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gamesme

chatlab-mcp

by gamesme

get_session_summaries

Get AI-generated summaries of chat sub-sessions to quickly survey discussed topics. Supports keyword filtering and time range. Returns text or JSON.

Instructions

Get AI-generated summaries of chat sub-sessions from the chat_session table. Use to quickly survey what topics have been discussed. Supports keyword filtering and time range. Returns text by default.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
session_idYesSession ID
keywordsNoFilter summaries containing any of these keywords (case-insensitive)
limitNoMax rows to return (default 20, max 100)
start_timeNoEarliest start_ts (Unix seconds)
end_timeNoLatest start_ts (Unix seconds)
formatNoOutput format: text (default) or json
timezoneNoTimezone for time display (default Asia/Shanghai)
Behavior3/5

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

With no annotations, the description carries full burden; it mentions default text output and filtering support but does not disclose potential issues like missing sessions, performance, or access restrictions.

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?

Two sentences concisely convey purpose and key features with no wasted words, ensuring quick comprehension.

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?

For a survey tool with 7 parameters and no output schema, the description covers purpose, filtering, and format. It lacks mention of pagination (limit) and timezone, but these are less critical for core use.

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

Parameters3/5

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

Schema coverage is 100%, so baseline is 3. The description adds context about keyword/time filtering and default format, but does not significantly enhance understanding beyond the schema's parameter descriptions.

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 the tool retrieves AI-generated summaries of chat sub-sessions and can be used to survey discussed topics, distinguishing it from sibling tools that return raw messages or full conversations.

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

Usage Guidelines3/5

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

The description implies usage for quick topic survey and mentions keyword/time filtering, but lacks explicit guidance on when not to use it or alternatives among siblings for different needs.

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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