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cognigy-ai-mcp-management-server

cognigy-ai-mcp-management-server

by TsvetanG2
OverviewSchemaSourceRelated ServersDiscussions(1)Admin
TypeScript

get_conversation_metrics

Read-onlyIdempotent

Retrieve monthly Cognigy.AI conversation counter metrics for a project or the entire organization to track usage and support reporting.

Instructions

Gets Cognigy.AI conversation counter metrics for one month. Returns aggregated conversation 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, and destructiveHint=false, so the safety profile is covered. The description usefully adds that the output is aggregated conversation counts rather than raw records, but it omits any detail on aggregation semantics, latency, or how organization-wide results are scoped.

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 no filler; the resource and the month granularity are front-loaded, and the second sentence covers the scoping choice efficiently.

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?

For a simple read-only aggregation tool with no output schema, the description covers scope, granularity, and the nature of the result, and annotations carry the safety profile. Only modest gaps remain around what the aggregates contain and how organization-wide scope is resolved.

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 all three parameters (year, month, projectId) are already documented including defaults. The description adds only the 'one month' framing and the project-vs-organization distinction, which restates what the schema already conveys, 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 (Gets) and resource (Cognigy.AI conversation counter metrics) with a clear temporal scope (one month), which distinguishes it from sibling metrics tools like get_knowledge_query_metrics and get_call_metrics. It does not explicitly name or contrast those siblings, so it falls short of a 5.

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 through scope: the description notes that results are for a project or the entire organization, mapping to the optional projectId. There is no explicit when-to-use guidance, no condition for choosing between project- and org-level reporting, and no reference to alternative metrics 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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