Skip to main content
Glama
bcharleson

Instantly MCP Server

get_warmup_analytics

Analyze email warmup performance for accounts by retrieving metrics on deliverability and engagement over specified date ranges.

Instructions

Get warmup analytics for one or more accounts. API REQUIREMENT: The Instantly API expects an array of email addresses, even for a single account.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailsYesArray of email addresses to get warmup analytics for (1-100 emails). Use email addresses from list_accounts.
end_dateNoEnd date (YYYY-MM-DD) - optional
start_dateNoStart date (YYYY-MM-DD) - optional

Implementation Reference

  • Core execution logic for get_warmup_analytics tool. Normalizes single email or array input, builds POST request body, calls Instantly API /accounts/warmup-analytics endpoint, and returns formatted MCP response.
    case 'get_warmup_analytics': {
      // Handle both single email and emails array for user convenience
      let emailsArray: string[] = [];
    
      if (args?.emails && Array.isArray(args.emails)) {
        emailsArray = args.emails;
      } else if (args?.email && typeof args.email === 'string') {
        emailsArray = [args.email];
      } else {
        throw new McpError(ErrorCode.InvalidParams, 'Either "email" (string) or "emails" (array) is required');
      }
    
      // Validate email array
      if (emailsArray.length === 0) {
        throw new McpError(ErrorCode.InvalidParams, 'At least one email address is required');
      }
    
      console.error(`[Instantly MCP] get_warmup_analytics for emails: ${JSON.stringify(emailsArray)}`);
    
      // Use POST method with JSON body as per official API documentation
      const requestBody: any = { emails: emailsArray };
    
      // Add optional date parameters to the body if provided
      if (args?.start_date) requestBody.start_date = args.start_date;
      if (args?.end_date) requestBody.end_date = args.end_date;
    
      console.error(`[Instantly MCP] POST body: ${JSON.stringify(requestBody, null, 2)}`);
    
      const result = await makeInstantlyRequest('/accounts/warmup-analytics', {
        method: 'POST',
        body: requestBody
      }, apiKey);
    
      return createMCPResponse(result);
    }
  • MCP tool registration definition including name, description, annotations, and inputSchema for get_warmup_analytics.
    {
      name: 'get_warmup_analytics',
      title: 'Warmup Analytics',
      description: 'Get warmup metrics for account(s)',
      annotations: { readOnlyHint: true },
      inputSchema: {
        type: 'object',
        properties: {
          emails: { type: 'array', items: { type: 'string' }, description: 'Account emails' },
          email: { type: 'string', description: 'Single email (alternative)' },
          start_date: { type: 'string', description: 'YYYY-MM-DD' },
          end_date: { type: 'string', description: 'YYYY-MM-DD' }
        }
      }
    },
  • Zod TypeScript schema (GetWarmupAnalyticsSchema) defining input validation rules for emails array (1-100), optional date ranges using shared EmailSchema and DateFormatSchema.
    /**
     * Warmup analytics validation schema
     */
    export const GetWarmupAnalyticsSchema = z.object({
      emails: z.array(EmailSchema)
        .min(1, { message: 'At least one email address is required' })
        .max(100, { message: 'Cannot specify more than 100 email addresses' }),
      start_date: DateFormatSchema.optional(),
      end_date: DateFormatSchema.optional()
    });
  • Helper validation function that applies GetWarmupAnalyticsSchema to input args via generic validateWithSchema utility, throwing McpError on failure.
    export function validateWarmupAnalyticsData(args: unknown): z.infer<typeof GetWarmupAnalyticsSchema> {
      return validateWithSchema(GetWarmupAnalyticsSchema, args, 'get_warmup_analytics');
  • Registration of the tool's validator in the central TOOL_VALIDATORS mapping object, enabling dynamic validation via validateToolParameters(toolName, args). Note: not directly called in handler but available system-wide.
    'get_warmup_analytics': validateWarmupAnalyticsData,
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the API requirement about array format, which is useful, but fails to describe key behaviors: whether this is a read-only operation, what the return format looks like, if there are rate limits, or authentication requirements. For a tool with 3 parameters and no annotations, this is insufficient.

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 concise with two sentences, front-loading the core purpose. The API requirement is relevant but could be integrated more smoothly. No wasted words, though it could be slightly more structured.

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?

For a tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It lacks information about return values, error conditions, behavioral constraints, and how it differs from sibling tools. The API requirement note is helpful but doesn't compensate for these gaps.

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 fully documents all parameters. The description adds the API requirement about using an array even for single emails, which provides some additional context beyond the schema, but doesn't explain parameter interactions or semantics like date range effects. Baseline 3 is appropriate when schema does the heavy lifting.

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 clearly states the verb ('Get') and resource ('warmup analytics for one or more accounts'), making the purpose specific and understandable. However, it doesn't explicitly differentiate this tool from sibling analytics tools like 'get_campaign_analytics' or 'get_account_details', which would be needed for a perfect score.

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 provides no guidance on when to use this tool versus alternatives like 'get_campaign_analytics' or 'get_account_details', nor does it mention prerequisites (e.g., that accounts must be warmed up). The API requirement note is technical rather than usage guidance.

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/bcharleson/Instantly-MCP'

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