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compress_context_tool

Compress text context by removing irrelevant information relative to a user question, significantly reducing context size while retaining key content for LLM processing.

Instructions

Compress text context using LLMLingua with a local model.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextYes
questionYes
target_tokensNo
rateNo
Behavior2/5

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

With no annotations, description must disclose behavioral traits. It only mentions 'compress' and the method, but omits side effects, idempotency, or output expectations. Minimal transparency.

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

Conciseness2/5

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

Single sentence is concise but not effective; it lacks essential details and fails to earn its place as a complete description.

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

Completeness1/5

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

Given 4 parameters, no output schema, and 0% schema coverage, the description fails to explain parameter purposes or use case, making it incomplete for agent invocation.

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

Parameters1/5

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

Schema coverage is 0% and description adds no meaning to parameters. 'question' and 'rate' remain unexplained beyond their names.

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

Purpose3/5

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

Description states verb 'compress' and resource 'text context' with method 'using LLMLingua with a local model'. It is clear but overly generic; does not distinguish from sibling optimization tools that also manage context.

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

Usage Guidelines1/5

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

No guidance on when to use this tool versus alternatives like optimize_context_tool. Missing usage context entirely.

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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