portkey-admin-mcp

get_error_rate_analytics

Read-onlyIdempotent

Fetch error-rate time-series data with summary error rate percent and per-bucket percentages of total requests to assess reliability and SLA trends.

Instructions

Get error-rate time-series data with summary.error_rate_percent and per-bucket percentages of total requests. Use this for reliability and SLA trends; use get_error_analytics for absolute error counts instead. Enterprise-gated. Returns 403 on non-Enterprise Portkey plans.

Input Schema

TableJSON Schema

Name	Required	Description
`configs`	No	Legacy Portkey query param for config slugs. Comma-separated string; prefer config_slugs for structured inputs.
`span_id`	No	Legacy Portkey query param for span IDs. Comma-separated string; prefer span_ids for structured inputs.
`cost_max`	No	Maximum cost in cents to filter by
`cost_min`	No	Minimum cost in cents to filter by
`metadata`	No	Legacy Portkey query param for metadata filtering. Stringified JSON object, e.g. '{"env":"prod","app":"myapp"}'; prefer metadata_filter for structured inputs.
`span_ids`	No	Structured alias for span_id. Use an array of span IDs; normalized to the legacy comma-separated Portkey query param.
`trace_id`	No	Legacy Portkey query param for trace IDs. Comma-separated string; prefer trace_ids for structured inputs.
`trace_ids`	No	Structured alias for trace_id. Use an array of trace IDs; normalized to the legacy comma-separated Portkey query param.
`api_key_ids`	No	Legacy Portkey query param for API key UUIDs. Comma-separated string; request_analytics also accepts an array and normalizes it to this form.
`prompt_slug`	No	Filter by prompt slug
`status_code`	No	Legacy Portkey query param for HTTP status codes. Comma-separated string; prefer status_codes for structured inputs.
`ai_org_model`	No	Legacy 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.
`config_slugs`	No	Structured alias for configs. Use an array of config slugs; normalized to the legacy comma-separated Portkey query param.
`status_codes`	No	Structured alias for status_code. Use an array of HTTP status codes; normalized to the legacy comma-separated Portkey query param.
`virtual_keys`	No	Legacy Portkey query param for virtual key slugs. Comma-separated string; prefer virtual_key_slugs for structured inputs.
`workspace_slug`	No	Filter by specific workspace
`metadata_filter`	No	Structured alias for metadata. Use an object such as { env: 'prod' }; normalized to a JSON string before the request is sent.
`provider_models`	No	Structured alias for ai_org_model. Use provider__model strings in an array; normalized to the legacy comma-separated Portkey query param.
`total_units_max`	No	Maximum number of total tokens to filter by
`total_units_min`	No	Minimum number of total tokens to filter by
`prompt_token_max`	No	Maximum number of prompt tokens
`prompt_token_min`	No	Minimum number of prompt tokens
`virtual_key_slugs`	No	Structured alias for virtual_keys. Use an array of virtual key slugs; normalized to the legacy comma-separated Portkey query param.
`completion_token_max`	No	Maximum number of completion tokens
`completion_token_min`	No	Minimum number of completion tokens
`weighted_feedback_max`	No	Maximum weighted feedback score (-10 to 10)
`weighted_feedback_min`	No	Minimum weighted feedback score (-10 to 10)
`time_of_generation_max`	Yes	End time for the analytics period (ISO8601 format, e.g., '2024-02-01T00:00:00Z')
`time_of_generation_min`	Yes	Start time for the analytics period (ISO8601 format, e.g., '2024-01-01T00:00:00Z')

Output Schema

TableJSON Schema

Name	Required	Description
`ok`	Yes	Whether the tool call succeeded and returned structured data
`data`	No	Structured success payload when ok is true
`error`	No	Structured error payload when ok is false

Schema Changelog

Changes observed during successful MCP inspections.

Changed16 schema fields changedv0.11.5Sep 8, 2026
- removedInput schema / properties / completion_token_max / exclusiveMinimum
```
Removed value: -0
```
- addedInput schema / properties / completion_token_max / minimum
```
Added value: +0
```
- removedInput schema / properties / completion_token_min / exclusiveMinimum
```
Removed value: -0
```
- addedInput schema / properties / completion_token_min / minimum
```
Added value: +0
```
- removedInput schema / properties / cost_max / exclusiveMinimum
```
Removed value: -0
```
- addedInput schema / properties / cost_max / minimum
```
Added value: +0
```
- removedInput schema / properties / cost_min / exclusiveMinimum
```
Removed value: -0
```
- addedInput schema / properties / cost_min / minimum
```
Added value: +0
```
- removedInput schema / properties / prompt_token_max / exclusiveMinimum
```
Removed value: -0
```
- addedInput schema / properties / prompt_token_max / minimum
```
Added value: +0
```
- removedInput schema / properties / prompt_token_min / exclusiveMinimum
```
Removed value: -0
```
- addedInput schema / properties / prompt_token_min / minimum
```
Added value: +0
```
- removedInput schema / properties / total_units_max / exclusiveMinimum
```
Removed value: -0
```
- addedInput schema / properties / total_units_max / minimum
```
Added value: +0
```
- removedInput schema / properties / total_units_min / exclusiveMinimum
```
Removed value: -0
```
- addedInput schema / properties / total_units_min / minimum
```
Added value: +0
```

Changed1 schema field changedv1.0.2Jul 4, 2026

changedInput schema / required

Previous value: -[
-  "time_of_generation_min",
-  "time_of_generation_max",
-  "api_key_ids"
-]New value: +[
+  "time_of_generation_min",
+  "time_of_generation_max"
+]

Addedv1.0.1May 7, 2026
RemovedMay 1, 2026
First observedJan 1, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already establish read-only/idempotent/non-destructive traits, so the description's added value is the Enterprise entitlement behavior (403 on non-Enterprise plans) and the exact response semantics (time-series with summary.error_rate_percent and per-bucket shares). This is meaningful behavioral context beyond the annotations, with no contradiction.

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?

Three sentences, each earning its place: one defines the deliverable, one gives usage direction vs an alternative, and one flags the enterprise restriction. The most decision-relevant information is front-loaded with no filler.

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 a rich 29-parameter schema that is 100% documented and an output schema present, the description still covers the core differentiator, the sibling routing, and the critical error condition. Nothing necessary for selecting or invoking the tool 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 description coverage is 100%, and every one of the 29 parameters already carries a meaningful description. The tool description adds global context about the output being error-rate percentages but no parameter-specific detail, so the baseline 3 for full schema coverage 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 opens with a specific verb and resource ('Get error-rate time-series data') and goes further by naming the return shape (summary.error_rate_percent, per-bucket percentages). It also names the sibling tool get_error_analytics and contrasts against it, so an agent can disambiguate without opening any schema.

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?

It explicitly says when to use this tool ('reliability and SLA trends') and when not to ('use get_error_analytics for absolute error counts instead'). It also declares the Enterprise-gating precondition and the 403 failure on non-Enterprise plans, leaving no ambiguity about eligibility.

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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