gapify-mcp-server

Reports: Account Conversation Metrics

gapify_account_conversation_metrics

Read-onlyIdempotent

Retrieve conversation metrics for a specific account to analyze support reports and assess performance.

Instructions

Get conversation metrics for Account [Reports] GET /api/v2/accounts/{account_id}/reports/conversations Auth: User API key (header api-access-token). Text values may be written in Persian (Farsi) or any other language and are sent unchanged; IDs, numbers and phone numbers may use Persian or Arabic-Indic digits.

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": {
      "account_id": "<account_id>"
    },
    "name": "gapify_account_conversation_metrics"
  }
}

Input Schema

Name	Required	Description	Default
`type`	No	Query param. Type of report One of: `account` Example: account
`account_id`	Yes	The numeric ID of the account

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.1Oct 20, 2026

TDQS

B3.2/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so the safety profile is covered. The description usefully adds the auth mechanism (User API key via api-access-token header) and an encoding caveat about Persian/Arabic-Indic digits, but says nothing about return format, time-range scoping, or rate limits.

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 definition is short and front-loads the purpose, followed by endpoint, auth, and i18n notes. The mixed format (prose + endpoint + auth lines) is slightly disjointed but every line carries information.

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?

With no output schema, the description is the only place to learn what the metrics contain, yet it never characterizes the returned data or any time-range filtering. For a metrics/report endpoint that is a meaningful gap, though the auth and encoding notes partially compensate.

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%, with account_id and the type query param already documented in the schema. The description adds no syntax or format detail for either parameter, so the baseline of 3 applies.

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 states a clear verb and resource: 'Get conversation metrics for Account'. This is specific enough to distinguish it from the account/agent report siblings, though it does not name gapify_agent_conversation_metrics or gapify_get_account_reports to make the boundary explicit.

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?

There is no guidance on when to use this tool versus the closely-related gapify_agent_conversation_metrics, gapify_get_account_reports, or gapify_get_account_reports_summary. The agent must infer the selection from names alone.

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

Deploy Server