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Kong

Kong Konnect MCP Server

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by Kong
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TypeScript
Remote

get_consumer_requests

Retrieve and analyze API requests made by a specific consumer, with options to filter by time range, success status, and limit results.

Instructions

Retrieve and analyze API requests made by a specific consumer.

INPUT:

  • consumerId: String - ID of the consumer to analyze. The format of this field must be "controlPlaneID:consumerId".

  • timeRange: String - Time range for data retrieval (15M, 1H, 6H, 12H, 24H, 7D)

  • successOnly: Boolean - Filter to only show successful (2xx) requests (default: false)

  • failureOnly: Boolean - Filter to only show failed (non-2xx) requests (default: false)

  • maxResults: Number - Maximum number of results to return (1-1000)

OUTPUT:

  • metadata: Object - Contains consumerId, totalRequests, timeRange, and filters

  • statistics: Object - Usage statistics including:

    • averageLatencyMs: Number - Average response time in milliseconds

    • successRate: Number - Percentage of successful requests

    • statusCodeDistribution: Array - Breakdown of requests by status code

    • serviceDistribution: Array - Breakdown of requests by service

  • requests: Array - List of requests with details for each request

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
timeRangeNoTime range for data retrieval (15M = 15 minutes, 1H = 1 hour, etc.)1H
consumerIdYesConsumer ID to filter by (obtainable from analyze-failed-requests or query-api-requests tools)
maxResultsNoNumber of items to return per page
failureOnlyNoShow only failed (non-2xx) requests
successOnlyNoShow only successful (2xx) requests

Tool Definition Quality

A3.9/5.0
Behavior3/5

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

Given no annotations, the description carries full burden. It details input and output but omits safety aspects (e.g., read-only nature, error handling, 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?

Well-structured with INPUT/OUTPUT sections, but could be more concise; some parameter descriptions are repeated between schema and description.

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?

Covers all 5 parameters and output structure adequately for a complex analytics tool, though no output schema provided.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds format constraint for consumerId and default values, going beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves and analyzes API requests for a specific consumer, distinguishing it from siblings like query_api_requests or list_consumers.

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?

While it mentions consumerId can be obtained from other tools, it lacks explicit guidance on when to use this tool versus siblings like query_api_requests.

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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