quantum-artificer

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

No annotations provided, so description carries full burden. It discloses bundled mechanisms and cost, but does not describe side effects, error behavior, or whether it is read-only. 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?

The description is one paragraph that includes purpose, constraints, and an example. It is concise but could benefit from clearer structure or front-loading of key information.

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 tool has no output schema, so the description should explain return values. It does not mention what the tool returns, leaving agents uninformed about the result format. The example shows input but not 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 description coverage is 100%, but schema descriptions are terse. The tool description adds significant value with an example and constraints, clarifying the structure of the 'spec' parameter beyond 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 clearly states the tool is for physical chemistry thermochemistry using Cantera and CoolProp, listing specific operations like equilibrium and adiabatic flame temperature. It distinguishes itself from siblings which are in different domains (e.g., numerical, 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?

The description provides an example and notes constraints like bundled mechanisms, implying usage context. However, it does not explicitly state when to use this tool versus alternatives or when not to use it.

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 the burden. It mentions output characteristics (residuals, condition numbers) and cost, but does not disclose permissions, side effects, or other behavioral traits.

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 plus a cost note, front-loading the domain and operations. Every sentence adds value with no 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 the complexity (multiple operations, nested spec) and no output schema, the description covers the main idea with examples and a reference for spec shapes. It is mostly complete but could detail spec structure for each operation.

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 descriptions are generic. The tool description adds meaning by listing supported operations and giving an example with a concrete spec shape, exceeding the 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?

The description clearly lists specific numerical operations (root-finding, optimization, integration, eigenvalues, linear solves) and distinguishes the tool from siblings like 'quantum' and 'symbolic' by naming the SciPy domain.

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 numerical tasks and provides an example, but does not explicitly state when to use this tool versus alternatives or when not to use it.

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 burden. It mentions energy/conservation checks and includes a cost indicator ($0.01 USDC), which adds some behavioral context. However, it does not disclose side effects, resource usage, or whether the operation is read-only, leaving 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?

The description is two sentences, front-loaded with purpose and examples, and every phrase adds value. No redundancy or filler.

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?

While the input format is fairly well described (operation and spec with subfields), the description lacks details about the output format. Given no output schema, the user is left to infer what solving returns (e.g., solution array, energy values). The reliance on 'quantum_artificer_info' for spec shapes also creates incompleteness.

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 schema has 100% coverage but uses a generic description for 'spec' that defers to another tool. The description compensates by listing the expected fields (template, params, tSpan) and giving concrete examples of templates, adding significant meaning beyond 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 clearly states it solves coupled ODE systems and lists specific templates (orbital 2-body, oscillator, etc.). It distinguishes from siblings like 'chemistry' or 'quantum' by domain and approach, though it could explicitly contrast with 'numerical' for better differentiation.

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 provides usage instructions (operation='solve', spec with template, params, tSpan) and hints at use cases via example templates. However, it does not explicitly state when not to use this tool or offer alternatives among siblings, relying on implied context.

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 discloses synchronous behavior, qubit limit, cost ($0.05 USDC), seeded determinism, and simulation methods. Missing details on error handling or rate limits, but sufficient for basic awareness.

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 dense paragraph of about 50 words. It front-loads purpose and packs useful details. Could benefit from slight structuring (e.g., bullet points) but remains 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?

No output schema exists, yet the description does not explicitly state what the tool returns (e.g., statevector, expectation values). It hints at outputs ('shots, expectation values') but does not clarify structure or examples. Also missing seed parameter usage, leaving some ambiguity.

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% coverage but vague descriptions ('The structured problem for this operation'). The description adds concrete meaning: operation='simulate', spec includes qubits, gates, shots, method. This greatly enhances parameter understanding beyond 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?

Description clearly states the tool simulates quantum circuits, lists supported methods (statevector, density-matrix, etc.), and specifies constraints (≤24 qubits, sync tier). This strongly identifies the tool's action and resource, distinguishing it from siblings like chemistry or 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='simulate', spec={qubits, gates:[{name,qubits,params?}], shots?, method?}' and states the qubit limit. However, it does not mention when to avoid this tool or recommend alternatives like quantum_artificer_info for metadata.

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, but the description fully compensates by stating the tool is free, requires no wallet, and returns a catalog. As a read-only info endpoint, this discloses all relevant behavioral traits without contradiction.

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, front-loaded with key information ('FREE', 'Full catalog'), and every word adds value. It is appropriately sized and structured for quick comprehension.

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 simple info tool with no output schema, the description adequately covers what the tool returns (domains, operations, input specs, limits, prices). It provides sufficient context for the agent to decide when and why to call this 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?

There are no parameters, so the input schema coverage is trivially 100%. The description adds value beyond the empty schema by explaining what the output contains (domains, operations, etc.), which is helpful for the agent to understand the tool's purpose.

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 that this tool provides a free catalog of the Quantum Artificer system, listing domains, operations, input specs, limits, and prices. It also instructs to call this first to learn how to build specs for paid tools, making the purpose unmistakable.

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 explicitly says 'Call this first', indicating the primary use case as an initial discovery tool. It explains what the tool returns (domains, operations, etc.) and implies that alternative tools (the paid compute tools) should be used after consulting this info. This clearly differentiates it from sibling tools like chemistry and numerical.

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 discloses that it performs exact symbolic computation with JSON ASTs and arbitrary-precision evaluation, and mentions numerical verification. No annotations are provided, so the description carries the full burden, and it adequately covers key behavioral traits.

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 very concise (two sentences plus an example), front-loaded with the main purpose, and every sentence adds value.

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, the description provides sufficient context for a symbolic math tool, including an example and mention of verification. It could be more explicit about return values, but the complexity is well-covered.

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?

Both parameters are described in the schema (100% coverage), and the description adds meaning by explaining that spec is a structured problem and giving an example with operation='integrate'. This enhances understanding beyond 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 clearly states it is for exact symbolic computation using SymPy, listing specific operations like simplify, differentiate, integrate, etc. It distinguishes itself from sibling tools like numerical or chemistry by focusing on symbolic math.

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 clear usage guidance: pass an operation and spec, with an example of the spec format. It mentions verification and cost but does not explicitly state when not to use or compare with alternatives.

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