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entroly_dashboard

See live metrics on token savings, selection speed, and context compression to verify the value Entroly delivers.

Instructions

Show the real, live value Entroly is providing to YOUR session right now.

Pulls from actual engine state — not synthetic data. Shows: Money saved: exact $ amounts from token optimization Performance: sub-millisecond selection speed vs API latency Bloat prevention: context compression ratio and memory footprint Selection quality: per-fragment scoring and context sufficiency Safety: duplicates caught, stale fragments filtered

Call this anytime to see exactly what Entroly is doing for you.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It explicitly states it pulls from actual engine state (not synthetic) and lists the types of data shown. It does not disclose potential side effects or performance impact, but given it's a read-only dashboard, this is acceptable. The transparency is good but could be more detailed about real-time behavior.

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

Conciseness4/5

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

The description is structured with a clear opening sentence and a bullet list summarizing key metrics. It is front-loaded with the purpose. However, the enthusiastic tone and exclamation marks are slightly verbose for a technical tool description, but the information density is good.

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

Completeness5/5

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

Given zero parameters and an existing output schema (not shown), the description provides a complete overview of what the tool does and the categories of data returned. An AI agent can confidently decide when to call this tool without additional context.

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?

There are no parameters, so the baseline score is 4. The description adds no parameter information because none is needed. The schema coverage is 100% (empty properties), so the description is not required to compensate.

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

Purpose4/5

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

The description clearly states the tool shows live value metrics from engine state, with a list of specific categories. However, the verb 'Show' is somewhat generic, and it could more explicitly state it retrieves a dashboard. It distinguishes from siblings like `get_stats` by focusing on session-specific value metrics, but doesn't explicitly contrast.

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

Usage Guidelines3/5

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

The description says 'Call this anytime,' implying it can be used freely, but it does not provide when-not-to-use guidance or compare to alternative tools like `get_stats` or `explain_context`. The usage context is implied but not explicitly differentiated.

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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