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get_chat

Retrieve a full conversation by its UUID, with pagination support to load long chats in parts.

Instructions

Fetch a specific conversation from history by uuid (with pagination).

USE when:

  • You have already identified a relevant chat (usually via search_chats) and need more context than the result snippet.

  • The user gave you a specific uuid and asked you to review it.

  • You are following a thread and need detail from a specific chat that appeared earlier in the conversation.

DO NOT use to discover new chats without searching first. To find chats by topic or keyword, use search_chats.

If the chat is long, call again with messages_offset to paginate (truncated response is indicated by truncated: true and total_messages).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
uuidYesChat UUID (typically obtained via `search_chats` or `list_recent_chats`).
messages_limitNoHow many messages to fetch starting from the offset (default 10, max 100). Individual messages are truncated to 1500 chars so the total response fits in the MCP client's token cap (~17k chars worst case with the default). If you need more detail per message, ask for fewer messages (e.g. messages_limit=5) and you can request a higher messages_limit in chats with short messages.
messages_offsetNoHow many messages to skip from the start of the chat (default 0). Use this to paginate long chats: first call with offset=0, second with offset=10, etc.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations provided, but the description fully bears the burden. It discloses pagination behavior, token limits, message truncation to 1500 chars, and how to handle long chats. No destructive traits to disclose.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (about 150 words) and well-structured with sections. The main purpose is front-loaded. Every sentence adds value; no fluff or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema and no annotations, the description covers all needed context: use cases, alternatives, pagination, token limits, message truncation. It is complete for an agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with good parameter descriptions. The description adds significant value by explaining pagination mechanics, token cap considerations, and practical usage advice like asking for fewer messages for more detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Fetch a specific conversation from history by uuid (with pagination).' It specifies the verb, resource, and key detail. It distinguishes from siblings by indicating this tool is used after searching.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit 'USE when' and 'DO NOT use' sections, contrasts with search_chats, and gives specific scenarios. The guidance is clear and actionable.

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

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