ChatGPT Remote MCP Server

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

Annotations already declare readOnlyHint and openWorldHint, so safety is covered; the description goes well beyond that by disclosing statelessness (no memory of earlier requests), that language follows the prompt, that web search is conditionally triggered, and that every consulted source is returned with full metadata. This is meaningful behavioral context an agent needs to invoke it correctly.

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 opening sentence is a dense list of return fields, then usage guidance follows, so it is reasonably front-loaded and every sentence carries information. It is close to the upper bound of comfortable length for a two-parameter tool, costing a point against truly tight prose.

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?

There is no output schema, yet the description fully specifies the return payload (answer, sources with metadata, links, title, model, search flag), plus behavior and usage. Nothing an agent needs to select or call the tool is missing.

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%, so both parameters are already fully documented in the schema (character limit, language behavior, IANA timezone framing). The description restates the language and timezone semantics but adds no syntax or format detail beyond what the structured fields already provide, so 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?

States a specific verb and resource (send a prompt to ChatGPT, get the answer) and then enumerates exactly what comes back: markdown answer, web sources with domain/title/snippet/date/thumbnail, inline links, conversation title, model, and search flag. An agent can tell this is a prompt-to-answer Q&A tool rather than a SERP scraper without opening the schema.

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?

Gives concrete use cases (answer questions with cited sources, ground a RAG pipeline, summarise current events, build datasets) and a key constraint ('a fresh conversation with no memory of earlier ones'). It does not explicitly name the most obvious alternative, hasdata_perplexity_chat_getPerplexityAnswer, or state when not to use it, so it falls short of full when/when-not/alternatives guidance.

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