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revenue-enablement-mcp

Discovery Question Bank

discovery_question_bank
Read-only

Get contextual discovery questions using MEDDPICC, BANT, SPICED, Challenger or Gap Selling, or all five at once. Questions adapt based on what you already know about the prospect.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
frameworkYesDiscovery framework to use
deal_stageNoStage of conversation
gaps_to_fillNoSpecific information gaps to address
known_metricsNoMetrics/KPIs already discussed
prospect_roleNoRole of person you are meeting with
your_solutionNoYour product/solution for relevant questions
known_pain_pointsNoPain points already identified
prospect_industryNoProspect industry for context

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds useful behavioral context that output is dynamically tailored to supplied context, but says nothing about what the returned question set looks like or how many questions to expect.

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?

Two sentences, zero waste, with the capability and the framework list front-loaded before the adaptive-behavior note. Nothing redundant.

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 read-only generator with full schema coverage and simple annotations, the definition covers purpose and framework scope adequately. The one gap is that no output schema exists, so the description could briefly characterize the returned question set (e.g. grouping by framework), which an agent might want.

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% with 8 parameters, so the schema already documents each field. The phrase 'what you already know about the prospect' loosely signals that fields like known_pain_points, known_metrics and prospect_role drive adaptation, but it adds no format or usage detail beyond the schema baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Get contextual discovery questions') and enumerates the supported frameworks (MEDDPICC, BANT, SPICED, Challenger, Gap Selling, all five). This is clearly distinct from sibling tools like deal_strategy_coach or demo_script_builder, though no sibling is named explicitly to route the agent.

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?

'Questions adapt based on what you already know about the prospect' implies this is used ahead of or during discovery calls, and the framework enum implies selection by methodology. However there is no explicit when-to-use, when-not-to-use, or named alternative among the many siblings.

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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