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list_chatwork_reply_candidates

Read-only

Chatwork messages that are waiting for the user's answer — [To:me], replies to me, [toall], and direct chats — across ALL connected Chatwork accounts. A candidate the user answered with an exact reply ([rp] to that message) is replied (hidden unless include_replied) but is NOT resolved: answering is not finishing the request. Quotes and unrelated messages never clear a candidate.

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_chatwork_reply_candidates"
  }
}

Input Schema

NameRequiredDescriptionDefault
accountsNoChatwork account labels. Omit for every connected Chatwork account.
max_roomsNoRooms to scan per account (default 10, max 30).
include_repliedNoAlso return candidates the user already replied to (default false).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior4/5

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

With only readOnlyHint available, the description does real work: it discloses cross-account scope, the default hiding of already-replied candidates, and the non-obvious rule that replying does not resolve a candidate and quotes never clear one. Missing only output shape/ordering, which no output schema supplies.

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

Conciseness4/5

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

Two sentences, front-loaded with the core definition, and the second earns its length by pinning down the replied/resolved distinction. Slightly dense with Chatwork-specific tokens ([To:me], [rp], [toall]) but they are the domain vocabulary.

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?

No output schema exists, and the description covers what comes back (candidates with a replied state) and the account-level fan-out. It leaves open the result ordering and whether any mechanism exists to fully resolve/clear a candidate, which an agent might reasonably wonder about.

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 coverage is 100%, so baseline is 3, but the description adds genuine meaning to include_replied by explaining that replied candidates are marked `replied` and are explicitly NOT resolved — a distinction the schema text does not make.

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?

States a specific resource and a precise semantic filter: 'Chatwork messages that are waiting for the user's answer', enumerated as [To:me], replies to me, [toall], and direct chats. This clearly separates it from sibling listers such as list_unread_chats or search_chatwork_messages, which lack this 'awaiting answer' semantics.

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?

The inclusion criteria define when the tool is the right choice, and the 'hidden unless include_replied' clause tells the agent how to widen scope. However, no sibling tool is named as an alternative or exclusion, so the routing is inferred rather than explicit.

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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