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

Annotations only state readOnlyHint=false, idempotentHint=false, destructiveHint=false, and openWorldHint=true. The description goes far beyond: it reveals that invoke runs the MCP one-off without installing, that credential/payment gaps produce connect/checkout links followed by a retry, that writes (install/uninstall/subscribe/cancel and the one-off install behind invoke) require workspace owner/admin, and that search/describe flag installed_in_toolkit vs installed_in_workspace. This is rich behavioral context with no 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 long but every sentence carries operational detail, covering the method, side effects, auth, edge cases, and the additional prompt library. It is front-loaded with the purpose and uses clear connectors ('Core flow', 'KEY', 'Use install only'). It could be better structured with bullets or sections given the breadth, but the density is justified by the tool's ambition.

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?

Against a 23-parameter tool with no output schema, the description is remarkably complete on behavior, flows, and caveats: it explains one-off execution, connect/checkout retries, permanent installs, billing actions, and the prompt library. It omits response/output formats and the 'resume' action, and does not enumerate required parameters per action, so it is not fully complete, but it covers the essential operational context an agent needs.

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 0% schema description coverage, the description must compensate. It successfully explains the central 'action' parameter by walking through all enum values (except 'resume') and clarifies the roles of tool_id and mcp_id in the describe→invoke flow. However, many of the 23 parameters (e.g., immediate, tier_slug, conversation, cancel_comment, report_context, prompt_targets) remain unexplained, and the description does not map parameters to each action systematically. Thus it adds meaning for the core flow but leaves significant gaps.

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 a crisp definition: 'The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It enumerates the core flow (search→describe→invoke) and names each action (search, describe, install, invoke, list_tools, subscribe/cancel, report_bug, request_mcp, prompt library). This clearly distinguishes the marketplace from sibling tools like connect or authenticate, which are singular-purpose.

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 explicit guidance for the key decision: 'Use install only to make an MCP PERMANENT in the active toolkit; prefer invoke for a single/occasional use.' It also explains when to use list_tools, subscribe/cancel, report_bug, and request_mcp. However, it does not explicitly contrast with sibling tools (e.g., standalone report_bug vs. the report_bug action), and the 'resume' action is never mentioned. Still, the usage context is largely clear.

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