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

Despite annotations already declaring readOnlyHint=false and openWorldHint=true, the description adds rich behavioral context: writes require 'workspace owner/admin', invoke runs tools 'one-off' without installing them, missing credentials return a connect link, and paid tools with an empty wallet return a checkout/top-up link. It also discloses environment distinctions (installed_in_toolkit vs installed_in_workspace) and that mcp.ai/p/<slug> links 'open without login'. No contradiction with 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 content is dense and every clause adds value, but it is effectively a wall of text: one massive first sentence uses run-on em-dash/ellipsis breaks to cover the entire marketplace flow, and the prompt library addition is bolted on as a final sentence. It is front-loaded appropriately (core flow first) and wastes no words, yet the lack of any structural formatting for a 14-action tool makes it harder to parse than necessary.

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?

Given the enormous surface (23 params, 14 enum actions, no output schema, no annotated parameters), the description admirably covers the core flow, permission model, auth/billing edge cases, and sub-features. Gaps remain: the 'resume' action is never explained, no return-value or error semantics are given for any action, and the boundary with sibling tools connect/authenticate is only implicit. These are acceptable given message-length constraints on a single tool.

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 across 23 parameters, the description must compensate, and it explains the critical action/mcp_id/tool_id orchestration ('every tool with its id + params, pricing, auth' so you pick the right tool_id) and the prompt_vars intent ('with {{variables}} filled'). However, a long tail of params — limit, query, message, immediate, tier_slug, cancel_reason, conversation — is never clarified, requiring inference for this sprawling surface.

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 states a specific verb+resource+scope: it is 'the official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them', and lays out the exact core flow: 'action=search discovers MCPs by intent → describe returns one MCP's full profile ... → invoke RUNS that tool'. It names concrete capability requests ('find an MCP that does X', 'consulta um CPF') and differentiates the prompt library sub-feature ('about ready-made prompt TEXT rather than MCPs'). It clearly distinguishes from siblings like show_version and toolkit_info by being the catalog/execution tool.

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?

Explicit guidance is everywhere with clear when-to-use vs. alternatives: 'Use install only to make an MCP PERMANENT in the active toolkit', 'prefer invoke for a single/occasional use', and 'request_mcp asks us to build a NEW MCP when nothing fits'. It handles edge cases (when the MCP is NOT installed, empty wallet, missing credential → connect/checkout links) and even prescribes follow-up behavior ('the user opens it, then you retry').

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