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bw-modeling-mcp

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TypeScript
Remote

bw_get_planning_sequence

Reads a Planning Sequence (TLOGO PLSQ) definition for Integrated Planning or embedded BPC, returning ordered steps, aggregation levels, and filters.

Instructions

Read a Planning Sequence (TLOGO PLSQ) definition — an ordered list of planning steps for Integrated Planning / embedded BPC. Returns name, description, InfoArea, package, status, and the ordered step list. Each step shows its kind (planning function, or manual input: an input template that only opens its filter), the aggregation level, the planning function (planning service), and the filter name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNo"text" (default): readable summary. "raw": the unmodified XML.
planning_sequence_nameYesTechnical name of the Planning Sequence (e.g. "OBJECT_NAME").

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.8.1
    • addedInput schema / properties / format
      Added value: +{
+  "description": "\"text\" (default): readable summary. \"raw\": the unmodified XML.",
+  "enum": [
+    "text",
+    "raw"
+  ],
+  "type": "string"
+}
  2. Addedv0.9.1

TDQS

A4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden — and it does disclose meaningful behavior: the exact payload (name, description, InfoArea, package, status, ordered steps) and the semantic distinction that a 'manual input' step is an input template that only opens its filter. It stops short of stating permission requirements or error behavior for a missing sequence.

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 sentences, purpose front-loaded, zero filler — the domain qualifier and the field list both earn their place and nothing is repeated.

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?

There is no output schema, so the description correctly compensates by enumerating the return fields and per-step attributes. It is nearly complete, missing only failure/permission behavior and any note about how format=raw differs.

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%, including the enum for format ('text' vs 'raw'), so the schema already documents both parameters fully. The description adds no parameter-level detail, so the baseline 3 applies.

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 verb (Read) and resource (Planning Sequence, TLOGO PLSQ) with the exact domain (Integrated Planning / embedded BPC), and enumerates the returned fields. It is clearly distinguishable from sibling reads like bw_get_planning_function and bw_get_planning_filter.

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?

Usage is implied by the name and the read framing, but the description never states when to reach for this tool instead of bw_get_planning_function, bw_get_planning_filter, or bw_get_aggregation_level, and lists no prerequisites or exclusions. Adequate but leaves routing to inference.

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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