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transform

Preprocess numeric table features into a new table using operations like log, z-score, clip, rank, detrend, returns, and row normalization while keeping labels, IDs, and datetimes unchanged.

Instructions

Preprocess the numeric features of a table into a new table (labels, ids and datetimes carried over unchanged).

ops: log1p, log (params offset), sqrt, abs, clip (lo/hi or z), winsorize (q), rank, zscore (params clip), minmax, demean, detrend (params by), diff / pct_change / log_return / cumsum along rows (params periods, by=, group_by, dropna), row_normalize (params target; library-size normalization for counts), center_rows (subtract each row's mean: removes the equal-weight market / common mode of a panel), deseasonalize (params by=, period=month|dayofweek|hour|dayofyear|quarter: subtracts per-period means -> anomalies), residualize (params on=[cols]: regress every feature on those columns and keep residuals, e.g. remove a market factor), dropna. Typical chains: counts -> row_normalize -> log1p -> project(clip_z=10); prices -> log_return -> describe; monthly series -> deseasonalize -> project(pca, standardize=False).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "name": "<name>",
      "op": "<op>"
    },
    "name": "transform"
  }
}

Input Schema

Name	Required	Description	Default
`op`	Yes
`name`	Yes
`params`	No
`columns`	No
`new_name`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.1Nov 9, 2026

TDQS

B3.2/5.0

Behavior3/5

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

With no annotations, the description carries the behavioral burden and does disclose that the tool produces a new table, preserves labels/ids/datetimes, and applies specific transformations including dropna. It does not clarify whether the original table is ever modified, what permissions are needed, or what failure behavior looks like, though the output schema covers return values.

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?

The description is front-loaded with the core purpose and then uses a dense op catalog plus typical chains. It is long, but the length is mostly justified by the large number of operations; a tabular layout could improve readability, but there is little wasted wording.

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

Completeness3/5

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

For a transform tool with five parameters, many operations, no annotations, and an output schema, the description is rich on operation behavior and chains. It remains incomplete for correct invocation because core arguments like name, columns, and new_name are left unexplained, and the top-level parameter structure is ambiguous.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It enumerates op values and many nested op parameters such as offset, clip, by, periods, and group_by, but it never explains the required name parameter, the columns parameter, or new_name, and it does not clearly describe how top-level params relate to the nested params object.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: preprocess numeric features of a table into a new table, with labels, ids, and datetimes carried over unchanged. That is clear enough to distinguish it from non-transform sibling tools in practice, but it does not explicitly contrast itself with tensor_transform or other transform-adjacent tools.

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?

The op catalog and typical chains give implied usage contexts, such as counts, prices, and monthly series, and even show chaining into project. However, it does not explicitly say when to use this tool versus tensor_transform, project, or other siblings, nor does it state exclusions or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.