Tuner MCP

by klei30

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

Dataset Prepare

dataset_prepare

Validate local or Hugging Face datasets and stage them as a persistent dataset ID for training.

Instructions

Stage validated local or pinned Hugging Face data as a persistent dataset ID.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`request`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed7 schema fields changedv0.1.1Sep 15, 2026

addedInput schema / properties / request / properties / chosen_field

Added value: +{
+  "default": "chosen",
+  "minLength": 1,
+  "type": "string"
+}

addedInput schema / properties / request / properties / inline_records

Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "additionalProperties": {},
+        "type": "object"
+      },
+      "maxItems": 1000,
+      "minItems": 1,
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Inline synthetic or user-authored records to validate and persist without a server-local file"
+}

addedInput schema / properties / request / properties / invalid_record_policy

Added value: +{
+  "default": "error",
+  "description": "Fail on invalid rows, or skip them and report removal counts",
+  "enum": [
+    "error",
+    "skip"
+  ],
+  "type": "string"
+}

addedInput schema / properties / request / properties / preference_prompt_field

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Row field for the shared preference prompt, e.g. 'instruction'"
+}

addedInput schema / properties / request / properties / rejected_field

Added value: +{
+  "default": "rejected",
+  "minLength": 1,
+  "type": "string"
+}

addedInput schema / properties / request / properties / tool_calls_field

Added value: +{
+  "anyOf": [
+    {
+      "minLength": 1,
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Row field containing target function calls, including raw xLAM answers"
+}

addedInput schema / properties / request / properties / tools_field

Added value: +{
+  "anyOf": [
+    {
+      "minLength": 1,
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Row field containing OpenAI or raw function declarations, including JSON text"
+}

First observedv0.1.0Sep 13, 2026

TDQS

B3/5.0

Behavior3/5

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

The description adds that data becomes a persistent dataset ID, which is consistent with readOnlyHint=false and clarifies that this is a staging/write operation. However, it does not disclose validation behavior, the meaning of 'validated' or 'pinned', or any side effects beyond persistence. With minimal annotation coverage, more behavioral context would be valuable.

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 a single sentence with no filler, front-loading the action and outcome. It is appropriately sized for a simple summary and does not waste tokens.

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

Completeness2/5

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

Despite having an output schema and many nested parameters with descriptions, the tool is complex, and the description leaves major gaps: no alternative routing, no validation semantics, no lifecycle or persistence details. An agent operating from this description alone would struggle to know whether to call dataset_validate first or how to configure the request.

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%, and the description does not compensate by explaining the required 'request' parameter or any of the nested fields. Saying 'local or pinned Hugging Face data' hints at dataset and hf_repo, but an agent cannot construct a valid request from the description alone.

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 action ('Stage'), a resource type ('validated local or pinned Hugging Face data'), and a concrete outcome ('persistent dataset ID'). It is clear enough to distinguish this from dataset_fetch_hf or dataset_probe_hf, though it does not explicitly contrast with sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given on when to use dataset_prepare versus alternatives such as dataset_validate, dataset_inspect, or dataset_fetch_hf. The description implies usage by stating what the tool does, but it provides no conditions, prerequisites, or exclusions.

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