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sktime

sktime-mcp

Official
by sktime

query_registry

Query sktime's registry to discover estimators, metrics, and capability tags. Filter by task, tags, or name to find specific tools.

Instructions

Discover sktime estimators, metrics, or capability tags. Common tags you can filter estimators by: 'capability:pred_int' (bool) - prediction intervals, 'capability:multivariate' (bool) - multivariate support, 'handles-missing-data' (bool) - NaN handling, 'scitype:y' (str) - target type ('univariate'/'multivariate'/'both'), 'requires-fh-in-fit' (bool) - needs forecast horizon at fit time. Set task='tag' (or 'tags') to query the full list of capability tags.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoFilter by capability tags, e.g. {'capability:pred_int': true}. Ignored if task='tag'.
taskNoFilter by scitype: forecaster, classifier, regressor, transformer, clusterer, detector, splitter, metric, param_est, aligner, network. Set to 'tag' or 'tags' to retrieve capability tags.
limitNoMaximum results (default: 50). Ignored if task='tag'.
queryNoSearch by name or description (substring, case-insensitive). Can be combined with task and tags filters.
offsetNoSkip this many results for pagination (default: 0). Ignored if task='tag'.
Behavior3/5

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

No annotations provided, so description carries behavioral disclosure. It describes query behavior and parameter interactions (e.g., 'Ignored if task='tag''), but does not explicitly state read-only nature or any side effects. Basic disclosure is present.

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?

Description is two sentences, front-loaded with the main action. The second sentence is slightly lengthy but still concise. Every part adds meaningful information.

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?

Given 5 parameters and no output schema, the description covers the main behaviors and special cases (task='tag', ignored params). It could mention output format or pagination details, but is sufficiently complete for a query tool.

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 description adds value by explaining common tags and the special behavior of task='tag'. It gives examples for the tags parameter, enhancing understanding 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?

Description clearly states the tool 'Discovers sktime estimators, metrics, or capability tags'. It uses a specific verb and resource, and distinguishes from siblings like describe_component or evaluate_estimator.

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?

Description explains when to use (to discover estimators/metrics/tags) and mentions the special task='tag' to get full tag list. However, it does not explicitly say when not to use or mention alternatives like describe_component for details.

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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