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

The description goes well beyond annotations by revealing key behaviors: invoke works even when the MCP is not installed, does not bloat the tool list, returns connect/checkout links with a retry pattern, and flags installed_in_toolkit vs installed_in_workspace. It also discloses permission requirements for write actions. Annotations said readOnlyHint=false and destructiveHint=false, and the description adds substantial context without contradiction.

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 dense wall of text (~450 words) without bullet points or section breaks. While every sentence adds valuable information, the lack of visual structure makes it harder to parse. It is comprehensive but not concise in format.

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 14 actions, 23 parameters, and no output schema, the description covers the main flows end-to-end: search/describe/invoke, auth/billing edge cases, permissions, and the prompt library. It does not detail exact return formats for every action, but it is remarkably complete for the complexity.

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?

With 23 parameters and 0% schema description coverage, the description carries the burden of explaining parameter behavior. It explains the meaning of action values (search, describe, invoke, install, etc.), tool_id selection, arguments, prompt variables, and request_mcp. However, not all parameters are covered (e.g., limit, immediate, tier_slug, cancel_reason), so it's not a full compensation for the schema gap.

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 official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them,' which clearly states what the tool is and does. It enumerates the core flow (search → describe → invoke) and distinguishes the marketplace's role from sibling tools by covering report_bug, request_mcp, and the prompt library.

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?

Provides explicit when-to-use guidance: 'prefer invoke for a single/occasional use' and 'Use install only to make an MCP PERMANENT in the active toolkit.' It also explains when to use list_tools, subscribe/cancel, report_bug, and request_mcp, and notes that writes require workspace owner/admin. This gives clear decision criteria.

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