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Kanysh Basic

Kanysh Basic

by almukhanomr
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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variogram_fit

Fits variogram parameters to conditioning well data for a chosen zone and property, enabling geostatistical modeling with specified length scale, variance, and anisotropy.

Instructions

variogram_fit

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": "variogram_fit"
  }
}

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

D1.7/5.0
Behavior1/5

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

Annotations declare readOnlyHint=false, idempotentHint=false and destructiveHint=false, but the description supplies no behavioral context: it does not say that a job is created/submitted, that conditioning well data (2–2000 points) is required, or how idempotency_key/execution policies affect reruns. It also omits the schema's important caveats (only local placement, float32 rejected, GPU constraints) that the schema does cover, so the description adds nothing beyond structured fields.

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

Conciseness2/5

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

There is nothing to trim, but this is under-specification rather than conciseness: a single bare token that conveys no structure, no front-loaded verb/resource, and no scope.

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

Completeness1/5

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

This is a complex nested-object geostatistics call with a job/idempotency model, no output schema, and non-idempotent annotations; the description provides none of the context an agent needs to call it correctly or know what it returns.

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 every nested field (project_id, properties ref, zone_id, field enum, anisotropy, length_scale_m, conditioning, execution policy) is documented in the schema itself. With no parameter information in the description, the baseline of 3 applies since the schema carries the semantics.

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

Purpose2/5

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

Tautological: description restates name/title.

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

Usage Guidelines1/5

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

There is no when-to-use guidance, no prerequisites, and no mention of alternatives. An agent cannot tell from the description whether this fits a variogram model from conditioning data, per zone, or as a step preceding kriging/simulation.

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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