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GTM Change Proposal

propose_gtm_change
Read-onlyIdempotent

Draft structured GTM change proposals for human review with intent, diff, impact, risk, evidence, and measurement plan. Supports ICP, persona, positioning, pipeline stages, exit criteria, GTM motions, scoring models, and playbooks.

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?

The description goes beyond annotations by stating the tool only outputs a proposal for human review and details the proposal components. This aligns with readOnlyHint and adds value.

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?

The description is well-structured with a clear summary, list of components, parameter details, and return value. It is front-loaded and each part serves a purpose.

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 an output schema exists and the description covers all parameters and return type, it is fairly complete. The term 'GTM OS anatomy' may be jargon but does not severely impact completeness.

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?

Despite 0% schema coverage, the description lists and explains all parameters with examples for entity_type and signal_type, providing meaning beyond the raw schema.

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 clearly states that the tool drafts a GTM commit proposal and explicitly says it does NOT apply the change. It specifies the components and distinguishes itself from sibling analysis tools.

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?

While the description implies its use for proposing changes, it does not explicitly compare with alternatives or state when to use this tool versus siblings like analyze_engine or identify_constraint.

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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