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sassy_self_check

Reconciles expected modules with live tools, showing which failed to import so silent breakage is surfaced and the server's integrity is verified.

Instructions

Proprioceptive self-check: reconcile the declared module manifest against the live tool registry and surface any module that FAILED to import — the silent drop _load_modules()'s try/except otherwise hides.

Answers "am I whole?" with a real readout instead of phantom limbs. A tool can be legitimately absent for three reasons, all reported separately so none is mistaken for a regression:

  • dormant — in an on-demand group (always_load=False) not yet toggled on or usage-boosted. Absent BY DESIGN; appears after sassy_tool_group_toggle or a usage boost.

  • pruned — low usage score dropped it from the default load (see _tool_loader.get_pruned_tools).

  • unsupported — module import raises on THIS platform, but it was never in the default load, so the failure is non-fatal.

The one real regression is:

  • BROKEN — an expected-loaded module whose import raises, so its tools never register and the loss is silent.

Every BROKEN module is logged at ERROR (server log / audit substrate) so the amputation is loud, not silent. verdict='whole' == every expected module imports cleanly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It thoroughly explains that the tool reconciles the manifest, reports four categories, logs BROKEN modules at ERROR, and defines what 'verdict=whole' means. It also reveals the underlying silent-failure mechanism in _load_modules(), adding valuable context beyond what any schema could convey.

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 detailed and well-structured, using bullet points to delineate categories. It front-loads the core purpose and then expands on necessary nuance. While somewhat verbose, each sentence contributes to understanding the tool's behavior and output interpretation. A minor reduction in length could improve conciseness without losing substance.

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 the tool has no parameters and an output schema exists, the description is complete for an agent to correctly invoke and interpret the tool. It covers the diagnostic purpose, the categories of absent modules, the meaning of the verdict, and the logging behavior. No critical information is missing.

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 description correctly includes no parameter details. The schema coverage is trivially 100%, and the baseline for parameterless tools is 4. The description adds value by explaining the output categories, which indirectly helps the agent understand what parameters would be irrelevant here.

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: it reconciles the declared module manifest against the live tool registry to surface any module that failed to import. It uses a specific verb ('reconcile... and surface') and resource, and answers 'am I whole?' with a real readout. This uniquely distinguishes it from sibling tools, which focus on other observability or health metrics.

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 provides clear context on when to use the tool (to detect modules that failed to import) and explains how to interpret the output categories (dormant, pruned, unsupported, BROKEN). It does not explicitly name alternative tools or state when not to use it, but the context is strong enough to guide the agent.

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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