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Agent.ai MCP Server

by OnStartups

meeting_followup_generate_coaching

Generate personalized coaching insights from meeting transcripts, identifying strengths, improvements, and strategic recommendations to enhance performance.

Instructions

Generates personalized coaching insights including strengths, improvements, and strategic recommendations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
transcript_analysisYesThe analysis from Analyze Transcript action.{{transcript_analysis}}
user_contextNoUser context from Load Followup Context action.{{followup_context}}
user_context_dataNoRaw user.context data as fallback for role inference.{{user.context}}
modelNoModel for coaching generation.gpt-5
output_variable_nameYesVariable name to store coaching insights.coaching_insights
Behavior2/5

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

No annotations provided, so description carries full burden. It fails to mention that this is likely a generative call consuming tokens, does not indicate side effects, or specify whether it is safe to call repeatedly.

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?

Single sentence with no fluff. Front-loads the core purpose immediately.

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

Completeness2/5

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

Without an output schema, the description should explain the return value format or structure. It does not. Also lacks mention of required prior steps (Analyze Transcript, Load Followup Context) that are referenced in parameter defaults.

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% with descriptions for all 5 parameters. Description adds generic terms like 'strengths, improvements' but does not provide semantic meaning beyond the schema's own descriptions.

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?

Clearly states the verb 'generates' and resource 'personalized coaching insights', listing included content (strengths, improvements, recommendations). It distinguishes from siblings like meeting_followup_analyze_transcript by focusing on coaching generation.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives like meeting_followup_generate_all_artifacts or meeting_followup_generate_followup_artifacts. Missing context on prerequisites or workflow order.

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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