Skip to main content
Glama
closermethod

SMB Sales Intelligence MCP

get_cold_email_template

Retrieve a cold email template for outbound sales, selecting from pattern interrupt, observation, mutual connection, case study, or breakup scenarios.

Instructions

Get a cold email template for outbound.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
template_typeYesThe type of cold email template

Implementation Reference

  • Input schema and tool registration metadata for 'get_cold_email_template'. Defines template_type enum: pattern_interrupt, observation, mutual_connection, case_study, breakup.
      name: "get_cold_email_template",
      description: "Get a cold email template for outbound.",
      inputSchema: {
        type: "object",
        properties: {
          template_type: {
            type: "string",
            enum: ["pattern_interrupt", "observation", "mutual_connection", "case_study", "breakup"],
            description: "The type of cold email template"
          }
        },
        required: ["template_type"]
      }
    },
  • src/main.ts:748-761 (registration)
    Tool registration within ListToolsRequestSchema handler. Declares 'get_cold_email_template' as an available tool with its description and input schema.
      name: "get_cold_email_template",
      description: "Get a cold email template for outbound.",
      inputSchema: {
        type: "object",
        properties: {
          template_type: {
            type: "string",
            enum: ["pattern_interrupt", "observation", "mutual_connection", "case_study", "breakup"],
            description: "The type of cold email template"
          }
        },
        required: ["template_type"]
      }
    },
  • The handler logic for 'get_cold_email_template' in the CallToolRequestSchema switch statement. Extracts template_type arg, looks up COLD_EMAIL_TEMPLATES, and returns the template with rules.
    case "get_cold_email_template": {
      const templateType = args?.template_type as string;
      const template = COLD_EMAIL_TEMPLATES[templateType as keyof typeof COLD_EMAIL_TEMPLATES];
      return {
        content: [{
          type: "text",
          text: JSON.stringify({
            module: "Cold Email Templates",
            rules: [
              "Keep subject lines under 5 words",
              "First line should be about THEM, not you",
              "End with a clear, easy-to-answer question",
              "Under 100 words total"
            ],
            template: template
          }, null, 2)
        }]
      };
    }
  • COLD_EMAIL_TEMPLATES constant — the data source containing all 5 cold email templates (pattern_interrupt, observation, mutual_connection, case_study, breakup) with name, subject, body, and psychology.
    const COLD_EMAIL_TEMPLATES = {
      pattern_interrupt: {
        name: "Pattern Interrupt",
        subject: "Quick question",
        body: `Hi [Name],
    
    Not sure if this is relevant for you, but I help [type of company] with [specific outcome].
    
    Is that something you're actively working on, or is this a 'not right now' situation?
    
    Either answer is fine — just don't want to waste your time.
    
    [Your name]`,
        psychology: "Permission-based. Respects their time. Easy to respond to."
      },
      observation: {
        name: "Observation-Based",
        subject: "[Something specific you noticed]",
        body: `Hi [Name],
    
    I noticed [specific observation about their company/product/content].
    
    [One sentence about why that matters or what it made you think]
    
    I help companies like yours with [outcome]. Worth a quick chat?
    
    [Your name]`,
        psychology: "Shows you did research. Personalization = higher response rate."
      },
      mutual_connection: {
        name: "Mutual Connection",
        subject: "[Mutual connection] suggested I reach out",
        body: `Hi [Name],
    
    [Mutual connection] mentioned you might be interested in [topic/outcome].
    
    I've helped [similar company] achieve [specific result]. Not sure if it's relevant for [their company], but figured it was worth a quick note.
    
    Open to a brief call?
    
    [Your name]`,
        psychology: "Social proof from someone they trust."
      },
      case_study: {
        name: "Case Study Lead",
        subject: "How [similar company] achieved [result]",
        body: `Hi [Name],
    
    Just wrapped up a project with [similar company] — helped them [specific result].
    
    Given what [their company] is doing with [specific thing], thought there might be a parallel.
    
    Worth a quick call to see if it's relevant?
    
    [Your name]`,
        psychology: "Leads with proof, makes them curious about the result."
      },
      breakup: {
        name: "Breakup Email",
        subject: "Closing the loop",
        body: `Hi [Name],
    
    I've reached out a few times about [topic] — haven't heard back, so I'll assume the timing isn't right.
    
    If [problem you solve] becomes a priority later, feel free to reach out. I'll be here.
    
    Good luck with [something specific to them].
    
    [Your name]`,
        psychology: "Respectful exit. Often triggers a response from people who meant to reply."
      }
    };
Behavior2/5

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

With no annotations, the description carries full burden for behavioral disclosure but only states the action. No mention of side effects, permissions, or read-only nature (though inferred from name). Minimal transparency.

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 description is a single concise sentence with no superfluous words. It is front-loaded and efficient, though could include more detail without becoming verbose.

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

Completeness3/5

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

For a simple tool with one parameter and no output schema, the description provides basic purpose. However, it does not explain what the returned template looks like or any return value context, which could be helpful but is not critical.

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?

The input schema provides a clear description for the single parameter, and enum values are self-explanatory. Schema coverage is 100%, so the description adds no additional meaning beyond the schema.

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 clearly states the tool retrieves a cold email template for outbound use. It is specific with verb and resource, and distinguishes from sibling tools like get_call_script or get_closing_script which target other communication materials.

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

Usage Guidelines2/5

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

The description lacks any guidance on when to use this tool versus alternatives. No explicit context, exclusions, or mention of appropriate scenarios beyond the implicit purpose.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/closermethod/smb-sales-intelligence-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server