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

With zero annotations provided, the description carries full behavioral-transparency burden, and it uses that responsibly: it discloses that a colleague will actually call the supplied number within 24 hours and that the case is taken from on-site survey through installation, so the tool has real external consequences. It also reveals the system-side validation behavior (invented/placeholder data is rejected). Missing details such as the response shape or error behavior are not covered, but the safety-critical behaviors are clear.

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 essential purpose and trigger appear in the first sentence, and each paragraph does carry real information (trigger, 24-hour callback, fake-data prohibition, need to ask the user). However, the same no-test/no-placeholder rule is repeated in multiple forms and in both languages, including repeated sample placeholders, making the text notably redundant for an MCP description. It is front-loaded but not economical.

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 mutation tool that triggers a real human callback, the description provides the agent with everything needed to call it correctly: the condition to call, which real data to collect, the constraint to ask the user when data is missing, and the post-call effect (24-hour callback and full case handling). Output schema is not provided, so the description does not need to document the return; the only genuine omission is the broader workflow (e.g., what comes after the returned code, interactions with confirm_quote).

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 already covers all 6 parameters (100% coverage), including per-parameter instructions on the real name, real phone intended for the callback, email receiving the 6-digit verification code, and the allowed interest values. The description adds only a general cross-parameter warning about real data; it does not enrich how any individual parameter is to be used or formatted. With full schema coverage, the baseline of 3 is correct.

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 opens with the role ('MEGRENDELÉS-INDÍTÓ ESZKÖZ' — the request-initiating tool), states a specific action (record the user's quote request/order intent, submit a REAL callback request), and gives the trigger condition (the user is satisfied with a price or stock item). This separates it from the stock- and price-oriented siblings (check_stock, get_price_estimate, list_inventory) and from confirm_quote by focusing on a new callback, not an already confirmed quote.

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?

Usage conditions are explicit: invoke the tool only when a real end-user has accepted a price/stock product and actually requested a callback with their own genuine contact details. It also states when-not: never use it for test/demo purposes, never input placeholder/example data, and if real contact data is missing, the agent must ask the user rather than invent it. The description stops short of a 5 because it never names alternative sibling tools (e.g., use get_price_estimate first / check_stock for availability), leaving some routing inference to the agent.

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