OpenCode Context Optimizer (OCCE)
Related Servers
Alternatives to OpenCode Context Optimizer (OCCE)
No user-submitted related servers found.
Related Servers
- AlicenseAqualityBmaintenanceEnables AI coding agents to reduce token usage by condensing source code into AST skeletons, extracting specific symbols, compressing logs, and estimating token budgets.4MIT
- AlicenseBqualityCmaintenanceEnables coding agents to reduce context tokens by requesting file outlines, individual symbols, compact searches, and condensed test, lint, and type-check failures instead of reading whole files or raw command output.60MIT
- AlicenseNot gradedqualityAmaintenanceEnables AI coding agents to compress terminal output and file context in real time, preserving full error tracebacks while reducing token consumption by 60-85%.43MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI coding agents to conserve tokens by enforcing rules like delegating heavy work to subagents, checkpointing before token limits, and grepping before reading files.MIT
- AlicenseNot gradedqualityCmaintenanceEnables Claude Code to reduce token usage by 70-90% using a local LLM for codebase indexing, tool output compression, and turn summarization.33 npm3MIT
- AlicenseNot gradedqualityCmaintenanceEnables coding agents to shrink bulky tool output, logs, test runs, and file dumps down to compact briefs, and to record and recall decisions in a small session ledger. This keeps long-running tasks within context limits while preserving errors, findings, and key notes.8 npmMIT
TDQS
Scored across 6 tools
Each tool has a distinct role: AST outline vs. single-symbol extraction vs. command execution vs. test output vs. diff vs. token accounting. The only mild overlap is code_skeleton vs. symbol_slice, where both read source code, but the outline-vs-implementation distinction in the descriptions resolves it.
All names are snake_case two-word compounds in a modifier+noun style (code_skeleton, symbol_slice, test_compress, diff_compact, token_ledger), with smart_exec being the only slight deviation in verb-vs-noun form. Readable and predictable overall.
Six tools for a context-optimization server is well-scoped: two for reading code, three for compressing execution/test/diff output, one for telemetry. No filler tools.
The surface covers code reading, command execution, test output, diffs, and token accounting, which are the core levers of context reduction. A small gap is the absence of a symbol/search tool to locate symbols across files before calling symbol_slice.