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

With no annotations, the description carries the full burden and does so thoroughly: it explains token handling rules, data storage and translation to Azure Translator, prohibition of passwords/payment data, and legal/operational caveats (inquiry acceptance does not establish overseas sales or shipping availability, subject to staff review). This is rich behavioral context beyond what any annotation could provide.

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 somewhat lengthy and mixes multiple concerns (token rules, data storage, translation, confirmation steps, legal disclaimers) in a dense paragraph. It is front-loaded with important constraints but could be better structured with separate sections or bullets for clarity. Some sentences are run-on but most are informative.

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 13-parameter mutation tool with no annotations and no output schema, the description covers essential context: what it does, how to use it, data handling, privacy, confirmation requirements, and limitations. It is complete enough for an agent to invoke correctly without needing to guess.

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 description coverage is 0%, so the description must compensate. It does explain key parameters indirectly (company, contact email, destination country, subject, details, language code, journey_token) and their handling rules. However, it does not cover many of the 13 parameters (manager_name, contact_info, quantity, specifications, inquiry_category, contact_phone) and does not provide format or validation details. Baseline 3 is appropriate—partial compensation.

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 the tool saves a customer-confirmed B2B inquiry (quote, sample, or supply-feasibility) rather than an order, which is a specific verb+resource. However, it does not distinguish this tool from its siblings (check_environment, get_hvac_filter_info, search_carsafe, set_crm_lifecycle) since none are similar, so the differentiation is implicit.

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 is exceptionally explicit: it says when to use it (save a customer-confirmed inquiry), what not to do (not an order), how to handle tokens (reuse only within the same conversation for this customer, never invent), and what to do before submitting (show the inquiry and obtain confirmation). It also lists alternatives like product lookups for context.

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