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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds useful behavioral context: it notes the 'filters' section is omitted when none exist, explains that include embeds additional data, and warns that JSON output is 'larger and noisier for an LLM to read.' It does not contradict annotations. It doesn't cover pagination or rate limits, but those are delegated to the sibling 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 a single, well-organized paragraph that front-loads the core purpose, then flows naturally into optional includes, then the sibling pointer. Every sentence earns its place—no filler or redundancy. The structure makes it easy for an agent to quickly extract the key decision points.

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 tool with 4 parameters, no output schema, and rich annotations, the description covers all necessary operational aspects: default return, optional sections, parameter usage, output format guidance, and redirection to the sibling for extended functionality. Nothing essential is missing for an agent to invoke it correctly.

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 description. The description adds value by explaining the default behavior of include (not specified in schema) and providing rationale for choosing output_format (markdown vs json), including a caution about JSON verbosity. It also clarifies the relationship between match_limit and include, reinforcing the schema's default of 5. This goes beyond mere repetition.

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 a specific verb ('Fetch') and resource ('a single alert's full configuration'), enumerates the content (title, kind, cadence, watched entities, emails, filters), and explicitly differentiates from the sibling tool particle_alert_list_matches by noting it returns the full paginated history. This leaves no ambiguity about the tool's role.

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 provides explicit usage guidance: it explains the default behavior (configuration only), how to request embedded sections via include, and directly instructs when to use an alternative tool ('For the full, paginated match history with transcript excerpts, use particle_alert_list_matches'). It also clarifies when to use output_format=json (programmatic chaining) vs markdown, covering both when and when-not to use this tool.

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