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AntonioBlago

VisiblyAI MCP Server

by AntonioBlago

import_meeting_preview

Turn a meeting transcript into a reviewable proposal for a project, returning lists of facts, brand rules, personas, and profile fields to edit before applying.

Instructions

Turn a meeting or call transcript into a reviewable proposal for one project. Credits: 15 per run, charged to the key holder.

Returns four lists, nothing is written: brain facts (each with a category and typed entities), brand rules (not_allowed, preferred, tone, hard_rule, excluded_topic), personas and brand profile fields. Show them to the user, let them strike or edit items, then call import_meeting_apply with the selection. text: 200 to 60000 characters, plain text; source: a short name for the transcript. Needs the key right memory:write and access to the project.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
sourceNo
project_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.6.1

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and delivers: it is non-mutating ('nothing is written'), states cost ('15 per run, charged to the key holder'), and required authorization ('memory:write right and access to the project'). This is unusually rich behavioral context for a preview tool.

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?

Purpose is front-loaded, then cost, return content, workflow, and params in sequence. The sentence is dense but each clause (cost, safety, workflow, constraints) earns its place; only minor tightening is possible.

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

Completeness5/5

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

Although an output schema exists, the description still explains the four returned lists and their categories, which aids selection. Combined with cost, auth, workflow, and parameter constraints, an agent has everything needed to invoke it correctly.

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?

Schema coverage is 0%, so the description must compensate. It documents 'text: 200 to 60000 characters, plain text' and 'source: a short name for the transcript', covering two of three parameters well; project_id is only implied by 'for one project'. A small gap remains for the id parameter's expected form.

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?

States a specific verb and resource: 'Turn a meeting or call transcript into a reviewable proposal for one project.' It also names the downstream sibling import_meeting_apply, so the agent can distinguish this preview step from the apply step without opening either schema.

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

Usage Guidelines5/5

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

Explicit when-to-use workflow: call this to get a proposal that 'nothing is written', show the four lists to the user, let them strike/edit, then call import_meeting_apply with the selection. The alternative tool and the condition selecting it are named directly.

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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