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ozon_chat_history

Retrieve chat message history for a specific chat ID. Set an optional limit to control how many messages are returned.

Instructions

Chat message history (история чата).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
chat_idYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv2.5.2

TDQS

D1.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure, and the only behavioral implication is that this is a read/history operation rather than a send operation. There is no mention of pagination, message ordering, what the default limit of 50 controls, response format, or any constraints. The description adds essentially no behavioral transparency beyond what the name already implies.

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

Conciseness2/5

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

The description is short, but this is under-specification rather than conciseness. The Russian parenthetical '(история чата)' is pure duplication that adds no value for an agent, while genuinely useful information about parameters, behavior, and use context is absent. Every token is spent restating the name instead of earning its place with new content.

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

Completeness2/5

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

The tool's surface complexity is low (2 params, 1 required, no nested objects), which lowers the bar, but with no annotations, no output schema, and 0% parameter coverage, the description is the sole source of context. It leaves unanswered what the return value looks like, where chat_id originates, and how limit behaves, making the tool insufficiently specified for reliable invocation.

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

Parameters1/5

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

Schema description coverage is 0%, so the description is the only possible source of parameter meaning, and it provides none. It does not explain that chat_id identifies whose chat history to fetch or where a valid chat_id comes from (e.g., ozon_chat_list), nor what limit governs (number of messages, page size). The description fails entirely to compensate for the uncovered schema.

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

Purpose2/5

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

The description 'Chat message history (история чата)' is a near-direct restatement of the tool name in English and Russian, adding only the word 'message' and nothing else. It names the domain (chat) but supplies no explicit verb, no scope, and no differentiation from sibling chat tools like ozon_chat_updates, ozon_chat_list, or ozon_chat_read. This functions as a tautology rather than an informative definition.

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

Usage Guidelines2/5

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

The description gives no guidance about when to use this tool versus the several chat-related siblings (ozon_chat_list, ozon_chat_updates, ozon_chat_read, ozon_chat_start). An agent has no way to determine whether this fetches a full transcript, polls for new messages, or overlaps with ozon_chat_updates, so tool selection is left entirely to inference.

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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