HeyLead

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setup_profile

Destructive

Connect your LinkedIn account and analyze your writing style to create a personalized voice for outreach messages. Required first step before using other HeyLead tools.

Instructions

Set up HeyLead by connecting your LinkedIn account and analyzing your writing style.

REQUIRED for first-time users — must be called before any other tool.

This analyzes your LinkedIn profile, posts, and writing style to create
a "voice signature" so every outreach message sounds like YOU, not a bot.
Handles LinkedIn automation setup, SDR onboarding, account connection,
and voice analysis for personalized outreach.

First-time setup: sign in at https://heylead.dev/auth/login-url, click
'Connect' on the LinkedIn row, copy the setup message ('Copy' under
'Get Started'), then call this tool with the eyJ... token from it as
backend_jwt. No API keys needed on the hosted backend.

Args:
    llm_api_key: Optional — only if you want to use your own AI key instead of the backend's.
    llm_provider: Which AI to use if providing your own key: "gemini", "claude", or "openai".
    backend_url: HeyLead Backend API URL. Leave empty — defaults to production server.
    backend_jwt: Your authentication token from HeyLead.

Input Schema

TableJSON Schema

Name	Required	Default
`backend_jwt`	No
`backend_url`	No
`llm_api_key`	No
`llm_provider`	No	gemini

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.10.375Sep 18, 2026

TDQS

A5/5.0

Behavior5/5

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

The description aligns with annotations (readOnlyHint=false, destructiveHint=true) and adds substantial behavioral context beyond them. It explains that the tool analyzes LinkedIn profile and writing style to create a 'voice signature,' and that it performs LinkedIn automation setup and SDR onboarding. It also discloses the authentication requirement via a backend_jwt token and clarifies that no API keys are needed unless providing a custom LLM key. This fully prepares the agent for the tool's side effects and dependencies.

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

Conciseness5/5

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

The description is well-organized and front-loaded. It opens with a one-sentence summary, then a bolded requirement, a short explanatory paragraph, a clear 'First-time setup:' section with steps, and an 'Args:' section with bullet points. Every sentence earns its place; no fluff or redundancy. The structure aids quick comprehension for an agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (external login, token acquisition, multiple configuration options), the description is remarkably complete. It covers prerequisites, exact steps, parameter semantics, and the tool's role in the broader system. The presence of an output schema (indicated by 'has output schema: true') means return-value documentation is not required in the description. Nothing an agent needs to invoke it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description carries the full burden, and it excels. Each parameter is explained: llm_api_key as optional with purpose, llm_provider with explicit allowed values, backend_url with a default behavior, and backend_jwt as the authentication token with instructions on how to obtain it. This is more than sufficient for an agent to fill the arguments correctly.

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?

The description states a clear specific purpose: 'Set up HeyLead by connecting your LinkedIn account and analyzing your writing style.' It further distinguishes this from siblings by detailing it handles 'LinkedIn automation setup, SDR onboarding, account connection, and voice analysis' and explicitly marks it as 'REQUIRED for first-time users — must be called before any other tool.' No ambiguity remains.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides explicit when-to-use guidance: 'REQUIRED for first-time users — must be called before any other tool.' It also gives a step-by-step walkthrough: sign in at a specific URL, connect LinkedIn, copy the setup message, and call the tool with the token. It notes 'No API keys needed on the hosted backend,' which preempts a common question. There is no need for alternative tools since this is a mandatory one-time setup.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.