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datalens-dev-mcp

by ADIKANT
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Python
Local

dl_dataset_validate

Read-onlyIdempotent

Validate dataset fields locally or against the provider before saving. Check schemas, refresh specific sources, and return a revision-guarded candidate without persisting or executing charts.

Instructions

Validate Dataset fields locally, or set provider=true to validate a saved Dataset through the provider without saving. Optional exact refresh_source_ids refresh source schemas without forcing field replacement. include_provider_state returns the full validation candidate for an explicit revision-guarded save; this is not a saved result or chart execution.

Input Schema

NameRequiredDescriptionDefault
fieldsNo
providerNo
dataset_idNo
refresh_source_idsNo
include_provider_stateNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.2.27
    • addedInput schema / properties / include_provider_state
      Added value: +{
+  "default": false,
+  "type": "boolean"
+}
    • addedInput schema / properties / provider
      Added value: +{
+  "default": false,
+  "type": "boolean"
+}
    • addedInput schema / properties / refresh_source_ids
      Added value: +{
+  "items": {
+    "minLength": 1,
+    "type": "string"
+  },
+  "maxItems": 100,
+  "minItems": 1,
+  "type": [
+    "array",
+    "null"
+  ],
+  "uniqueItems": true
+}
  2. Addedv1.2.0

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already establish the safety profile (readOnly, idempotent, non-destructive), and the description adds genuinely useful context: no save occurs, no field replacement is forced, and the output is not a saved result or chart execution. These negative-space clarifications reduce real ambiguity for a tool whose name and 'refresh_source_ids' wording could otherwise read as mutating.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The main purpose is front-loaded, but the remaining text is a dense run-on chain of clauses ('for an explicit revision-guarded save; this is not a saved result or chart execution') that is hard to parse and repeats the same no-side-effect idea. Information density is high but readability suffers.

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?

With 5 parameters, no output schema, and 0% schema coverage, the description should do more to explain the input contract (especially the dataset_id/fields anyOf) and what a successful validation returns. It explains the include_provider_state return, but leaves the core return shape and the required-input rule implicit.

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 0%, so the description must carry the parameter burden, and it only partially does: provider, refresh_source_ids, and include_provider_state get real semantics, but dataset_id and fields do not, and the anyOf requirement (dataset_id OR fields) is never surfaced. It compensates for some but not all of the gap.

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?

States a specific verb+resource ('Validate Dataset fields') and distinguishes the two operating modes (local validation vs. provider=true validation of a saved Dataset). It does not name or differentiate itself from the sibling dl_editor_validate, so an agent must infer the boundary, keeping this short of a 5.

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 description implies when provider mode is appropriate ('through the provider without saving') and hints at the revision-guarded save workflow, but gives no explicit when-to-use vs. dl_editor_validate or other validation siblings, and no prerequisites. Usage is inferable rather than stated.

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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