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Get methodology

get_methodology
Read-onlyIdempotent

Retrieve category-management playbook principles for a situation such as supply disruption, slow sales, stock pressure, competitor moves, ranging or pricing. Use it to ground advice in the methodology after reading the data. It returns principles, not workspace figures; for those use get_entity_profile or twelfth_list_products.

Input Schema

NameRequiredDescriptionDefault
topicYesThe situation in plain words, e.g. "supplier out of stock before a promotion".

Output Schema

NameRequiredDescriptionDefault
spineYesCore category-management principles that always apply.
topicYes
sourceYes
warningsYes
groundingYesPrinciples matched to the topic, most relevant first.
retrievalYessemantic when matched by embedding search, bundled when read from the built-in corpus.
evidence_idYes
source_evidence_idsYesWorkspace evidence IDs backing these rows. Empty when the source carries no evidence records.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds meaningful content-type disclosure: it returns principles rather than workspace figures. It does not mention latency or any limits, but for a static playbook lookup that gap is minor.

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 tightly packed sentences: purpose first, usage second, a return-content caveat with sibling routing third. No filler and every clause earns its place.

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

Completeness5/5

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

With an output schema present and full parameter coverage, the description does everything an agent needs: identifies the operation, says when to call it, and clarifies the kind of content returned so it isn't confused with numeric-data tools.

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 coverage is 100% and the topic parameter already carries its own description with an example. The description reinforces the same situation-based phrasing but adds no syntax, format, or constraint beyond what the schema provides, 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?

Specific verb (retrieve) plus resource (category-management playbook principles) with concrete triggering examples like supply disruption, slow sales, and pricing. It explicitly distinguishes itself from get_entity_profile and twelfth_list_products by stating it returns principles rather than figures.

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

Usage Guidelines5/5

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

States when to invoke ('gravity advice in the methodology after reading the data') and where to go instead for workspace figures, naming the two sibling tools. Both the use condition and the exclusion are 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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