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furl_compress

Compress large text outputs to reduce context usage. Original content is preserved and retrievable by hash.

Instructions

Compress content to save context window space. Use this on large tool outputs, file contents, search results, or any content you want to shrink before reasoning over it. The original is stored and can be retrieved later via mcp__furl__furl_retrieve. Returns compressed text + a hash for retrieval. Optional 'mode' controls aggressiveness; optional include/exclude patterns limit which lines are compressed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoCompression aggressiveness (default 'normal' = current behavior). 'lossless_only': only proven-lossless transforms run — nothing is dropped or substituted, so the output carries no retrieval markers (larger, fully reversible). 'aggressive': keep fewer items per crush and accept marginal compressions the default would reject (smaller output; all drops stay CCR-recoverable).
contentYesThe content to compress. Can be any text: file contents, JSON, search results, logs, code, etc.
exclude_patternsNoGlob-or-regex patterns. Any content line matching one is PROTECTED — passed through verbatim, never compressed. Applied on top of include_patterns.
include_patternsNoGlob-or-regex patterns (regex tried first, glob fallback). When set, ONLY content lines matching at least one pattern are eligible for compression; all other lines pass through verbatim.
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It discloses that original content is stored and retrievable, returns compressed text with a hash, and explains how mode affects lossiness (lossless_only vs. normal/aggressive) and that dropped items are recoverable. Include/exclude patterns are explained. Could mention the exact behavior of compression transforms, but overall disclosure is solid.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise: three sentences covering purpose, usage, and behavior. Every sentence adds value. No repetition, no fluff. Front-loaded with the core action and use cases.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (4 params, no output schema, no annotations), the description covers all key aspects: purpose, usage, parameter details, and output (compressed text + hash). It mentions retrieval but could include an example output format or structure to be fully complete for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds value beyond schema by explaining mode behaviors in plain language (e.g., what 'lossless_only' does, that include/exclude patterns are applied in order). This helps the agent understand parameter effects beyond the schema's enum values and patterns.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'Compress content to save context window space' with specific verb and resource. It lists use cases (large tool outputs, file contents, etc.) and distinguishes from sibling tools by mentioning retrieval via mcp__furl__furl_retrieve. This leaves no ambiguity about what the tool does or how it relates to alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance on when to use: 'on large tool outputs, file contents, search results, or any content you want to shrink before reasoning over it.' It also describes optional mode and include/exclude patterns. However, it does not explicitly state when not to use it or provide direct comparisons to sibling tools beyond mentioning retrieval.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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