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Local Model MCP

Local Model MCP

by Doughboyfressh
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

inspect_dataset

Read-only

Validate a dataset and report row formats and character counts without exposing text, so you can check data structure before training.

Instructions

Validate a dataset and report row formats and character counts, without text.

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": {
      "dataset": "<dataset>"
    },
    "name": "inspect_dataset"
  }
}

Input Schema

NameRequiredDescriptionDefault
datasetYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds one genuine behavioral fact — its report excludes raw text — but leaves unclear whether that is a privacy guarantee, a size limit, or a failure mode.

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?

One sentence, front-loaded with the verb and resource, with the report contents before the qualifier. Nothing is padded, though the closing qualifier is too terse to parse confidently.

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?

An output schema exists, so return shape need not be explained, and annotations cover the read-only nature. The remaining gap is the undefined dataset identifier and the missing routing signal against sixteen siblings, which the description does not fill.

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?

There is a single required parameter with 0% schema description coverage, so the description must carry the burden and does not; it never explains whether 'dataset' is a name, an ID, or a path, nor how the naming pattern maps to existing datasets. It only repeats the resource noun already given by the name.

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 and resource ('Validate a dataset') plus the report contents (row formats, character counts), which separates it from create_dataset and inspect_model. The trailing 'without text' is ambiguous phrasing that muddies rather than sharpens the purpose.

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?

Validation-before-training is implied by 'validate', giving a weak usage signal. There is no explicit when-to-use condition, no stated prerequisite, and no named alternative among the many siblings (e.g. inspect_model, read_tensor).

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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