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Kanysh Basic

Kanysh Basic

by almukhanomr
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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dataset_import

Imports an uploaded CSV or LAS file as a specified subsurface dataset type, using user-declared CRS, vertical datum, and column units, then returns a job ID and dataset artifact.

Instructions

Import an uploaded CSV/LAS (source_upload upload_id) as a dataset of an explicit kind: collars, trajectory, markers, surfaces, orientations, production, las. CRS, vertical datum and column units are declared by the user, never inferred. Returns job_id then a dataset artifact.

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": {
      "request": "<request>"
    },
    "name": "dataset_import"
  }
}

Input Schema

NameRequiredDescriptionDefault
requestYesTool arguments object (all fields documented; Z is positive upward).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

Annotations only declare the safe-ish write profile (readOnly=false, destructive=false, idempotent=false), so the description's disclosure that this is an async job returning job_id then a dataset artifact is genuinely additive. The 'never inferred' constraint also warns the agent that mismatched CRS/datum will fail validation rather than be silently coerced.

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?

Three tight sentences: what is imported, the declaration rule, and what comes back. The upstream dependency and kind list are front-loaded with no filler.

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

Completeness4/5

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

For a nested-argument write tool with no output schema, the description covers input provenance, the declaration contract, and the return shape (job_id then artifact). Execution-policy details (placement, device_policy, retries) are left entirely to the schema, which documents them well, so this is close to complete.

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 100% and the nested request object documents every field, including the 'never inferred' wording that the description merely repeats. The description adds no syntax or format detail beyond what the schema already provides, so the baseline 3 applies.

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 (import) and resource (uploaded CSV/LAS becomes a dataset) and enumerates the seven explicit kinds, so the agent knows exactly what lands where. The CSV/LAS format scope implicitly separates it from geodata_import/seismic_import/grid_import, but no sibling is named outright.

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?

It correctly names the required upstream step (upload_id comes from source_upload) and mandates that CRS, datum and units be user-declared rather than inferred. However it gives no when-not guidance and never contrasts with nearby siblings such as dataset_import_snapshot, geodata_import, or dataset_validate.

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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