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Kanysh Basic

Kanysh Basic

by almukhanomr
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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volumes_reconcile

Recompute confirmed regional scenarios and compare against a revision-checked Petrel/RMS volume CSV; report differences, unsupported rows as NOT RUN, evidence-labelled causes without altering inputs.

Instructions

Recompute user-confirmed regional scenarios and compare against a revision-checked Petrel/RMS volume CSV. User maps region_id/metric/value/unit columns and states metric/region definitions. Report differences, unsupported rows as NOT RUN and evidence-labelled possible causes (contact, Bo, NTG clipping, inactive cells, region definition); never auto-calibrate inputs.

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": "volumes_reconcile"
  }
}

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

A4.5/5.0
Behavior5/5

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

Goes well beyond the annotation hints by disclosing outcome semantics: unsupported CSV rows are reported as NOT RUN, causes are evidence-labelled (contact, Bo, NTG clipping, inactive cells, region definition), and the tool never auto-calibrates inputs. The revision-checked CSV rejection behavior is also surfaced, which annotations do not cover.

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?

Three sentences, front-loaded with the core action before the input requirements and the reporting contract. It is dense but no sentence is filler; the only cost is that several distinct obligations are packed into a single long opening sentence.

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 deep nested schema with no output schema, the description covers inputs, the comparison contract, and the report contents. It leaves open whether the run is synchronous or a job (an idempotency_key and execution block exist), and it does not clarify whether scenarios are passed inline or referenced from an existing artifact.

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 coverage is 100%, so the baseline is 3, but the description adds real meaning by explaining the roles of the columns map (region_id/metric/value/unit) and the metric/region definition statements. It still says nothing about the execution policy block or relative_tolerance semantics, which the schema alone must carry.

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 compound action (recompute confirmed regional scenarios, compare against a revision-checked Petrel/RMS volume CSV) on named resources. An agent can distinguish this reconciliation tool from regional_volumes, scenarios_compare, and model_diff purely from the text.

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

Usage Guidelines4/5

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

The description makes the operating context clear: the user supplies a mapped CSV plus explicit metric/region definitions, and the tool reports differences. It never names a sibling as an alternative (e.g. regional_volumes for straight computation, scenarios_compare for scenario-vs-scenario), so routing is implied rather than explicit.

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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