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JavaScript
Remote

meeting_followup_render_followup_html

Renders meeting followup artifacts, coaching insights, and transcript analysis into a professional HTML email using Jinja2 templates.

Instructions

Renders the meeting followup document as a professional HTML email. No LLM required - uses Jinja2 templates.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "followup_artifacts": "<followup_artifacts>",
      "coaching_insights": "<coaching_insights>",
      "transcript_analysis": "<transcript_analysis>",
      "enriched_event": "<enriched_event>",
      "output_variable_name": "<output_variable_name>"
    },
    "name": "meeting_followup_render_followup_html"
  }
}

Input Schema

NameRequiredDescriptionDefault
followup_artifactsYesThe artifacts from Generate Followup Artifacts action.{{followup_artifacts}}
coaching_insightsYesInsights from Generate Coaching action.{{coaching_insights}}
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 user name/role.{{user.context}}
enriched_eventYesThe enriched event data.{{enriched_event}}
output_variable_nameYesVariable name to store the rendered HTML.followup_html

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

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

The description mentions using Jinja2 templates and not requiring an LLM, which is a positive behavioral trait. However, with no annotations provided, the description lacks details on idempotency, side effects, or authentication needs. The given info is somewhat helpful but insufficient.

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 a single, concise sentence that conveys the core function and a key distinguishing feature. It is front-loaded and efficient, though could be slightly expanded for completeness.

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?

Given the tool has 7 parameters and no output schema, the description is too brief. It does not explain the relationship to sibling tools or provide context on how inputs are used. The schema helps, but the description should offer a more comprehensive overview, such as the typical pipeline usage.

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%, so the input schema already documents all parameters. The tool description adds no extra parameter meaning beyond the schema, so baseline score of 3 is appropriate.

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 clearly states it renders a meeting followup document as a professional HTML email, using Jinja2 templates. The purpose is specific and distinguishable from sibling render tools, though it does not explicitly differentiate from other 'render' tools.

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, such as other render tools or when to skip LLM usage. The 'No LLM required' note is a capability hint but not usage context.

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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