request_data_access

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

No annotations are provided, so the description carries the burden. It usefully discloses the conditional side effect of the MARKETPLACE_USE_DATA_CONTRACT_SUBSCRIBERS flag (whether a local record is created or Atlas is the single source of truth). However, it doesn't disclose whether this triggers a review/approval workflow, whether permissions take effect immediately, or the response/return behavior.

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 reasonably concise with a purpose statement, a conditional behavior note, and an Args list. The Args section is efficiently formatted. However, it's bilingual (German headers with mixed terminology) which adds slight cognitive load, and the DSGVO reference could be clarified, but overall each sentence earns its place.

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 6 required parameters, no annotations, and the description compensates well with conditional behavior disclosure and parameter examples. An output schema exists, so return-value documentation is not needed from the description. The description is fairly complete for the tool's complexity, though it could note whether this triggers an asynchronous approval workflow given siblings like approve_access_request and reject_access_request exist.

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 carries the full parameter documentation burden. The description provides examples for product_id (DP-MART-CREDIT-QUALITY-MATRIX), requester (E-Mail oder User-ID), purpose (DSGVO/Revision), and target_environment (marketing-snowflake), giving meaningful semantic context for all 6 parameters. Format hints for duration_months (in months) are also present.

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 verb-resource ('Zugriff anfordern / abonnieren') and explains that it registers subscribers in the Data Contract consumer attribute. It distinguishes itself from siblings like list_access_requests, approve_access_request, and list_product_subscribers, though it doesn't explicitly name them. The German-language description is specific about what is registered and where.

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 about when this tool is relevant (for requesting/subscribing access in Atlas) and notes the conditional behavior based on the MARKETPLACE_USE_DATA_CONTRACT_SUBSCRIBERS flag, explaining that when true (default), no local subscriber record is created. However, it doesn't explicitly contrast with sibling tools like approve_access_request to state 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.