data-transformer
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| ADT_WORKSPACE_ROOT | No | Absolute path to a granted workspace root, used as a compatibility grant when the MCP host does not support the roots protocol. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| data_inspectA | Inspect JSON, JSONL, CSV, TSV, YAML, or Parquet shape, types, counts, and a small sample without returning the full payload. Use for 'what fields are in this data?' or unknown tool output. Optionally compare record fields with target_schema and return deterministic mapping candidates; a draft plan is returned only after explicit mappings are supplied. One successful call is sufficient for its recorded observations; never repeat the same arguments to confirm it. |
| data_transformA | Transform or rewrite records: reshape, filter, join, aggregate, cast, flatten, or convert structured data with Transformation Plan v1. Do not use this tool for a validation-only request such as checking non-null or unique fields; use data_validate. Known transformation shape: {"version":"1","sources":{"input":{"path":"users.json","select":"data.users[*]"}},"steps":[{"id":"filtered","op":"filter","source":"input","where":{"field":"age","gte":18}},{"op":"select","source":"filtered","fields":[{"field":"userId","as":"id"}]}],"return":{"mode":"auto"}}. Omit workspace with ADT_WORKSPACE_ROOT; otherwise it is an exact granted root name, never a path. Returns a compact sample and explicit execution effects; large results require output.path. |
| data_validateA | Validate or check requirements on existing structured data (校验/检查非空、唯一、类型、字段或行数); choose data_validate, not data_transform, for validation-only requests. It accepts JSON Schema and deterministic assertions and returns valid true or false without rewriting the source. Known validation shape: {"source":{"path":"users.json","select":"data.users[*]"},"assertions":[{"type":"not_null","field":"userId"},{"type":"unique","field":"userId"}]}. |
| data_diffA | Compare two structured datasets by schema and rows, optionally using stable key fields. Returns compact added, removed, and changed counts and samples. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool occupies a cleanly separated role: observe (inspect), modify (transform), check (validate), and compare (diff). The overlap risk between transform and validate is explicitly addressed with cross-references steering agents to the right tool. No ambiguity remains after reading the descriptions.
All tools follow the exact data_<verb> pattern in snake_case with uniform imperative verbs: transform, inspect, validate, diff. The convention is fully predictable with zero deviation.
At 4 tools, the surface is tightly scoped with no bloat, comfortably within the ideal range for a focused data utility. Every tool earns its place within the read/validate/write/compare lifecycle.
Core workflows are fully covered: reading, transforming/writing output, validating, and diffing data, with output paths already handled by the transform tool. Minor gaps like an explicit export/delete or data source listing tool prevent a perfect score, but agents can accomplish all primary tasks.