femm_check_rule

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

With no annotations, the description carries the burden, and it does well: it discloses that this performs no FEMM call (no session required, side-effect free) and gives the exact return shape {name, ok, value, expected, tolerance, message}. It omits error/failure behavior, but the compute-only nature is made explicit.

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?

Front-loads the core purpose, then a discovery hint, then three compact illustrative examples and the return shape. Dense but every element earns its place; the examples add a little bulk over a minimal statement.

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?

For a 2-param offline compute tool with no output schema, the description supplies the return contract, the rule discovery mechanism, and concrete argument examples, leaving little 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 coverage is 0% and the generic 'args' object is opaque, but the description compensates by naming the rule strings and showing the required args per rule (energy/flux_linkages/currents, p_input/p_losses, f_a/f_b/tolerance). This meaningfully fills the 0% coverage gap.

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+resource ('Run one pure physics rule on numbers you already have') and immediately scopes it with 'no FEMM call', which cleanly distinguishes it from session-driven siblings like femm_solve or femm_point_values.

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?

Tells the agent when to use it (numbers you already have, offline check) and how to discover the rules via rule='list'. It doesn't name an alternative tool or state when NOT to use it, but the offline-vs-session framing is a clear usage cue.

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