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

The description adds substantial behavioral context beyond the sparse annotations: invoke runs a tool one-off without adding the MCP to the toolkit, missing credentials produce a connect link, paid tools with empty wallets produce a checkout link, and writes require workspace owner/admin. It also explains installed_in_toolkit vs installed_in_workspace flags. There is no contradiction with the annotations.

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 densely informative, front-loading the core marketplace purpose and the search→describe→invoke flow. It contains no filler, but it is one continuous run-on paragraph with semicolons and many clauses that could be easier to parse with bullets or subheadings.

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 23-parameter, multi-action tool with no output schema, the description is unusually complete: it covers the full lifecycle, install vs invoke semantics, auth/checkout recovery, permission requirements, and the separate prompt library. It omits the 'resume' action and does not explain return/error behavior or several secondary parameters, which prevents a 5.

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 carry the parameter-meaning load. It does explain the action enum values and the roles of query, mcp_id, tool_id, arguments, and prompt_vars/slug. However, many parameters—limit, immediate, tier_slug, cancel_reason/comment, report_context, request_details, prompt_body/title/category/targets, and conversation—are never mentioned, so the compensation is partial rather than complete.

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 is 'the official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them' and covers capability requests like 'find an MCP that does X'. It names the core actions (search, describe, invoke, install) and the prompt library, so the resource and main verbs are specific. However, it does not explicitly differentiate this tool from siblings like toolkit_info or report_bug, so it misses the top score.

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 guidance is explicit and detailed: it gives the search→describe→invoke core flow, contrasts 'prefer invoke for a single/occasional use' with 'Use install only to make an MCP PERMANENT', and explains when to use list_tools, subscribe/cancel, report_bug, and request_mcp. It also explains the connect/checkout recovery workflow and notes write permissions, which is exactly the kind of alternative guidance the rubric rewards.

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