resolve_constraints

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

With no annotations provided, the description carries the behavioral disclosure burden. It transparently states what the tool determines, identifies, and returns, including the JSON response shape. It does not explicitly say whether it is read-only or may make network calls, but the verbs 'determine' and 'retrieve' strongly imply a non-mutating analysis operation.

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 well-structured with a summary paragraph, an Args section, and a Returns section. Every sentence contributes meaningful information without redundancy or padding.

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 3-parameter tool with no annotations, the description is mostly complete: it covers purpose, parameters, and return type. It could further strengthen sibling differentiation and clarify expected behavior with external resources, but the core invocation context is sufficiently specified.

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 0%, so the description must fully compensate. It does: task_description is explained as the coding task description, libraries as an optional list or mapping with an example, and project_dir as the Python project root with a default. This adds meaning beyond the bare 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 opens with a specific verb-resource pair: 'Determine exact project dependency versions and retrieve authoritative version-specific constraints.' It also enumerates concrete outputs (APIs, deprecated/forbidden patterns, evidence sources), which clearly distinguishes it from siblings like inspect_stack and freshness_audit.

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 gives practical usage context: it takes a task description, allows optional library filtering, and targets a Python project directory. It does not explicitly name alternatives or exclusion criteria, but the purpose is clear enough for an agent to infer when this tool applies.

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