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Load Json

load_json

Read a JSON file from disk into a new dataset with records or column-oriented data, returning dataset ID, row count, and column details for analysis.

Instructions

Load a JSON file from disk into a new dataset (a list of records or a column-oriented object). Args: file_path (str): Full path to the JSON file on the machine running the server. Returns: dict: dataset_id, rows, columns and column_names of the new dataset.

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": {
      "file_path": "<file_path>"
    },
    "name": "load_json"
  }
}

Input Schema

NameRequiredDescriptionDefault
file_pathYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full burden. It discloses that the file must live on the server machine and that a new dataset is created, but says nothing about error/failure behavior, whether the dataset persists in the session, whether names collide, or any size/rate constraints — thin for a mutation-style creation tool.

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?

Front-loaded one-sentence summary followed by explicit Args/Returns blocks; every line carries information and nothing is padded, though the pseudo-Python docstring formatting is slightly heavier than needed for one parameter.

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 no output schema, the Returns section usefully names dataset_id, rows, columns and column_names. Gaps remain around failure modes and the lifecycle of the created dataset, which matter for an import tool that other tools will consume.

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

Parameters4/5

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

Schema description coverage is 0%, so the description has to compensate, and it does: it explains that file_path is a full path on the machine running the server rather than a client-relative path, which is the single most likely point of misuse for a 1-parameter tool.

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

Purpose5/5

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

States a specific verb and resource ('Load a JSON file from disk into a new dataset') and clarifies the two accepted JSON layouts (list of records or column-oriented object), which distinguishes it from the load_csv / load_excel / load_parquet siblings on format alone.

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?

Usage is only implied by the file format; there is no explicit when-to-use or when-not guidance, no mention of the sibling loaders as alternatives, and no note on what happens if the file is malformed or the dataset already exists.

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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