get_agent_chat_context

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

With no annotations, the description carries full burden. It discloses chronological order and message types included. However, it does not mention authentication requirements, rate limits, or behavior for edge cases (e.g., empty chat, large pages). It covers the basics but lacks some operational details.

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 well-structured with bullet points for included content and a clear Args section. It is concise, every sentence adds value, and the important information is front-loaded.

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 that an output schema exists, the return description is sufficient. The tool covers what it does, when to use it, and parameters. Minor gaps like error handling or empty results are not addressed, but overall it is complete for its purpose.

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 0%, so the description must compensate. It explains all three parameters: chat_id as required identifier, page and page_size with defaults and max. This adds significant value beyond the schema's bare titles.

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 retrieves conversation context for agent rehydration, specifying exactly which messages are included (all agent-sent messages and @mentions). It distinguishes itself from siblings like get_agent_next_message or list_agent_messages by explicitly stating its use case for rehydration.

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 explicitly says 'Use this to load the complete context a remote agent needs to resume execution,' providing clear guidance on when to use it. It does not mention when not to use it or directly name alternative tools, but given the sibling list, the context is clear enough for an AI agent.

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