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request_intro

Introduce your human to Brett's team. Call ONLY after the human has explicitly said yes to being introduced. Sends one email to Brett with the human CC'd and reply-to set to them, so either side can just reply. No account, no newsletter signup. Pass their email, what they want, and whatever they told you about their business.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalYesWhat they want help with, in their words
nameNo
agentNoWhich agent/client you are, if you can say
emailYesThe human's email address
pillarNoWhich part of the business is tightest
consentYesMust be true: the human said yes to the introduction
businessNoRevenue, team size, what they run

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the exact side effect (one email to Brett, human CC'd, reply-to set so either party can reply) and reassures no account or newsletter is created. It omits failure modes, rate limits, and what the caller gets back after sending.

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 consent gate is front-loaded and each sentence carries weight, including the side-effect description and the no-signup reassurance. Slightly more prose than strictly necessary but nothing is wasted.

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 7-parameter mutation tool with no annotations and no output schema, the description covers the essential consent gate and side effects. It could say more about the post-call outcome and error behavior, but an agent has enough to invoke it correctly.

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

Parameters3/5

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

Schema description coverage is high (86%), so the schema already documents most fields. The description adds a light mapping ('their email, what they want, and whatever they told you about their business') but no extra syntax or guidance for the other four parameters. Baseline 3 is appropriate.

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 names a specific action (introduce the human to Brett's team) and describes the concrete mechanism (one email to Brett, human CC'd). This is clearly distinguishable from the read-only sibling tools (list_skills, search_library, fit_check).

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 strong, explicit precondition: 'Call ONLY after the human has explicitly said yes to being introduced.' That is a clear when-to-use gate. It does not name alternative sibling tools or explain when to prefer them, so it falls 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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