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Hybrid

get_feedback_models_analytics

Read-onlyIdempotent

Compare feedback volume and satisfaction across models using per-model time-series counts. Spot model-level trends to guide performance improvements.

Instructions

Get feedback time-series data grouped by model, with per-model counts over time. Use this to compare feedback volume and satisfaction across models; use get_feedback_analytics for the overall total instead. Enterprise-gated. Returns 403 on non-Enterprise Portkey plans.

Input Schema

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

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

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed16 schema fields changedv0.11.5
    • 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
  2. Changed1 schema field changedv1.0.2
    • 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"
+]
  3. Addedv1.0.1

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds meaningful context beyond those structured hints: the Enterprise gating and the concrete 403 behavior on non-Enterprise plans. It also clarifies the grouping and time-series output shape, which helps set expectations for what the tool actually returns.

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?

The description is three sentences with no fluff: core function, usage guidance with alternative, and an important error/access caveat. Every sentence earns its place, and critical information is front-loaded.

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 output schema exists and annotations cover the safety profile, the description provides the remaining essential context: what the data is grouped by, when to use this instead of the sibling, and the Enterprise restriction. Nothing needed for correct selection or invocation 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%, so the schema already documents all 29 parameters in detail. The description does not add parameter-level meaning beyond stating the model grouping and time-series nature, which is exactly the baseline situation where a score of 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 opens with a specific verb and resource: 'Get feedback time-series data grouped by model, with per-model counts over time.' It also states the analytical use case ('compare feedback volume and satisfaction across models') and explicitly differentiates this tool from get_feedback_analytics, so an agent can identify the right sibling without inspecting schemas.

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?

The description gives explicit usage context: 'Use this to compare feedback volume and satisfaction across models; use get_feedback_analytics for the overall total instead.' This tells the agent exactly when to choose this tool and when to pick the named alternative.

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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