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List recorder statistic IDs

statistics_list_statistic_ids
Read-onlyIdempotent

Lists recorder statistic IDs by type (sum or mean) to discover entities with long-term statistics before fetching detailed statistics or metadata.

Instructions

List recorder statistic IDs of the requested type.

Calls WS recorder/list_statistic_ids and returns one entry per statistic the recorder tracks, with its source and unit.

Use when: discovering which entities have long-term statistics before calling statistics_get_statistics or statistics_get_statistics_metadata. Not for: raw short-term state history — use history_get_state_history instead. Returns: list of {"statistic_id", "name", "source", "unit", "has_sum", "has_mean"} dicts. Errors: [{"error": "..."}] when statistic_type is not 'sum'/'mean', when the WS response isn't a list, or when the WS call raises.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
statistic_typeNoWhich kind of statistic to list: 'sum' for counters (energy, gas, water) or 'mean' for sensors (temperature, humidity).sum

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.25.1
    • addedInput schema / properties / statistic_type / description
      Added value: +"Which kind of statistic to list: 'sum' for counters (energy, gas, water) or 'mean' for sensors (temperature, humidity)."
  2. Addedv0.18.1

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world behavior, but the description adds meaningful context: the exact WS endpoint, the returned entry shape, and specific error conditions when statistic_type is invalid or the WS response is malformed. This gives the agent a richer behavioral picture than annotations alone.

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 front-loaded with the purpose, immediately followed by use/when-not guidance, then return shape and error behavior. Every sentence carries useful information without redundancy.

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?

Even though an output schema exists, the description coherently explains the result shape and failure modes, and it routes the agent to the right sibling tools. For a one-parameter read tool, nothing essential is missing.

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%, so the allowed values and meaning of statistic_type are already documented in the input schema. The description adds useful error semantics by stating that errors occur when statistic_type is not 'sum' or 'mean', which goes slightly 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 states a specific verb and resource: list recorder statistic IDs of the requested type. It also names the underlying WS call and clarifies that it returns one entry per tracked statistic with source and unit, which distinguishes it from related statistics and history tools.

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 it: discovering entities with long-term statistics before calling statistics_get_statistics or statistics_get_statistics_metadata. It also states when not to use it: raw short-term state history should use history_get_state_history instead.

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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