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AvolveAi

Prosp MCP Server

by AvolveAi

Get Conversation

get_conversation
Read-only

Retrieve a lead's full LinkedIn conversation history using their profile URL. Review past messages to understand context before continuing outreach.

Instructions

Get the LinkedIn conversation history with a lead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYesInput for getting a conversation from LinkedIn.

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

A3.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, and the description's 'Get' is consistent with that. It adds the useful behavioral fact that the response contains historical conversation content with a lead, but does not disclose pagination, empty-result behavior, or authorization. With annotations covering the safety profile, this is adequate for a simple read operation.

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?

One concise sentence with no filler. The action and object are front-loaded, and every word contributes to understanding the tool's purpose.

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?

For a single-parameter, read-only tool with full schema coverage, a readOnlyHint annotation, and an output schema present, the description plus structured data are sufficient for an agent to invoke it correctly. No critical invocation details are missing.

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%, and the only parameter is already described as the lead's full LinkedIn profile URL. The tool description adds no new parameter semantics beyond the implicit link between the URL and the lead, so the baseline 3 applies.

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 ('Get') and resource ('LinkedIn conversation history with a lead'). The phrase makes clear this is a read operation retrieving existing history, which distinguishes it from siblings like send_message or send_voice_message without needing to name them.

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 intended use is implied: call this when you need a lead's past LinkedIn conversation history. However, it does not explicitly say when not to use it or how it relates to alternatives such as send_message or get_lead_stage, leaving routing decisions to inference.

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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