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Tokenomy is a transparent token-reduction toolkit for Claude Code.
It helps developers reduce unnecessary token usage when working with tool-heavy AI workflows.
Tokenomy works by intercepting tool calls using Claude Code hooks and trimming oversized tool responses before they are sent back to the model.
The result is lower token consumption, cleaner context windows, and faster reasoning cycles without breaking tool behavior.
When building agents or tool-driven systems, tools often return significantly more data than the model actually needs.
Common issues include:
- MCP tools returning very large JSON payloads
- File read tools loading entire files unnecessarily
- Debug logs and metadata bloating responses
- Context windows filling up with irrelevant tool output
In many real workflows, 80–90% of tool output is unused, yet it still consumes tokens.
Tokenomy introduces a structured way to control and reduce these responses automatically.
Tokenomy integrates with Claude Code using two hooks:
Runs before a tool executes.
This hook prevents oversized tool inputs by enforcing limits such as:
- restricting large file reads
- limiting chunk sizes
- preventing accidental multi-MB inputs
- enforcing safe tool parameters
This ensures tools cannot accidentally inject huge amounts of data into the context.
Runs after a tool returns a response.
This hook trims the tool output before it is sent back to the model.
Typical reductions include:
- removing redundant metadata
- trimming large arrays
- filtering verbose logs
- extracting only relevant fields
The model receives the information it needs without unnecessary token overhead.
Tokenomy keeps a transparent log of token reductions so developers can measure the impact.
Example:
Tool: MCP.query
Original tokens: 12,480
Reduced tokens: 2,130
Saved: 10,350 tokens (83%)
This makes optimization observable and measurable rather than hidden.
Tokenomy works especially well for workflows involving:
- MCP tools with large responses
- repository exploration tools
- document retrieval pipelines
- file reading utilities
- log analysis tools
- long-running AI agent sessions
Any system that frequently uses tools and produces large outputs can benefit from token reduction.
Tokenomy follows a few core principles:
Transparency
Developers should always know what data is being trimmed.
Safety
Optimizations should never break tool semantics.
Minimal Intrusion
Existing tools should work without modification.
Efficiency
Token reduction should improve both cost and reasoning performance.
Potential areas of development include:
- semantic trimming strategies
- token-aware chunking
- automatic summarization fallback
- tool-specific optimization policies
- token usage dashboards
If you are experimenting with Claude Code agents, MCP tools, or tool-driven development workflows, feedback is very welcome.
You can contribute by:
- reporting edge cases
- suggesting trimming strategies
- proposing new optimization rules
- sharing token-heavy workflows
Tokenomy aims to make token-efficient AI tooling the default approach for developer workflows.