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Deputy MCP Server and API by Cotlet

Approve timesheet in Deputy

deputy_approve_timesheet
Read-onlyIdempotent

Approve timesheet in the user's own Deputy account from ChatGPT, Claude or any AI agent. Returns how to do it in Deputy step by step (screens, buttons, fields) and opens nothing by itself; then do it with the cotlet_browser_* tools in the user's Deputy Cloud Browser. Deputy's official API: Yes, Deputy has a full REST API, 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": "deputy_approve_timesheet"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds meaningful context beyond that: it returns step-by-step screens/buttons/fields and 'opens nothing by itself', which directly reinforces the read-only annotation. The note about Deputy's REST API and lack of an official MCP server is extra but does not explain return format depth or limits.

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?

The core behavior and the follow-up tool path are front-loaded in two sentences, with the API/MCP aside trailing at the end. That trailing note ('checked 20 Oct 2026') is somewhat verbose and low-value for an agent choosing a tool, but overall the text is tight.

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?

With no parameters and no output schema, the description's job is to explain what comes back and what to do next, which it does (step-by-step instructions, then browser tools). It is complete enough to call correctly, though it does not describe the shape/detail level of the returned guidance.

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?

There are zero parameters, so there is no parameter semantics to add and the baseline of 4 applies. Nothing in the description contradicts or obscures the empty 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?

The description names a specific verb and resource (approve timesheet) and, crucially, clarifies that the tool only returns step-by-step guidance rather than performing the approval. This corrective scope statement prevents the name from being read as a write action. It does not explicitly differentiate itself from the sibling approve/decline tools, which keeps it below a 5.

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 clear next-step routing: use the result and then act via the cotlet_browser_* tools in the user's Deputy Cloud Browser. It does not state when this tool is the wrong choice (e.g., versus deputy_list_timesheets to find a timesheet first), so the guidance is contextual rather than exhaustive.

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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