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Kanysh Basic

Kanysh Basic

by almukhanomr
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

scenarios_run

Run seeded probabilistic volume scenarios from a properties artifact by sampling parameter distributions, producing reproducible results for subsurface geological models.

Instructions

scenarios_run

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": "scenarios_run"
  }
}

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

D1.4/5.0
Behavior1/5

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

Annotations declare readOnlyHint=false, destructiveHint=false, idempotentHint=false, and openWorldHint=false, but the description adds nothing beyond that — no mention of what is created, whether results are persisted, or the reproducibility/job semantics. No contradiction with annotations, but zero added value.

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 be concise about — the single token is a name echo rather than a compressed explanation. This is under-specification, not conciseness, though it is at least not padded.

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 that launches seeded Monte Carlo scenario jobs with nested execution policy and artifact references, the description is wholly inadequate. The rich schema supplies argument syntax but nothing about intent, preconditions (e.g. needing a properties artifact), or expected outcomes.

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 schema is unusually thorough (bo, seed, count, distributions, execution policy, idempotency_key all documented inline). Per the stated baseline, a 3 applies even though the description contributes no parameter meaning of its own.

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

Purpose1/5

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, when-not-to-use, or alternative-selection guidance at all. With ~60 siblings including several plausible substitutes (ensemble_run, uncertainty_monte_carlo, scenarios_compare), the absence of routing guidance is a serious gap.

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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