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cognigy-ai-mcp-management-server

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TypeScript

get_knowledge_query_metrics

Read-onlyIdempotent

Retrieve monthly Cognigy.AI Knowledge AI query counts, aggregated by project or organization, to track knowledge usage and support reporting.

Instructions

Gets Cognigy.AI Knowledge AI query counter metrics for one month. Returns aggregated knowledge query counts for a project or the entire organization.

Input Schema

TableJSON 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

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description usefully adds that results are aggregated counts scoped to a single month, but says nothing about rate limits, auth, or pagination behavior.

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?

Two tight sentences with the resource and temporal scope front-loaded and no filler. The second sentence partially overlaps with schema content, keeping it just below maximal efficiency.

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 does state the return content ('aggregated knowledge query counts'), which is the key missing piece. It is adequate for a simple read-only metrics tool, though it could say more about granularity of the aggregates.

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 year, month, and projectId (including the 'omit for organization-wide' semantics) are already fully documented. The description's mention of project-vs-organization scope merely restates the schema, adding no new syntax or default details.

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 ('Gets') and a precise resource ('Cognigy.AI Knowledge AI query counter metrics'), which is distinguishable from the metrics siblings like get_call_metrics and get_conversation_metrics. It does not explicitly name those siblings, so it falls just short of full differentiation.

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?

The description implies usage through the scope ('for one month', 'a project or the entire organization') but never states when to pick this over the call/conversation metric tools or any prerequisites. Usage is inferable rather than stated.

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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