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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety and scope are covered structurally, and an output schema exists so return shape need not be described. The description adds the useful constraint that ids must originate from search results. It says nothing about failure behavior for stale or malformed ids, or whether the document is returned in full or truncated — modest value on top of 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?

Two short sentences, front-loaded with the use case followed by the exclusion — nothing redundant. The 'ChatGPT deep research' framing is slightly client-specific phrasing that costs a little clarity without adding information, keeping it just short of a 5.

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?

With one parameter, full schema coverage, annotations declaring read-only/local scope, and an output schema defining return values, the definition supplies nearly everything an agent needs to invoke it correctly. The only material gap is disambiguation from the entity-specific getter siblings, which the description leaves unaddressed.

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 100% and the single id parameter's schema already enumerates every prefix form (listing:{store}:{plugin_id}, app:{slug}, tag:{id}, chart:{store}:{chart}, server:{url}). The description restates the same type list without adding format rules or edge cases, so the schema is doing the work. Baseline 3 applies.

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 does name the resource — a document for an id of type listing, app, tag, chart, or MCP toolset — so the agent gets more than the bare verb 'fetch'. However, it frames the purpose around 'ChatGPT deep research' rather than the general operation, and it never distinguishes itself from siblings like charts_get, mcp_toolset_get, or listings_profile that appear to retrieve the same entity types by id. The purpose is inferable but not crisp.

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?

It gives a clear precondition ('when ChatGPT deep research needs a document for an id returned by search') and an explicit exclusion ('Do not use for ids outside search results'). That is genuine when/when-not guidance. What it omits is routing against the near-duplicate siblings — an agent holding a chart id has no signal whether to call fetch or charts_get.

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