learn_from_failures

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

With no annotations, the description bears the full burden. It implies a state-changing capability ('apply corrections') but never states whether mining is read-only, what corrections actually modify, whether they are reversible, or what permissions are required. This is a significant gap for a tool that can mutate data.

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

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

Two tight sentences with the core action front-loaded and no filler. The conciseness is undermined only by opaque jargon rather than by verbosity.

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

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

An output schema exists, so return values need not be described. But for a 3-parameter tool with a mutating mode and zero schema documentation, the definition omits the parameter meanings and the safety/reversibility profile an agent needs to call it correctly.

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

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

Schema description coverage is 0%, so the description must compensate. Only auto_apply is loosely implied by 'Optionally apply corrections'; 'deep' and 'min_occurrences' are entirely unexplained, leaving an agent unable to set them meaningfully.

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

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

States a specific verb ('mine') and resource ('failure patterns') with named data sources (PRISM, vault, evolution). However, 'PRISM' and 'evolution data' are internal jargon that reduce immediate comprehension, and no sibling tool is named for contrast.

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

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

The phrase 'Optionally apply corrections' hints at two modes but never says when to prefer this over siblings like refresh_beliefs, verify_and_repair, or vault_hygiene_scan. No trigger conditions, prerequisites, or exclusions are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.