Ground your AI with deterministic mechanical engineering tools. Prevent hallucinations in dynamics, thermal fluids, and solid mechanics via strict validation.
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Average 4.1/5 across 3 of 3 tools scored.
Each tool addresses a distinct area: state-space analysis, vibration analysis, and PID tuning. No overlap in functionality, making it easy for an agent to select the correct tool.
All tool names follow a consistent verb_noun pattern with snake_case: analyze_state_space, analyze_vibration, tune_pid. The prefix 'analyze' for two tools and 'tune' for the third is appropriate for their actions.
Three tools is appropriate for a focused mechanical engineering calculator covering control and vibration analysis. Each tool provides significant functionality, and the count is neither too sparse nor overly heavy.
For the domain of dynamics and control, the tools cover state-space analysis, vibration analysis, and PID tuning, which are core topics. Minor gaps exist, such as frequency response analysis, but the set is largely complete for basic control system design and vibration assessment.
analyze_state_spaceAnalyze State Space SystemAAnalyze an LTI system in state-space form. Evaluates stability, controllability, and observability. Automatically catches ragged matrices and dimension mismatches.
| Name | Required | Description | Default |
|---|---|---|---|
| A | Yes | System matrix A (n x n) | |
| B | Yes | Input matrix B (n x m) | |
| C | Yes | Output matrix C (p x n) | |
| D | Yes | Feedforward matrix D (p x m) |
| Name | Required | Description |
|---|---|---|
| observability | Yes | |
| controllability | Yes | |
| system_dimensions | Yes | |
| stability_analysis | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly, idempotent, and not destructive. The description adds that the tool automatically catches ragged matrices and dimension mismatches, which is useful behavioral information beyond the annotations.
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 two sentences with no unnecessary words. It front-loads the core purpose and adds a key behavioral 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?
Given the complexity of the tool (matrix parameters, analytical output) and the presence of schema descriptions and an output schema, the description covers the main analyses and error handling. It could mention return value structure but is otherwise sufficient.
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 input schema has 100% description coverage, with each parameter (A, B, C, D) having a description. The description adds no additional parameter-level semantics, so a baseline score of 3 is appropriate.
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 clearly states the tool analyzes LTI systems in state-space form, evaluating specific properties (stability, controllability, observability). This distinguishes it from siblings like analyze_vibration or tune_pid.
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 description does not provide guidance on when to use this tool versus alternatives. It lacks explicit usage context or exclusions, leaving the agent to infer based on tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
analyze_vibrationAnalyze VibrationAAnalyse a mass-spring-damper system. Returns natural frequency (rad/s and Hz), damping ratio, damped natural frequency, system classification, logarithmic decrement, and engineering flags. All inputs must be in strict SI units.
| Name | Required | Description | Default |
|---|---|---|---|
| mass | Yes | Mass of the system in kilograms | |
| stiffness | Yes | Spring stiffness in Newtons per metre | |
| damping_coefficient | Yes | Damping coefficient in Newton-seconds per metre |
| Name | Required | Description |
|---|---|---|
| f_d | Yes | Damped natural frequency in Hz (underdamped only) |
| f_n | Yes | Natural frequency in Hertz |
| zeta | Yes | Damping ratio (dimensionless) |
| omega_d | Yes | Damped natural frequency in rad/s (underdamped only) |
| omega_n | Yes | Natural frequency in radians per second |
| c_critical | Yes | Critical damping coefficient in N·s/m |
| log_decrement | Yes | Logarithmic decrement (underdamped only) |
| classification | Yes | System damping classification |
| engineering_flags | Yes | Advisory messages about the system characteristics |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds that it returns outputs and requires SI units, providing useful context beyond annotations.
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 sentences, front-loaded with purpose and outputs, no wasted words. Highly concise and structured.
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?
Covers inputs, outputs, and constraints. Output schema exists (not shown here) which reduces burden. Could detail 'engineering flags' but overall complete.
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 100% with good descriptions. The description adds the overarching constraint of SI units but does not elaborate per-parameter beyond schema. Baseline 3 is appropriate.
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 clearly states the tool analyzes a mass-spring-damper system and lists specific outputs. It is distinct from siblings (analyze_state_space, tune_pid).
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?
Explicitly states all inputs must be in strict SI units, which is a clear usage constraint. Implicitly indicates use for mass-spring-damper systems; lacks explicit when-not-to-use guidance but is adequate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
tune_pidTune PIDAGenerate PID tuning parameters using Ziegler-Nichols and Cohen-Coon methods from a FOPDT process model. Returns Kp, Ki, Kd for the selected controller type and method, with reliability flags and method comparison. All inputs must be in strict SI units.
| Name | Required | Description | Default |
|---|---|---|---|
| method | No | Tuning method to apply | both |
| dead_time | Yes | Process dead time theta in seconds | |
| process_gain | Yes | Static process gain K from step test | |
| time_constant | Yes | First-order time constant tau in seconds | |
| controller_type | No | Type of controller to tune | PID |
| Name | Required | Description |
|---|---|---|
| warnings | Yes | Reliability advisory messages for the computed parameters |
| cohen_coon | Yes | Tuning parameters computed via the Cohen-Coon method |
| ziegler_nichols | Yes | Tuning parameters computed via the Ziegler-Nichols open-loop method |
| recommended_method | Yes | Which tuning method is preferred for this process model |
| controllability_class | Yes | Textual controllability assessment based on the dead-time ratio |
| controllability_ratio | Yes | Dead-time ratio r = θ/τ; values above 1.0 indicate a difficult process |
| method_agreement_ratio | Yes | Cohen-Coon / Ziegler-Nichols Kp ratio; values between 0.8–1.2 indicate good agreement |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds behavioral detail about return values (Kp, Ki, Kd, reliability flags, method comparison) and SI unit requirement, exceeding what annotations provide.
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 sentences: first clearly states purpose, second adds constraints. No redundant words, efficient and front-loaded.
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?
Explains return values and constraints (SI units). Given complexity of 5 parameters and available output schema, description is sufficiently complete for an AI agent.
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 100%, so baseline is 3. Description adds extra meaning: all inputs must be in strict SI units, and mentions controller type and method options beyond schema descriptions.
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?
Description states specific verb 'Generate' and resource 'PID tuning parameters' with clear distinction from sibling tools like analyze_state_space and analyze_vibration. It defines the methods (Ziegler-Nichols, Cohen-Coon) and output (Kp, Ki, Kd, reliability flags).
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?
Implies usage for FOPDT process model tuning and strict SI units, but does not explicitly state when not to use or contrast with alternatives. No exclusion criteria provided.
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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