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cognigy-ai-mcp-management-server

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TypeScript

get_call_metrics

Read-onlyIdempotent

Fetch monthly Cognigy.AI Voice Gateway call counter metrics for a project or the entire organization to analyze call volumes.

Instructions

Gets Cognigy.AI call counter metrics (Voice Gateway) for one month. Returns aggregated call counts for a project or the entire organization.

Input Schema

NameRequiredDescriptionDefault
yearNoYear to report on (e.g., 2026). Defaults to the current year.
monthNoMonth to report on (1-12). Defaults to the current month.
projectIdNoProject ID for project-level metrics. Omit for organization-wide metrics.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changedv0.2.1
    • removedInput schema / properties / endDate
      Removed value: -{
-  "description": "End date for metrics (ISO 8601 format)",
-  "type": "string"
-}
    • addedInput schema / properties / month
      Added value: +{
+  "description": "Month to report on (1-12). Defaults to the current month.",
+  "maximum": 12,
+  "minimum": 1,
+  "type": "integer"
+}
    • removedInput schema / properties / startDate
      Removed value: -{
-  "description": "Start date for metrics (ISO 8601 format)",
-  "type": "string"
-}
    • removedInput schema / properties / timezone
      Removed value: -{
-  "description": "Timezone for aggregation (e.g., 'UTC', 'America/New_York')",
-  "type": "string"
-}
    • addedInput schema / properties / year
      Added value: +{
+  "description": "Year to report on (e.g., 2026). Defaults to the current year.",
+  "maximum": 9007199254740991,
+  "minimum": -9007199254740991,
+  "type": "integer"
+}
  2. First observedv0.1.4

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is covered. The description adds genuine context beyond them — the one-month window constraint and that results are aggregated counts rather than raw records — but says nothing about pagination, granularity, or default behavior when the month is omitted.

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?

Two tight sentences with zero filler. The resource identity and window are front-loaded, and the second sentence immediately delivers the return semantics.

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?

With no output schema, the description partially compensates by stating the return is aggregated call counts. Combined with fully documented parameters and annotations covering safety, an agent has enough to call it correctly; only finer output detail (granularity, pagination) is missing.

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 coverage is 100%: year, month, and projectId each carry clear descriptions including defaults and the omit-for-org semantics. The description reinforces the month window and project/org scope but adds no syntax or format detail beyond what the schema already provides, so the baseline 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?

States a specific verb+resource (gets Cognigy.AI call counter metrics / Voice Gateway) with a clear temporal scope (one month) and output nature (aggregated call counts). It implicitly distinguishes itself from sibling metric tools like get_conversation_metrics and get_knowledge_query_metrics by naming the metric domain, though it never explicitly routes against them.

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?

Usage is only implied: 'for one month' and 'for a project or the entire organization' hint at when the tool applies, but there is no explicit when-to-use, when-not-to-use, or alternative-tool guidance. An agent must infer this is the call-count reporting tool rather than the conversation or knowledge-query metric tools.

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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