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finance

by com.zoninga
OverviewDiscussionsAdmin

Agent Chat: List agent chat sessions

list_agent_chat_sessions
Read-onlyIdempotent
    List the user's past AI assistant chat sessions.

    Useful for answering "what did we discuss last time?" or helping
    the user find a specific past conversation. Returns the most
    recently updated sessions first. Transcripts are not included.

    Args:
        limit: Maximum number of sessions to return (1-50, default 10).

    Returns:
        {"sessions": [{id, title, is_active, message_count, ...}, ...],
         "count": N}

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_agent_chat_sessions"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world). The description adds meaningful behavior beyond that: most-recently-updated-first ordering, exclusion of transcripts, and the return payload shape.

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?

Front-loaded with the purpose, then usage context, then structured Args/Returns sections. No filler sentences; every line adds information an agent can act on.

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?

For a simple read-only list tool with one optional parameter and no output schema, the description supplies ordering, content exclusions (no transcripts), parameter bounds, and the return structure — everything needed to invoke and interpret it.

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 description coverage is 0%, so the description carries the full burden, and it does: it documents the sole parameter's meaning, range (1-50), and default (10), fully compensating for the undocumented schema.

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?

States a specific verb and resource ('List the user's past AI assistant chat sessions') and clarifies scope by noting transcripts are not included, which implicitly separates it from get_agent_chat_session. It doesn't explicitly name that sibling, so it stops short of full differentiation.

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

Usage Guidelines4/5

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

Gives concrete usage contexts ('what did we discuss last time?', finding a specific past conversation), which tells the agent when this tool is appropriate. There is no explicit when-not guidance or named alternative, but the trigger conditions are clear.

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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