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AvolveAi

Prosp MCP Server

by AvolveAi

Send Voice Message

send_voice_message

Send a LinkedIn voice message to a lead's profile to personalize outreach and improve engagement.

Instructions

Send a LinkedIn voice message to a profile.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYesInput for sending a LinkedIn voice message.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.2.0

TDQS

C2.9/5.0
Behavior2/5

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

The annotations only provide destructiveHint=false, and the description adds little beyond the bare sending action. It does not disclose that this sends an external, likely irreversible communication, nor does it mention authorization needs, rate limits, or side effects. The description is not contradictory, but it does not carry the behavioral disclosure burden well.

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 one concise sentence with no filler and the core action is front-loaded. However, it mostly restates the title plus 'LinkedIn' and 'to a profile', so it is efficient but not exceptional.

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?

The input schema has no message content or audio parameter, yet the description does not explain that the voice message is predefined, attached to a campaign, or selected elsewhere. An agent cannot fully understand what will actually be sent, even though an output schema may cover return values.

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%: both linkedin_url and campaign_id have descriptions in the schema. The tool description adds no additional parameter meaning, so the high-coverage baseline 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 uses a specific verb and resource: 'Send a LinkedIn voice message to a profile', which clearly identifies the action and target. It does not explicitly compare against the sibling 'send_message', so differentiation relies mainly on the word 'voice' instead of an explicit contrast.

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?

There is no guidance about when to choose this tool over alternatives like send_message. It gives no exclusions, prerequisites, or routing cues, so an agent must infer the appropriate context from the tool name alone.

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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