SassyMCP

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sassy_tool_usage

Read-onlyIdempotent

View tool usage analytics to see which tools are actively used, identify inactive groups safe to disable, and understand usage patterns for context savings.

Instructions

Read-only. Returns tool usage analytics tracked by the server and persisted across sessions in ~/.sassymcp/tool_usage.json (last 90 days, capped at 500 invocations per tool): unique_tools_ever, total_invocations, invocations_today, invocations_this_week, and a top_10 list of tool names with recency-weighted scores (0.0-1.0 via exponential decay, so recent calls count more). Takes no parameters. Use it to see which tools are actually exercised, to inform which groups are safe to disable, or to understand usage patterns; pair with sassy_tool_groups when deciding what to prune for context savings.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedv1.15.1Sep 18, 2026

addedOutput schema / additionalProperties
```
Added value: +true
```

removedOutput schema / properties

Removed value: -{
-  "result": {
-    "title": "Result",
-    "type": "string"
-  }
-}

removedOutput schema / required
```
Removed value: -[
-  "result"
-]
```

changedOutput schema / title

Previous value: -"sassy_tool_usageOutput"New value: +"sassy_tool_usageDictOutput"

First observedv0.1.0Jul 31, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the annotations (readOnly, idempotent, non-destructive), the description reveals persistence across sessions, the file location (~/.sassymcp/tool_usage.json), retention window (last 90 days), per-tool cap (500 invocations), and the exponential decay weighting of the top_10 scores. These are meaningful behavioral details an agent would not infer from annotations alone.

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 front-loaded with 'Read-only' and a clear statement of what it returns, then packs retention details and usage guidance into three dense sentences. Every clause carries information—file path, cap, decay formula, and decision use cases—with no filler.

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?

For a parameterless tool with an output schema, the description is fully self-contained: it explains persistence, data scope, the decay scoring model, and how to use the results for pruning. It even cross-references sassy_tool_groups, covering the decision workflow.

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 takes zero parameters and the schema coverage is 100% (vacuously). No parameter documentation is needed, which matches the baseline of 4 for parameterless tools; the description adds no parameter-specific meaning because none exists.

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 names a specific verb and resource: 'Returns tool usage analytics tracked by the server' and enumerates the exact statistics returned. It distinguishes itself from lookalike siblings by focusing on persisted usage metrics (invocations, recency-weighted top 10) rather than observability health or audit logs.

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?

The description gives explicit use cases: 'to see which tools are actually exercised, to inform which groups are safe to disable, or to understand usage patterns' and recommends pairing with sassy_tool_groups for pruning decisions. It does not mention when not to use it or explicit alternatives, but the context is clear.

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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