get_context

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

No annotations are present, so the description must carry the full burden. It states that context is assembled but does not disclose whether the operation is read-only, what inputs are expected (given zero schema parameters), what the assembled context includes, or any limitations.

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?

One short sentence, front-loaded with the core action and no filler. It earns a high score for conciseness, though brevity contributes to vagueness.

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?

With no annotations and no output schema, the description should explain what context is returned and how it is assembled. It only provides a high-level phrase, leaving major gaps for an apparently complex 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?

The tool has zero declared parameters and the schema is empty, so the baseline of 4 applies. The description adds no parameter semantics, but there are no parameters to document; the phrase 'natural-language question' hints at an implicit input.

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 uses a specific verb ('assemble') and identifies the resource ('task-relevant code context') and trigger ('natural-language question'). It is understandable but does not explicitly distinguish itself from sibling context tools like symbol_search or summarize_file.

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?

No guidance is provided about when to choose this tool over siblings; there are no exclusions or alternative references. The only usage hint is the phrase 'for a natural-language question,' which implies a broad query but does not say 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.