[Numerical, SciPy] Root-finding, optimization, numerical integration (quad), eigenvalues/vectors, linear solves — with residuals and condition numbers. Pass {operation, spec}, e.g. operation='eigen', spec={matrix:[[2,1],[1,2]], symmetric:true}. $0.01 USDC.
| Name | Required | Description | Default |
|---|---|---|---|
| spec | Yes | The structured problem for this operation (see quantum_artificer_info for shapes). | |
| operation | Yes | Operation within this domain. |
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.
Add one secure layer between your agents and this server.
Each tool targets a distinct domain (chemistry, numerical, ODE, quantum, symbolic, info) with clear, non-overlapping purposes. The descriptions explicitly separate capabilities, making it easy to choose the correct tool.
Most tool names are single lowercase words (chemistry, numerical, ode, quantum, symbolic), with one exception (quantum_artificer_info) that uses underscores. The naming pattern is mostly consistent, though 'ode' is an abbreviation while others are full words.
With 6 tools covering distinct scientific domains, the count is well-scoped. Each tool earns its place, and the number is appropriate for a specialized computation server.
The tool set covers the major domains hinted by the server name: chemistry, numerical methods, ODEs, quantum simulation, and symbolic math. The info tool assists navigation. Minor gaps exist (e.g., no explicit classical mechanics beyond ODE templates), but overall coverage is solid.