get_lambda_overview

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

No annotations are present, so the description fully bears transparency. It discloses that environment variable values are never returned (privacy) and that runtime signals (recentThrottles, recentErrors) are conditionally included. It does not cover potential pagination or read-only assurance, but for a zero-parameter tool, it is transparent enough.

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 short and front-loaded with the core purpose. Every sentence adds valuable context (return fields, usage scenarios, behavioral notes). No fluff or repetition.

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 no output schema, the description covers the return value comprehensively: which fields are included (runtime, memory, timeout, env var key names, triggers with event shape), and conditional runtime signals. It provides sufficient context for an AI to understand what to expect from the tool output.

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 parameters (schema coverage 100%), so per guidelines the baseline is 4. The description correctly does not need to add parameter information as there are none.

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 returns all Lambda functions with specific attributes (runtime, memory, timeout, env var key names, triggers with event shape). It distinguishes itself from sibling 'analyze_function' by offering a broader overview without deep analysis.

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?

Explicit usage guidance is provided: 'Call this when auditing Lambda configuration for default memory (128 MB) or high timeouts, or when you need the trigger event shape for a specific function without running analyze_function.' This directly tells the AI when to use this tool over alternatives.

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