quantum-artificer

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

No annotations exist, so the description must convey behavior. It mentions the cost, the use of specific libraries, and the limitation to bundled mechanisms. However, it does not state whether the tool has side effects, requires authentication, or is read-only, leaving some behavioral ambiguity.

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 compact and front-loaded, conveying domain, capabilities, limitations, and an example in a few lines. It could be slightly more structured (e.g., separate sections), but overall it is efficient and free of redundancy.

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?

The description covers input sufficiently and references another tool for spec shapes, but does not explain output format or return values. Given no output schema, this gap reduces completeness for a calculation tool.

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?

Although the schema descriptions are minimal (referencing another tool for spec shapes), the main description provides a concrete example with specific keys and values, adding significant meaning beyond the schema. This fully compensates for the schema's lack of detail.

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 identifies the tool as performing physical chemistry and thermochemistry calculations using Cantera and CoolProp. It lists specific operations like equilibrium and adiabatic flame temperature, and distinguishes from sibling tools by naming bundled mechanisms, making its purpose highly specific and unambiguous.

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 implies usage for thermochemical computations and provides an example, but it does not explicitly state when to use this tool over alternatives like 'numerical' or 'symbolic'. It mentions limitations (bundled mechanisms only) but lacks explicit when-not-to-use guidance.

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 provided; description mentions residuals, condition numbers, and cost ($0.01 USDC), giving partial behavioral insight but no details on side effects or safety.

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 plus cost note, front-loaded with category, no fluff.

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?

No output schema, description partially covers return values (residuals, condition numbers) but leaves gaps; spec explanation references another tool, reducing self-contained completeness.

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 parameter descriptions; description adds example usage and clarifies return info (residuals, condition numbers), enhancing the schema.

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 lists specific operations (root-finding, optimization, numerical integration, eigenvalues, linear solves) and provides an example, clearly distinguishing from siblings like 'ode' and 'quantum'.

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 mentions passing {operation, spec} with an example, but lacks explicit guidance on when to use versus alternatives like 'symbolic' or 'chemistry'.

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 provided, so the description carries full burden. It mentions energy/conservation checks and cost ($0.01 USDC), adding behavioral context beyond a simple solve. However, it lacks details about whether the tool is read-only, side effects, or error 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 concise, including domain, templates, checks, usage syntax, and cost in a few sentences. It is front-loaded with '[Physics ODE, SciPy]' and each part serves a purpose. Minor improvement could be listing templates more compactly.

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 has 2 parameters, no output schema, and no annotations, the description adequately covers core functionality and usage. However, it lacks details about return format, error conditions, or how energy checks are reported, leaving gaps for a complete understanding.

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% but schema descriptions are generic ('The structured problem for this operation'). The description adds significant meaning by specifying exact spec structure: {template, params, tSpan:[t0,tf]}, and operation value 'solve'. This compensates for vague 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?

The description clearly identifies the tool as solving coupled ODE systems from parameterized templates, listing specific examples like orbital 2-body and oscillator. It distinguishes from siblings like 'numerical' by focusing on ODEs and SciPy backend, and from 'symbolic' by being numerical.

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 provides explicit usage instructions: 'Pass operation='solve', spec={template, params, tSpan:[t0,tf]}'. It implies the tool is for ODE solving with energy checks, but does not explicitly state when not to use it or mention alternatives like generic numerical solver.

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 provided; description covers seeded determinism, shots, expectation values, and qubit limit, but lacks error behavior, rate limits, or auth requirements. Moderate transparency with gaps.

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?

Concise single paragraph front-loading purpose, packed with essential info. Could be better structured but efficient.

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 no output schema and complexity, description omits return format or output details. References quantum_artificer_info for spec shapes, but lacks completion regarding expected output.

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% but schema descriptions are minimal. Tool description adds significant meaning by specifying operation='simulate' and detailing spec structure (qubits, gates, shots, method), surpassing schema alone.

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 clearly states it simulates quantum circuits using specific methods (statevector, density-matrix, etc.) and mentions sync tier and cost. However, it does not explicitly differentiate from sibling tools like quantum_artificer_info, relying on context.

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 small/medium circuits under 24 qubits and mentions cost, but does not explicitly state when to use alternatives or exclude cases. No explicit when-not guidance.

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?

The description declares it is 'FREE' and 'No wallet needed', which implies it is a read-only, non-destructive tool. It also explains what information it returns. Without annotations, it covers the essential behavioral aspects, though it could explicitly state there are no side effects or rate limits.

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, well-structured paragraph that front-loads key information ('FREE'), states the purpose concisely, and adds actionable guidance. Every sentence adds value without redundancy.

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 there is no output schema and no parameters, the description fully explains what the tool returns (catalog of domains, operations, input-spec shapes, limits, prices) and why it should be called first. It addresses the agent's need to learn how to build specs for sibling tools, making it contextually 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?

The input schema has no parameters with 100% coverage, so the description need not add parameter details. However, it goes beyond by describing the output: the 5 domains, operations, shapes, limits, and prices – which helps the agent understand what data it will receive.

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 provides a full catalog of Quantum Artificer domains, operations, input-spec shapes, limits, and prices. It explicitly distinguishes itself from sibling tools by noting it is free and requires no wallet, and by positioning itself as a prerequisite for paid compute tools.

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 explicitly says 'Call this first to learn how to build the `spec` for the paid compute tools' and mentions 'No wallet needed', which implies when to use it and that it's a free informational resource. It does not explicitly state when not to use it, but the context is clear enough.

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 provided; description lists operations and mentions verification but does not disclose behavioral traits like idempotency, side effects, or error handling. Adequate but not rich.

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?

Single paragraph, front-loaded with 'Symbolic math, SymPy', every sentence adds information. No wasted words.

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?

Complex tool with nested objects and no output schema. Description partially explains spec format but refers to external tool (quantum_artificer_info) for shapes, leaving gaps. Could include return value info.

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%, but description adds significant value with example JSON AST spec and list of operations, clarifying the structured input beyond schema.

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 clearly states it's for symbolic math using SymPy, lists specific operations (simplify, differentiate, integrate, etc.), and distinguishes from sibling tools like chemistry, numerical, etc.

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?

Provides clear context for when to use (symbolic computation) with example input. Does not explicitly exclude alternatives, but the domain is well-defined.

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