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Lever MCP Server and API by Cotlet

Email candidate in Lever

lever_email_candidate
Read-onlyIdempotent

Email candidate in the user's own Lever account from ChatGPT, Claude or any AI agent. Returns how to do it in Lever step by step (screens, buttons, fields) and opens nothing by itself; then do it with the cotlet_browser_* tools in the user's Lever Cloud Browser. Lever's official API: Yes.

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": "lever_email_candidate"
  }
}

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 flag readOnlyHint=true, destructiveHint=false, idempotentHint=true; the description reinforces this with "opens nothing by itself," which matters because the underlying action is a write operation in Lever. It also discloses the return shape (screens, buttons, fields) and the execution vehicle (Lever Cloud Browser), though the trailing "Lever's official API: Yes" is ambiguous and adds little.

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 key constraint (returns guidance, opens nothing) is front-loaded in the second sentence, and the follow-up action is stated immediately. The sentence is dense with parentheticals and the dangling "Lever's official API: Yes" fragment is wasted space.

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, what it does not do, and how to proceed afterward. Only the vague API remark is unresolved.

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 nothing for the description to disambiguate; the baseline applies. The description correctly implies the candidate and content must be supplied later through the browser tools.

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 ("Email candidate in the user's own Lever account") and clarifies a non-obvious aspect of the tool's role: it returns instructions rather than performing the action. It doesn't explicitly differentiate itself from siblings such as lever_add_note, but the purpose is unambiguous.

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: call this to get step-by-step instructions, then execute them with the cotlet_browser_* tools in the Lever Cloud Browser. It does not state when-not-to-use or an alternative path, which keeps it just short of a 5.

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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