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get_cost_analytics

Read-onlyIdempotent

Analyze cost time-series data for spend analysis and spike detection. Returns summary total cost, average cost per request, and per-bucket costs.

Instructions

Get cost time-series data with summary.total_cost, summary.average_cost_per_request, and per-bucket total/avg cost. Use this for spend analysis and spike detection; use get_token_analytics when you need token volume instead of monetary cost. Enterprise-gated. Returns 403 on non-Enterprise Portkey plans.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
time_of_generation_minYesStart time for the analytics period (ISO8601 format, e.g., '2024-01-01T00:00:00Z')
time_of_generation_maxYesEnd time for the analytics period (ISO8601 format, e.g., '2024-02-01T00:00:00Z')
total_units_minNoMinimum number of total tokens to filter by
total_units_maxNoMaximum number of total tokens to filter by
cost_minNoMinimum cost in cents to filter by
cost_maxNoMaximum cost in cents to filter by
prompt_token_minNoMinimum number of prompt tokens
prompt_token_maxNoMaximum number of prompt tokens
completion_token_minNoMinimum number of completion tokens
completion_token_maxNoMaximum number of completion tokens
status_codeNoLegacy Portkey query param for HTTP status codes. Comma-separated string; prefer status_codes for structured inputs.
weighted_feedback_minNoMinimum weighted feedback score (-10 to 10)
weighted_feedback_maxNoMaximum weighted feedback score (-10 to 10)
virtual_keysNoLegacy Portkey query param for virtual key slugs. Comma-separated string; prefer virtual_key_slugs for structured inputs.
configsNoLegacy Portkey query param for config slugs. Comma-separated string; prefer config_slugs for structured inputs.
status_codesNoStructured alias for status_code. Use an array of HTTP status codes; normalized to the legacy comma-separated Portkey query param.
virtual_key_slugsNoStructured alias for virtual_keys. Use an array of virtual key slugs; normalized to the legacy comma-separated Portkey query param.
config_slugsNoStructured alias for configs. Use an array of config slugs; normalized to the legacy comma-separated Portkey query param.
workspace_slugNoFilter by specific workspace
api_key_idsYesLegacy Portkey query param for API key UUIDs. Comma-separated string; request_analytics also accepts an array and normalizes it to this form.
metadataNoLegacy Portkey query param for metadata filtering. Stringified JSON object, e.g. '{"env":"prod","app":"myapp"}'; prefer metadata_filter for structured inputs.
ai_org_modelNoLegacy Portkey query param for provider/model pairs. Format: 'provider__model' with double underscore, e.g. 'openai__gpt-4' or 'anthropic__claude-3-opus'. Comma-separated string; prefer provider_models for structured inputs.
provider_modelsNoStructured alias for ai_org_model. Use provider__model strings in an array; normalized to the legacy comma-separated Portkey query param.
trace_idNoLegacy Portkey query param for trace IDs. Comma-separated string; prefer trace_ids for structured inputs.
trace_idsNoStructured alias for trace_id. Use an array of trace IDs; normalized to the legacy comma-separated Portkey query param.
span_idNoLegacy Portkey query param for span IDs. Comma-separated string; prefer span_ids for structured inputs.
span_idsNoStructured alias for span_id. Use an array of span IDs; normalized to the legacy comma-separated Portkey query param.
metadata_filterNoStructured alias for metadata. Use an object such as { env: 'prod' }; normalized to a JSON string before the request is sent.
prompt_slugNoFilter by prompt slug

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYesWhether the tool call succeeded and returned structured data
dataNoStructured success payload when ok is true
errorNoStructured error payload when ok is false

Implementation Reference

  • The MCP tool handler (callback) for 'get_cost_analytics'. It calls service.analytics.getCostAnalytics, maps the response data points to {timestamp, total_cost, average_cost}, then formats and returns the result via formatGraphAnalytics.
    server.tool(
    	"get_cost_analytics",
    	"Get cost time-series data with summary.total_cost, summary.average_cost_per_request, and per-bucket total/avg cost. Use this for spend analysis and spike detection; use get_token_analytics when you need token volume instead of monetary cost.",
    	baseAnalyticsSchema,
    	async (params) => {
    		const analytics = await service.analytics.getCostAnalytics(
    			normalizeAnalyticsParams(params as Record<string, unknown>),
    		);
    		const dataPoints = analytics.data_points.map((point) => ({
    			timestamp: point.timestamp,
    			total_cost: point.total,
    			average_cost: point.avg,
    		}));
    		return {
    			content: [
    				{
    					type: "text",
    					text: JSON.stringify(
    						formatGraphAnalytics(
    							{
    								total_cost: analytics.summary.total,
    								average_cost_per_request: analytics.summary.avg,
    							},
    							dataPoints,
    						),
    						null,
    						2,
    					),
    				},
    			],
    		};
    	},
  • The formatGraphAnalytics helper function used by the handler to structure the response with summary, point_count, and data_points.
    function formatGraphAnalytics(
    	summary: Record<string, unknown>,
    	dataPoints: Record<string, unknown>[],
    ): {
    	summary: Record<string, unknown>;
    	point_count: number;
    	data_points: Record<string, unknown>[];
    } {
    	return {
    		summary,
    		point_count: dataPoints.length,
    		data_points: dataPoints,
    	};
    }
  • The actual service method getCostAnalytics that makes the HTTP GET request to '/analytics/graphs/cost' with the built analytics parameters.
    async getCostAnalytics(
    	params: CostAnalyticsParams,
    ): Promise<CostAnalyticsResponse> {
    	return this.get<CostAnalyticsResponse>(
    		"/analytics/graphs/cost",
    		this.buildAnalyticsParams(params),
    	);
    }
  • Type definitions for CostAnalyticsParams, CostAnalyticsResponse, CostDataPoint, and CostSummary - the input/output shapes for the cost analytics endpoint.
    // ==================== Cost Analytics Types ====================
    
    export interface CostDataPoint {
    	timestamp: string;
    	total: number;
    	avg: number;
    }
    
    export interface CostSummary {
    	total: number;
    	avg: number;
    }
    
    export interface CostAnalyticsResponse {
    	object: "analytics-graph";
    	data_points: CostDataPoint[];
    	summary: CostSummary;
    }
    
    export interface CostAnalyticsParams extends BaseAnalyticsParams {}
  • The tool registration pipeline: registerAnalyticsTools is called from index.ts as part of the analytics domain (line 37). The tool name 'get_cost_analytics' is also listed in ENTERPRISE_GATED_TOOL_NAMES (line 111) for access control.
    const TOOL_DOMAIN_REGISTRARS = [
    	["users", registerUsersTools],
    	["workspaces", registerWorkspacesTools],
    	["configs", registerConfigsTools],
    	["keys", registerKeysTools],
    	["collections", registerCollectionsTools],
    	["prompts", registerPromptsTools],
    	["analytics", registerAnalyticsTools],
    	["guardrails", registerGuardrailsTools],
    	["limits", registerLimitsTools],
Behavior5/5

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

Annotations already indicate read-only, non-destructive, idempotent, open-world. Description adds enterprise-gating (returns 403 on non-Enterprise plans), which is valuable behavioral context beyond annotations.

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 sentences, front-loaded with purpose and key fields. Every sentence adds value: purpose, alternative tool, access restriction. No fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the high parameter count (29) and presence of output schema, the description sufficiently covers the tool's purpose, return fields, use case, and error behavior. No critical gaps.

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?

Input schema has 100% description coverage for all 29 parameters. The description does not add parameter-specific meaning beyond what the schema provides, so baseline 3 is appropriate.

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 cost time-series data, listing specific fields (summary.total_cost, summary.average_cost_per_request, per-bucket total/avg cost). It differentiates from get_token_analytics by monetary vs. token focus.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly recommends use for spend analysis/spike detection and provides an alternative tool (get_token_analytics) for token volume needs. This is clear when-to and when-not guidance.

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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