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Agent.ai MCP Server

by OnStartups

meeting_prep_load_user_context

Loads user profile, voice preferences, and meeting goals to customize meeting preparation for specific meeting types.

Instructions

Loads user profile data, voice preferences, and meeting goals for personalization.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
user_dataNoUser profile data including name, email, LinkedIn profile. Usually {{user.context}}.{{user.context}}
user_emailYesCurrent user's email address.{{_google_email}}
meeting_typeNoType of meeting for customized goals.general
output_variable_nameYesVariable name to store user context.user_context
Behavior2/5

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

With no annotations, the description must disclose behavioral traits but only vaguely implies reading. It does not state whether the tool is read-only, has side effects, or requires specific permissions. The lack of clarity on mutation versus query is a significant gap.

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 sentence, making it very concise. It front-loads the key action and resource. However, it may be too brief, sacrificing detail for brevity.

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 complexity of the tool (4 parameters, no output schema, many sibling tools), the description is too sparse. It does not explain the return value, storage behavior (despite 'output_variable_name' parameter), or how this tool fits into the meeting prep pipeline. This leaves the agent with insufficient context for correct invocation.

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% and parameter descriptions are adequate. The description adds little beyond summarizing the schema fields (user_data, user_email, meeting_type, output_variable_name). Since the schema already documents each parameter, the description provides marginal added semantic value.

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 loads user profile data, voice preferences, and meeting goals for personalization, making the primary action and resource clear. However, it does not explicitly differentiate from sibling tools like meeting_prep_v3_load_user_context, leaving some ambiguity.

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 is provided on when to use this tool versus alternatives, such as the v3 variant or other meeting prep tools. There is no mention of prerequisites or context that would help an agent decide to invoke this tool.

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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