Seed-system-dynamics

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

No annotations exist, so the description carries the full behavioral burden. 'Transparent rankings' is marketing phrasing that discloses no mechanism: not whether this is read-only, deterministic, whether it runs full simulations internally, execution cost, or what the ranking is based on. Minimal disclosure for an unannotated tool.

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?

A single front-loaded sentence with no filler. It is efficient, though the brevity comes at the cost of substance rather than being a case of well-targeted concision.

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?

With seven undocumented parameters, no annotations, and no output schema, the description is far too thin. It does not convey what a 'ranking' contains, how policies are scored, or what defaults mean — an agent cannot reliably construct a call from this.

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 all seven parameters have empty descriptions, so the description must compensate. It only indirectly implies that the _a/_b/_c suffixed parameters correspond to the three policies being compared; pipeline_delay, units, valid ranges, and the meaning of supply_line_weight remain completely unexplained.

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 (compares) and resource (managerial ordering policies) with a clear scope cap of three. It implicitly distinguishes itself from simulate/optimize/diagnose siblings by being the side-by-side comparison tool, though it never names or contrasts an alternative.

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?

There is no when-to-use guidance, no precondition, and no reference to any of the six sibling tools (simulate, optimize, sweep, stability, diagnose, model_info) that a user might pick instead. The agent must infer that this is the multi-policy comparison entry point.

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

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

With no annotations, the description carries the full behavioral burden, and it discloses almost nothing: not which mode is active, what input shape selects diagnosis vs. ingestion, whether the tool mutates state, or what the output looks like. The dual-mode phrasing raises more questions than it answers.

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?

It is a single, short sentence, which is appropriately sized, but the second clause is garbled ('ingests simulate results directly'), so the brevity comes at the cost of clarity rather than being disciplined conciseness.

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 4-parameter tool with 0% schema coverage, no annotations, and no output schema, the description is substantially inadequate. An agent has no way to know how to invoke either of the two claimed modes or what the parameters should contain.

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?

All four parameters have empty schema descriptions (0% coverage), so the description must compensate, and it does not. Nothing in the text explains symptom_key's enum values, what observed_overshoot_pct/undershoot_pct measure, or how is_settled affects the audit.

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 verb 'Audits' plus 'operational pathologies' gestures at a purpose, but the opaque 'S-E-E-D Diagnostic Matrix' jargon and the elliptical second clause 'or ingests simulate results directly' leave the actual operation ambiguous. It never clearly states whether this diagnoses, validates, or imports data, and the only sibling signal is an indirect reference to simulate results.

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?

There is no explicit when-to-use guidance and no named alternative among the six sibling tools. The phrase 'or ingests simulate results directly' hints at a relationship with seed_system_dynamics_simulate, but it is phrased as a capability, not as a routing rule telling the agent which tool to pick.

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

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

With no annotations, the description carries the full burden. "Returns" implies a read, but there is no confirmation of read-only semantics, no side-effect disclosure, and no output schema to fall back on. It only hints at the shape of the payload (equations, assumptions, version boundaries) without stating privilege needs, cost, or whether a model must already exist.

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?

A single eight-word sentence that is front-loaded with the verb and lists the payload. It is efficient with no filler, though the terseness shades into under-specification rather than crisp completeness.

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 zero-parameter tool this is nearly viable, and the description does enumerate the three content groups returned, partially covering the absent output schema. But with no annotations and no output schema, it should also convey read-only safety and when in a workflow to call it, which it does not.

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?

The tool takes zero parameters, which is the baseline-4 case. The description correctly implies a no-argument global metadata retrieval, and there is no parameter semantics to mis-specify.

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 verb+resource is identifiable ("Returns governing mathematical equations, assumptions, and version boundaries"), so an agent knows roughly what comes back. However, it reads as a list of payload contents rather than a statement of purpose, and it never distinguishes this metadata getter from the six action-oriented siblings (compare, diagnose, optimize, simulate, stability, sweep).

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?

There is no when-to-use guidance and no mention of any sibling as an alternative. The agent must infer from the name alone that this is the inspection step preceding or explaining a simulation. Nothing is misleading, but nothing is offered either.

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

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

No annotations are provided, so the description carries the full behavioral burden. It does not state whether the tool is read-only or mutating, whether it requires a configured model to exist first, what 'empirically matched' means operationally, or what the caller receives back. The only behavioral hint is that a computation occurs.

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?

A single sentence is efficient, but the 'or' clause splits the purpose rather than front-loading one clear action, and jargon like 'empirically matched' is used without definition. It is short but not maximally informative per word.

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?

With no annotations, no output schema, three wholly undocumented parameters, and only one sentence of prose, the definition is insufficient for a tool that appears to run a nontrivial control-theory computation. An agent lacks the information needed to invoke it confidently.

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% and all three parameters have empty descriptions. The prose mentions 'adjustment time (AT)' and 'damping ratio,' loosely mapping to current_adjustment_time and target_damping_ratio, but pipeline_delay is never mentioned and no parameter is tied to a name, units, or valid range.

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 names a verb (calculates) and a resource (optimal adjustment time), and 'optimize' in the tool name aligns. But the trailing 'or evaluates supply-line control' introduces a second, unexplained capability, so an agent cannot tell what single job this tool performs versus siblings like seed_system_dynamics_simulate or seed_system_dynamics_sweep.

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?

There is no when-to-use guidance, no prerequisites, and no reference to any of the six sibling tools. The agent must infer from the name alone whether this is the right tool for tuning versus simulating or sweeping.

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

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

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It describes the simulation's domain characteristics but says nothing about whether the tool is read-only, what it returns, whether it has side effects, or any execution constraints. The agent is left to guess at operational behavior.

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 description is a single, front-loaded sentence with no wasted words. However, for a tool with 11 parameters, this brevity comes at the cost of essential detail, making it under-specified rather than effectively concise.

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?

Given the tool's complexity (11 parameters, no output schema, no annotations), the description is far from complete. It omits any explanation of parameters, usage context, or return values, leaving the agent with insufficient information to invoke the tool 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?

There are 11 parameters with 0% schema description coverage, and the description adds no parameter-level information whatsoever. It does not explain any of the inputs (e.g., dt, step_time, pipeline_delay, include_trajectory), leaving them entirely undocumented.

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 specific verb ('Simulates') and resource ('dynamic inventory behavior') and names key simulation features (pipeline lags, limit-cycle capture, optional noise). However, it does not differentiate this tool from its siblings (compare, diagnose, optimize, etc.), leaving the agent to infer that 'simulate' is distinct from those analyses.

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?

There is no guidance on when to use this tool versus the alternatives, no mention of prerequisites, and no exclusions. The description only states what the simulation does, not when an agent should choose it over the sibling tools.

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

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

No annotations are provided, so the description carries the full behavioral burden. It implies a read-only computation but never states that it is side-effect free, deterministic, or what the output looks like; it also gives no hint about required inputs or failure modes.

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?

A single front-loaded sentence with zero filler, which is good, but it is undersized for a three-parameter domain tool with no annotations or schema documentation to lean on.

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?

With no annotations, no output schema, 0% parameter documentation, and six ambiguous siblings, the description leaves the agent without the information needed to invoke this tool correctly or confidently.

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?

All three parameters (pipeline_delay, adjustment_time, supply_line_weight) have 0% schema description coverage, and the description never explains what any of them mean or accept. The phrase 'dead-time' is only a loose implicit nod to pipeline_delay, leaving adjustment_time and supply_line_weight entirely unexplained.

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 names a specific verb ('Calculates') and concrete domain outputs ('dead-time linear stability limits, floor margins, and tuned targets'), which is far more informative than a tautology. It does not, however, distinguish this from the six sibling tools (compare, diagnose, optimize, simulate, sweep, model_info), so an agent cannot tell why it should pick stability over, say, diagnose or optimize.

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?

There is no when-to-use guidance, no prerequisites, and no mention of any alternative tool. With seven closely named siblings in the namespace, the absence of any routing guidance is a meaningful gap.

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

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

No annotations are provided, so the description carries the full behavioral burden, and it discloses almost nothing: no read-only vs mutating nature, no cost/time expectations for a grid sweep, no indication of how results are returned. It hints at the evaluated metrics (stability, overshoot, settling time), which is the one useful behavioral detail.

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?

A single front-loaded sentence with no filler; the action and outputs come first. It is efficient, though the brevity is achieved partly by omitting needed detail.

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 four-parameter tool with no annotations, no output schema, and 0% schema coverage, the description is far too thin. It does not explain the mismatched parameter names, the sweep ranges, or the return shape, leaving the agent unable to invoke it confidently.

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 all four parameters (base_demand, target_stock, initial_stock, pipeline_delay) are undocumented in both schema and description. Worse, the description names 'AT' and 'w_sl' as the swept parameters, which do not appear in the input schema at all, actively misleading the agent about what to pass.

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 concrete action (sweep an AT x w_sl parameter grid) and the analysis goals (stability, overshoot, settling time), so the purpose is clear. However, it never differentiates itself from close siblings such as seed_system_dynamics_simulate, stability, or compare, and the only implicit contrast is the phrase 'in a single call'.

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?

There is no explicit guidance on when to choose a grid sweep over simulate, optimize, or compare, nor any prerequisites or exclusions. 'In a single call' weakly implies batch usage instead of iterative simulation, but the agent must infer that entirely.

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