furl-ctx
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Alternatives to furl-ctx
- AlicenseNot gradedqualityCmaintenanceMCP server that provides real-time rate-limit and context budget awareness to Claude Code, enabling it to plan tasks that fit within its constraints and defer work when needed.9 npmApache 2.0
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- AlicenseAqualityBmaintenanceLets AI coding agents read an outline of a large file or a digest of noisy command output instead of the full text, while every hidden line stays retrievable on disk through a run handle. Agents can request only the line ranges, slices, or grep matches they need and list recent runs, cutting token spend without losing information.541 PyPIMIT
- AlicenseAqualityAmaintenanceReversibly compress a large tool output to a recoverable 8-hex handle over stdio, expand it back to the exact original bytes on demand, and report a local token/dollar savings ledger. Zero runtime dependencies; content never leaves the machine.3278 PyPI21Apache 2.0
- AlicenseNot gradedqualityBmaintenanceEnables 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 gradedqualityBmaintenanceEnables a coding agent to compress noisy tool output such as compiler errors, failing test runs and repeated logs before it enters the context window, and to fetch any collapsed ref back as its exact original text. Two tools, squeeze_fetch and squeeze_compress, keep the reduction reversible by the model itself.2 npm1MIT
- AlicenseAqualityBmaintenanceEnables coding agents to delegate the reading of large files, folders, and long logs to local models, returning short answers whose every claim is backed by a quote verified word for word in the source with its real file:line. Everything runs on the user's own hardware, so no file content ever leaves the machine.3MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI coding agents to retrieve the exact original tool-output slices omitted during lossless context compression, via on-demand expansion of hashed references stored in a shared cross-process SQLite vault. Tracks expansion overhead so agents and users can measure true net token savings across debugging sessions.MIT
TDQS
Scored across 6 tools
Each tool has a distinct primary role—compress, retrieve by hash, stats, purge, search, and list—making the set largely easy to navigate. The main ambiguity is that furl_retrieve also supports a query-based search mode, which overlaps with furl_search, though the descriptions are detailed enough to mitigate confusion.
All tools follow the same furl_ prefix followed by a clear action verb (compress, retrieve, stats, purge, search, list). This creates a predictable and consistent naming pattern across the entire server.
Six tools is well-scoped for a context compression and retrieval server. Each tool covers a necessary function—compressing, retrieving, searching, listing, purging, and statistics—without unnecessary bloat or missing essentials.
The toolkit provides a full lifecycle for compressed content: create (compress), read (retrieve/list/search), delete (purge), and observability (stats). This is a complete and coherent surface for the stated purpose of managing offloaded context.