI where shell access and composition suit the job, MCP where its integration surface is useful, and combine them when that improves the workflow.
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