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sassy_context_estimate

Calculate token usage from MCP tool definitions to see total estimated tokens, percentage of the 200K window, and heaviest tools, helping identify why context is depleting.

Instructions

Estimate current context window usage from MCP tool definitions.

Shows: total estimated tokens, % of 200K window, heaviest tools. Use this to understand why your context is running low.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description carries the full burden. It describes the tool as estimating usage and listing outputs, which implies a read-only, non-destructive operation, but it does not explicitly state that it has no side effects or mention any permissions or reversibility. This is adequate but not rich in behavioral disclosure.

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?

The description is concise and front-loaded. The first sentence states the core action, and the following lines list outputs and use case without any fluff or redundancy. Every sentence earns its place.

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?

The tool is simple with no parameters and has an output schema, so the description doesn't need to detail return values. Given its simplicity, the description sufficiently covers the tool's purpose and outputs, making it complete enough for an agent to select and invoke correctly.

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?

The tool has zero parameters, so the baseline is 4. The description adds context about what the tool returns (e.g., total tokens, % of window, heaviest tools), which is meaningful even though there are no parameters to explain.

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?

The description clearly states the tool's purpose with a specific verb ('Estimate') and resource ('current context window usage'). It details what it shows (token estimates, percentage of 200K window, heaviest tools), distinguishing it from sibling tools like sassy_observability_tool_stats.

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 a clear use case: 'Use this to understand why your context is running low.' This gives context for when to employ the tool, though it does not explicitly mention when not to use it or name alternative tools.

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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