Token Optimizer MCP server

slimctx — the token optimizer for AI agents

<!-- mcp-name: io.github.omkar9854/token_optimizer -->

Zero-dependency, fully-reversible context compression for AI agents.

slimctx compresses what your agent reads — tool outputs, logs, JSON, source files, prose — before it reaches the LLM. Same answers, fraction of the tokens. Pure Python stdlib: no ML models, no downloads, no network calls, ever. Auditable end to end in ~1,600 lines.

Live output of python3 benchmarks/demo.py — run it yourself, nothing is staged.

from slimctx import Pipeline, Config

pipe = Pipeline(Config(target_tokens=32_000))
result = pipe.compress(messages)        # OpenAI/Anthropic-style dicts
print(result.savings_ratio)             # e.g. 0.82

original = pipe.retrieve("a1b2c3d4...")  # byte-exact original, any time

Results (synthetic workloads modeled on real agent traffic)

Every run also verifies that each planted "needle" (the FIXME, the OOMKill, the outlier) survives compression, and that every lossy transform is byte-exact reversible. Reproduce with python3 benchmarks/bench.py.

How it works

messages ──► ContentRouter ──► one of:
                ├─ JSON  : lossless tabularization (repeated keys → header,
                │          constant columns → legend), then relevance-ranked
                │          row selection only if still over budget
                ├─ LOG   : Drain-style template mining — repeated lines
                │          collapse to `pattern [x1432]`; errors verbatim
                ├─ CODE  : AST skeleton — signatures + docstrings kept,
                │          bodies elided EXCEPT those relevant to the query
                └─ TEXT  : extractive sentence selection (BM25 + salience
                           + position), verbatim, never paraphrased

The four guarantees

Shortened. The full README is on GitHub.