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Agent.ai MCP Server

by OnStartups

meeting_followup_load_followup_context

Loads user context by identifying role and goals from profile data, inferring them when not explicitly provided. Stores the context for follow-up actions.

Instructions

Loads user context including role classification and goals. Can infer role/goals from user.context via LLM if not provided.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
primary_roleNoUser's role (e.g., SDR, AE, CSM, Solutions Engineer). If empty, will be inferred from user context.{{primary_role}}
goalsNoUser's coaching/improvement goals. If empty, will be inferred from role.{{goals}}
user_context_dataNoUser profile data for role/goals inference when not explicitly provided. Usually {{user.context}}.{{user.context}}
user_emailNoUser's email address for identification.{{_google_email}}
output_variable_nameYesVariable name to store the user context.followup_context
Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses the key behavioral trait of LLM inference, but lacks details on side effects, permissions, or data handling. Given the tool's safety profile, this is adequate but not rich.

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 concise sentences with no waste, front-loaded with the primary purpose. All information is relevant and efficiently presented.

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?

Given no output schema and good parameter descriptions, the description adequately explains the tool's function and inference capability. It could mention the output variable usage, but is sufficient for the tool's complexity.

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 parameter descriptions already covering inference logic. The description adds minimal new meaning beyond what the schema provides, 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 the tool loads user context including role and goals, and mentions inference via LLM. However, it does not distinguish from sibling tools like meeting_prep_load_user_context or meeting_prep_v3_load_user_context.

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?

The description provides context on when to use (loading user context) and inference behavior, but does not specify when not to use or suggest alternatives from the sibling list.

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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