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Kanysh Basic

Kanysh Basic

by almukhanomr
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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properties_simulate

Simulate geostatistical property fields, such as porosity or permeability, from variogram settings and well conditioning data to support subsurface model scenarios and reproducible analysis.

Instructions

properties_simulate

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": "properties_simulate"
  }
}

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 only declare that this is a non-read-only, non-destructive, non-idempotent operation; the description adds nothing about job submission behavior, that results are asynchronous, idempotency_key semantics, or reproducibility via seed. With a mutation-style tool and rich nested execution options, the description carries a burden it entirely fails to meet.

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?

It is short, but short because it is under-specified rather than efficient: a single bare token with no information to front-load. Conciseness cannot compensate for near-total absence of content.

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?

For a tool with a deeply nested request object, execution/device policy options, and no output schema, the description supplies nothing an agent needs to call it correctly or interpret results. Completely inadequate given the tool's complexity.

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% — every nested field (seed, field, zone_id, anisotropy, conditioning, execution policy) is documented in the schema itself. The description adds zero meaning beyond that, so the baseline 3 applies.

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 indication of when to use this tool versus siblings such as properties_krige, properties_assign, variogram_fit, or uncertainty_monte_carlo. No prerequisites, no exclusions, no alternative routing are stated.

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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