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alexboissAV

artefact-revenue-intelligence

by alexboissAV

GTM Change Proposal

propose_gtm_change
Read-onlyIdempotent

Propose structured GTM changes with detailed analysis of intent, impact, risk, and measurement for team review.

Instructions

Draft a structured GTM commit proposal following the GTM OS anatomy.

Creates a version-controlled change proposal with: Intent, Diff, Impact Surface, Risk Level, Evidence, and Measurement Plan. Does NOT apply the change — outputs a proposal for human review.

Args: entity_type: What's being changed — "icp", "persona", "positioning", "pipeline_stage", "exit_criteria", "gtm_motion", "scoring_model", "playbook". change_description: Human-readable description of the proposed change. current_state: Optional description of current state (before). proposed_state: Optional description of proposed state (after). signal_type: Optional signal type that triggered this change (win_loss_pattern, conversion_drop_off, velocity_anomaly, spiced_frequency, attribution_shift, data_quality). signal_data: Optional JSON string with structured evidence from signal detection.

Returns: JSON with structured commit proposal and next steps.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entity_typeYes
change_descriptionYes
current_stateNo
proposed_stateNo
signal_typeNo
signal_dataNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description aligns by stating 'does NOT apply the change' and creates a proposal. It adds context about the proposal structure (Intent, Diff, etc.) and 'version-controlled', which annotations do not cover. However, it doesn't clarify if the proposal itself modifies any internal state or is purely advisory.

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?

The description is concise and well-structured, front-loading the primary purpose and then listing parameters in clear groups. Every sentence adds value, no redundancy or filler. It uses a clean bullet-like format for parameters.

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?

Given 6 parameters (2 required) and medium complexity, the description covers purpose, usage, parameters, return type, and structure. It lacks details on error handling or example usage. The return value is described as 'JSON with structured commit proposal and next steps' but no output schema is provided. Still, it's largely sufficient for an agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description fully compensates by explaining each parameter: entity_type lists 8 possible values, change_description is human-readable, current_state and proposed_state are optional descriptions, signal_type lists 6 values, signal_data is JSON string. This adds complete meaning beyond the schema's raw types.

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?

The description uses specific verbs 'Draft' and 'outputs' with a clear resource 'structured GTM commit proposal', distinguishing it from other tools. It explicitly states it does not apply changes, separating it from execution tools. Sibling tools like 'detect_signals' further differentiate.

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 clearly states when to use: to propose a change without applying it, for human review. It lists entity types and optional parameters. While it doesn't explicitly state when not to use, the 'does NOT apply' and 'for human review' provide clear context. No explicit alternatives are given, but sibling tools are listed.

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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