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GaijinEntertainment

Pararam Nexus MCP

search_chats

Find chats by name or description in the Pararam Nexus MCP server to locate specific conversations and manage communication channels.

Instructions

Search for chats by name or description.

Args: query: Search query string limit: Maximum number of results to return (default: 20, applied client-side)

Returns: ToolResponse with SearchChatsPayload containing chat list including chat ID, name, type, and member count

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoError message if operation failed
messageYesHuman-readable summary of the result
payloadNoThe actual response data
successYesWhether the operation was successful
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. It mentions the 'limit' parameter is 'applied client-side,' which adds useful context about processing behavior. However, it lacks details on permissions, rate limits, error handling, or whether this is a read-only operation (implied but not stated), leaving significant gaps for a search tool.

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 well-structured and concise, with a clear purpose statement followed by Args and Returns sections. Every sentence adds value: the first defines the tool, the second explains parameters, and the third specifies the return format, with no wasted words.

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

Completeness4/5

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

Given the tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description is reasonably complete. It covers purpose, parameters, and return values, and the output schema handles response details. However, it lacks behavioral context like permissions or error cases, preventing a perfect score.

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

Parameters4/5

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 both parameters: 'query' as a 'Search query string' and 'limit' with its default value and client-side application. This adds meaningful semantics beyond the bare schema, though it could elaborate on query syntax or limit constraints for a higher score.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Search for chats by name or description.' It specifies the verb ('search') and resource ('chats'), and indicates the searchable fields. However, it doesn't explicitly differentiate from sibling tools like 'search_messages' or 'search_users,' which would be needed for a perfect score.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'search_messages' (for searching within chat content) or 'get_chat_messages' (for retrieving messages from a specific chat), leaving the agent to infer usage context without explicit direction.

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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