# Token Savings: jCodeMunch MCP
## Why This Exists
AI agents waste tokens when they must read entire files to locate a single function, class, or constant.
jCodeMunch indexes a repository once and allows agents to retrieve **exact symbols on demand**, eliminating unnecessary context loading.
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## Example Scenario
**Repository:** Medium Python codebase (300+ files)
**Task:** Locate and read the `authenticate()` implementation
| Approach | Tokens Consumed | Process |
| ---------------- | --------------- | ------------------------------------- |
| Raw file loading | ~7,500 tokens | Open multiple files and scan manually |
| jCodeMunch MCP | ~1,449 tokens | `search_symbols` → `get_symbol` |
**Savings:** ~80.7%
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## Typical Savings by Task
| Task | Raw Approach | With jCodeMunch | Savings |
| ------------------------ | --------------- | --------------- | ------- |
| Explore repo structure | ~200,000 tokens | ~2k tokens | ~99% |
| Find a specific function | ~40,000 tokens | ~200 tokens | ~99.5% |
| Read one implementation | ~40,000 tokens | ~500 tokens | ~98.7% |
| Understand module API | ~15,000 tokens | ~800 tokens | ~94.7% |
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## Scaling Impact
| Queries | Raw Tokens | Indexed Tokens | Savings |
| ------- | ---------- | -------------- | ------- |
| 10 | 400,000 | ~5k | 98.7% |
| 100 | 4,000,000 | ~50k | 98.7% |
| 1,000 | 40,000,000 | ~500k | 98.7% |
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## Key Insight
jCodeMunch shifts the workflow from:
**“Read everything to find something”**
to
**“Find something, then read only that.”**
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