final-notice

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

No annotations are provided, so the description must cover behavioral traits. It discloses key aspects: generates a PDF, localized formatting, returns base64 resource, and free access. However, it omits details like error handling, processing time, or parameter validation behavior, which is a gap for a complex 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 extremely concise—two sentences—and immediately front-loads the core action ('Generate the finished demand-letter PDF'). Every word adds value; no redundancy. Ideal for quick comprehension.

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 good schema descriptions, the tool has 20 parameters, nested objects, and conditional logic (e.g., attested required for business senderType). The description does not synthesize this complexity or provide a high-level workflow. For such a feature-rich tool, the description is too brief to be fully contextually complete.

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?

All 20 parameters are described in the input schema (100% coverage), so the baseline is 3. The description itself adds no additional parameter-level meaning beyond what the schema already provides. It does not highlight dependencies or conditional requirements.

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 generates a finished demand-letter PDF with envelope, localized and legally formatted. It distinguishes itself from the sibling 'preview_demand_letter' by emphasizing 'finished' and the return of a base64 PDF, making the purpose unambiguous.

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 does not explicitly advise when to use this tool versus alternatives like preview. It only mentions the output format and cost, leaving usage context implied but not clearly defined. No guidance on prerequisites or when not to use.

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

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

No annotations, so description carries burden. Discloses returned data (code, name, currency, default language, languages, tones). Implies read-only but does not explicitly state non-destructive nature or safety profile.

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?

Two sentences, no fluff. First states functionality, second provides use guidance. Efficient and front-loaded.

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 parameterless listing tool with no output schema, the description fully covers what to expect (data fields) and when to use (initial call). Complete given simplicity.

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 has zero parameters (100% coverage baseline 4). Description enriches meaning by detailing output fields and the tool's purpose, exceeding baseline.

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?

Clear and specific: lists supported jurisdictions with fields (code, name, currency, default language) plus languages and tones. Distinguishes from siblings generate_demand_letter and preview_demand_letter, which are about generating letters, not listing.

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?

Explicitly says 'Call this first to pick a valid jurisdiction + language', providing clear when-to-use context. No explicit when-not-to, but the guidance is direct and actionable.

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

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

No annotations are provided, so the description must carry the full burden. The description correctly states that it does not produce a PDF and returns structured JSON, which is non-destructive. However, it does not describe the exact structure of the returned JSON (e.g., keys like title, body, etc.), nor does it mention any authorization requirements or side effects. The description is adequate but lacks some behavioral details beyond the obvious.

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 extremely concise: two sentences that clearly state the tool's function and usage. It is front-loaded with the main action and purpose, and every word adds value. 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?

The tool has 20 parameters (5 required), no output schema, and a nested object. The description mentions that the output is structured JSON but does not elaborate on its structure or any limitations. Given the complexity (many parameters, no output schema), the description is somewhat incomplete as it doesn't describe the return format or any validation rules beyond the schema.

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 input schema provides 100% coverage with descriptions for all 20 parameters, so the schema itself is informative. The description does not add any additional meaning beyond the schema; it just restates the overall purpose. According to the guidelines, baseline is 3 when schema coverage is high, and the description adds no extra parameter semantics.

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 renders demand letter text content as structured JSON without producing a PDF. It specifies the exact resource (demand letter text) and action (render as structured JSON), and distinguishes it from the sibling tool 'generate_demand_letter' which likely produces a PDF.

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 clear context for when to use this tool: 'Use to review or refine wording before generating.' This implies the alternative is generating the final PDF (via 'generate_demand_letter'), though it does not explicitly state when not to use this tool. The mention of 'before generating' gives good usage guidance.

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