JobDiva MCP Server and API by Cotlet

Add candidate note in JobDiva

jobdiva_add_candidate_note

Read-onlyIdempotent

Add candidate note in the user's own JobDiva account from ChatGPT, Claude or any AI agent. Returns how to do it in JobDiva step by step (screens, buttons, fields) and opens nothing by itself; then do it with the cotlet_browser_* tools in the user's JobDiva Cloud Browser. JobDiva's official API: JobDiva has a REST API with public Swagger docs, 759 paths across two versions, but it only works with a client ID that JobDiva provides, and JobDiva has no official MCP server (checked 23 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": "jobdiva_add_candidate_note"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 9, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and that no write occurs, and the description reinforces this with 'opens nothing by itself', plus it discloses the return shape (screens, buttons, fields). The REST API/Swagger context explains why browser automation is the fallback, which is genuinely useful context an agent would not get from annotations alone.

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 key point (returns instructions, does not act) is stated early, but the opening 'from ChatGPT, Claude or any AI agent' is padding and the closing Swagger-docs sentence is verbose justification rather than operational information. It is serviceable but not 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?

For a zero-parameter instruction tool with no output schema, the definition covers what it returns, that it performs no action, and which tools carry out the follow-up. It is essentially complete, with only the sibling-routing detail missing.

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 the baseline is 4. The description adds nothing parameter-related because there is nothing to add, and no compensation is needed.

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 states the resource (candidate note in JobDiva) and clarifies the actual operation: it returns step-by-step instructions rather than performing the write. That correction of the misleading name is valuable, though the opening line 'Add candidate note...' still frames it as the action itself, which blurs the primary purpose slightly.

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?

It gives a workflow cue: get instructions here, then execute via cotlet_browser_* tools. It does not say when to prefer this over alternatives such as jobdiva_create_candidate or direct API paths, nor does it state prerequisites like an open JobDiva session; usage is implied 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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