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Gladly MCP Server and API by Cotlet

Close conversation in Gladly

gladly_close_conversation
Read-onlyIdempotent

Close conversation in the user's own Gladly account from ChatGPT, Claude or any AI agent. Returns how to do it in Gladly step by step (screens, buttons, fields) and opens nothing by itself; then do it with the cotlet_browser_* tools in the user's Gladly Cloud Browser. Gladly's official API: Gladly has a full REST API (version 1.0), but no official MCP server (checked 20 Oct 2026).

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": "gladly_close_conversation"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.7/5.0
Behavior4/5

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

Beyond the annotations' readOnlyHint/destructiveHint declarations, the description adds genuinely useful behavior: it returns concrete navigation steps (screens, buttons, fields) and explicitly 'opens nothing by itself'. That resolves the apparent tension between a name like 'close' and a non-destructive annotation. It does not describe the shape of the returned guidance beyond 'step by step'.

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

Conciseness3/5

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

The core two clauses earn their place, but the trailing sentence about Gladly's REST API version and the absence of an official MCP server (with a checked-on date) is background trivia that does not help an agent decide or invoke. The key non-action disclosure also arrives after the imperative framing rather than being front-loaded.

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 zero-parameter, read-only guidance tool with no output schema, the description covers what it returns, that it has no side effects, and what tool family to use next. That is sufficient to call it correctly; only the exact return format is unspecified.

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?

The tool takes zero parameters, so there is no parameter meaning to add and the baseline of 4 applies. Nothing in the description misleads about inputs.

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

Purpose3/5

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

The opening sentence states a verb and resource ("Close conversation in Gladly"), but the actual behavior disclosed two clauses later is that the tool only returns step-by-step instructions and performs no action. That is a meaningful distinction an agent needs immediately, and burying it behind the imperative phrasing makes the purpose read as an action tool when it is a guide tool.

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?

It gives a clear workflow ('...then do it with the cotlet_browser_* tools in the user's Gladly Cloud Browser'), telling the agent this tool is a precursor to browser actions rather than the closer itself. It does not name a specific alternative such as cotlet_browser_close or state exclusions, so it stops short of a full when-not statement.

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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