get_ai_context

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 discloses that the tool auto-creates .contextignore on first call, returns 2-3k tokens in default mode, and auto-disables includeDocs/includeSkeleton when '*' is used. These are important behavioral traits that go beyond what the schema reveals.

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 compact and well-structured, using numbered modes to present the two behaviors. Every sentence adds meaningful information: the default mode, the wildcard mode, the .contextignore side effect, and when to use it. No filler or 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?

Despite having no output schema, the description explains what the tool returns in both modes (skeleton+docs vs. all source code), the token footprint, and the auto-creation of .contextignore. It doesn't specify the output format (e.g., plain text vs JSON), but for an AI context bootstrapping tool this is likely acceptable. Overall, enough detail is provided for correct invocation and expectation setting.

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% and each parameter already has a detailed description. The description adds value by explaining the interaction between parameters: using includeFiles: ['*'] triggers a 'pure code dump' and automatically disables includeDocs and includeSkeleton. This cross-parameter semantics is not captured in 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 that the tool 'boots AI agent context' and distinguishes two modes: default returns skeleton+docs, while includeFiles: ['*'] returns all compressed source code. It names the resource (project context) and explicitly positions itself as the first tool to call ('Call FIRST when starting work'), which differentiates it from siblings like get_skeleton and docs.

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?

Provides explicit guidance: 'Call FIRST when starting work' and advises using ['*'] for small/medium projects that fit in context. This implies for larger projects one should avoid the wildcard mode. It doesn't name alternative tools or explicitly state when not to use, but the guidance is clear and actionable.

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