plasticity_calibrate_turon_mixed_mode_law

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

Annotations only declare it a safe read (readOnlyHint=true, no writes/open-world), but the description adds substantial behavioral context beyond that: rejection rules, the exact-setup/interface-normal consistency checks, the returned quantities (peak tractions and per-MMB residuals), and an explicit list of non-capabilities (does not identify K, qualify the law, authorize FEA, establish allowables, or approve a part). This is rich disclosure that goes well past the annotations and is internally consistent with them (a computation that returns results for review, not a persisted write).

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?

The purpose is front-loaded in the opening clause before any constraints, and each subsequent sentence (format rule, rejection conditions, return values, non-capabilities) earns its place. It is dense and jargon-heavy, but not padded.

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?

There is no output schema, so the description appropriately explains what is returned (pure-mode peak tractions and per-MMB energy residuals). Combined with the input preconditions and the explicit non-capabilities, an agent has everything needed to decide to call it and interpret its scope.

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 carry meaning, and it does: modeIRecordId maps to the DCB record, modeIIRecordId to the ENF record, and mixedModeRecordIds to 'at least two distinct-ratio MMB' records, matching the minItems=2 constraint. It does not restate the 64-hex-hash format (which the schema pattern already enforces), but the semantic role of each parameter is well conveyed.

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?

The description states a precise verb and artifact: 'Fit a candidate Code_Aster CZM_TURON ETA_BK' from specific measured test records. This is unmistakably distinct from sibling tools like plasticity_calculate_mmb_mode_i_ii_energy (computes energy) or plasticity_analyze_cohesive_interface, and the scope constraints (same-material printed-layer interface, single process) further sharpen its identity.

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?

It gives strong when-to-use/when-not conditions: requires immutable measured DCB, ENF and at least two distinct-ratio MMB records, and rejects different-material bond tests, missing curves, non-interface failure, conflicting setups and mismatched processes/normals. The one gap is that it never names an alternative tool to reach for when these preconditions fail.

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