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Estimate Claude token usage, model costs, and caching break-even offline.
This MCP tool is described as a local, offline estimator for Claude token usage and cost, requiring no API key or network access, and it is open source for review; no clear high-risk red flags are evident from the provided materials. Since it is flagged as executing code and lacks a README with very low community adoption, it is best treated with low-to-moderate caution.
The materials explicitly state that no keys or environment variables are required. No API tokens, account credentials, or other sensitive authentication data are requested, so credential exposure risk appears low.
The description says 'without an API key or network,' and no remote host is listed. Based on the available materials, there is no indication of user data being sent to external services or connections to unknown hosts.
The system flags it as executes-code, indicating the MCP tool has the normal ability to run code/processes locally. The materials do not show unusual system privileges beyond what its claimed local cost-estimation function would need, but it should still be treated with standard caution for local execution tools.
The description only indicates local token/cost estimation and does not declare any need to access specific files, databases, or cloud resources. However, as an executable MCP tool, it may theoretically interact with local input data. No clear signs of overbroad access are present, but the missing README leaves its read/write boundaries insufficiently documented.
Positive factors include being open source under the MIT License, which allows source review. However, it comes from a third-party registry, has 0 stars, unknown maintenance status, and no README, which reduces trust and verifiability; code and dependencies should be reviewed before installation.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "claude-cost-mcp" yet — see the docs or source repo.
Estimate the token count and projected cost of this Claude prompt across different models, and compare them in a table: <paste prompt content>
Returns estimated token counts, a cost comparison table by model, and notes on cost differences.
Based on this system prompt and expected reuse count, calculate the Claude prompt caching break-even point and explain at what usage frequency it becomes cost-effective: <paste system prompt>
Provides the caching break-even usage count, cost trend, and a recommendation on whether to enable caching.
I plan to call Claude 500 times per day with the following average input and output sizes. Estimate daily and monthly costs, and suggest more cost-efficient models: <fill in usage details>
Outputs daily and monthly cost estimates, key assumptions, and model recommendations to reduce costs.
Count tokens and estimate costs across many LLMs for planning and budgeting.
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