Mach GTM MCP

draft_outreach

Drafts personalized cold outreach for a prospect using their buying signal and your proof points, structured as one hook, three value points, and one CTA.

Instructions

Draft a personalised cold outreach message (email or LinkedIn) for a specific prospect. Grounds the message in the prospect's signals and your proof points — not generic templates. Follows the Mach GTM 1-3-1 framework: one hook, three value points, one CTA.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prospect_name": "<prospect_name>",
      "prospect_title": "<prospect_title>",
      "company_name": "<company_name>",
      "buying_signal": "<buying_signal>",
      "your_offering": "<your_offering>"
    },
    "name": "draft_outreach"
  }
}

Input Schema

Name	Required	Description	Default
`channel`	No	Outreach channel. Defaults to email.	email
`proof_point`	No	A relevant case study or result (e.g. "helped Acme reduce CAC by 40%").
`company_name`	Yes	Prospect's company name.
`buying_signal`	Yes	A specific signal or context about this prospect (e.g. "posted about rising cloud costs", "just raised Series B", "hiring 10 SDRs").
`prospect_name`	Yes	First name of the prospect.
`your_offering`	Yes	What you sell and the core value proposition.
`prospect_title`	Yes	Job title (e.g. "CTO", "VP Sales").

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Nov 7, 2026

TDQS

B3.4/5.0

Behavior2/5

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

With no annotations, the description must carry the full behavioral burden, but it does not say whether the draft is returned as text, saved, or sent, nor does it describe output format, length, permissions, or side effects. It clarifies drafting versus generic templates and the 1-3-1 structure, but that is methodology rather than operational transparency.

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 three front-loaded sentences with no filler: purpose, grounding approach, and framework. The 1-3-1 framework is explained inline so the jargon does not obscure the instruction.

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

Completeness2/5

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

There are no annotations and no output schema, so the description should explain the return shape and side effects, but it only gives methodology. It also omits output format and length while saying email or LinkedIn even though the schema allows WhatsApp.

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 100%, so the schema already documents all seven parameters. The description reinforces use of buying_signal and proof_point but adds no parameter-level meaning beyond the schema, and it does not address the WhatsApp enum option or the optional proof_point.

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 uses a specific verb, 'Draft', and resource, 'cold outreach message', for a specific prospect, and its grounding in signals and proof points separates it from siblings like research_prospect and score_lead. The minor channel omission does not make the core purpose ambiguous.

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 establishes context: use this for personalised outreach to a specific prospect using signals and proof points. However, it does not name sibling alternatives such as research_prospect or build_sequence, nor does it state when not to use this tool.

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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