query_executable

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 clearly marks the operation as 'Read' (implying read-only), and adds valuable nuance about the deterministic oracle, the distinction between certified QUANT assertions and interpretive NARRATIVE, and knowledge graph links. It omits auth/rate-limit details but is sufficient for a query 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?

The description is dense but well-organized with an intro, Args, and Returns sections. Every sentence contributes meaningful detail, though it is longer than strictly necessary; the structure aids readability and reference.

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?

Paradoxically, the description includes a fully detailed Returns JSON structure, obviating the need for a separate output schema. It covers conditions (corner/temp/vdd), PDK, tools, and knowledge graph connections, making the tool's behavior and result format completely transparent for its complexity.

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 only provides a type and default for topology_class, while the description explains its meaning with an example ('miller_ota_2stage_nmos_in') and explicitly defines the empty string as 'all'. This adds significant value 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 begins with 'Read the executable-circuit substrate', a specific verb+resource, and further details 'simulation-VERIFIED circuit specimens and their claim-cards', clearly distinguishing it from sibling tools like query_knowledge or get_evidence by focusing on oracle-certified quantitative claims versus interpretive narratives.

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 identifies the tool as 'the evidence layer for teaching', providing clear context for when to use it (when oracle-certified quantitative facts are needed). It also warns that the mechanism narrative must not be taught as oracle-certified fact, but does not explicitly name alternative tools or state 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.