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mrgulshanyadav

MisarReach MCP

process_sales_agent

Process a conversation through the AI sales agent pipeline to determine the next action, generate an optional reply, and log the decision to the agent activity feed.

Instructions

Run the AI sales agent pipeline on a conversation — decides the next action and optionally generates a reply. Logs the action to the agent activity feed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
conversationIdYesUUID of the conversation to process
Behavior3/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It notes the tool 'Logs the action to the agent activity feed,' which is useful side-effect context. However, it doesn't disclose that this runs an AI pipeline (potentially costing quota), whether it mutates conversation state, or what determines whether a reply is generated.

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?

Two concise sentences, front-loaded with the primary purpose and followed by the side-effect. No wasted words, though it could mention prerequisite state in the same compact structure.

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

Completeness3/5

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

For a single-parameter tool with full schema coverage, the description covers the core function and one side effect. However, this is an action-taking tool with no annotations and no output schema; it doesn't clarify return value expectations, whether a reply is guaranteed, or failure modes. Adequate but with gaps for a generative pipeline tool.

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 the single parameter (conversationId, a UUID) is fully described in the schema. The description adds minimal extra meaning beyond the schema. Baseline 3 is appropriate given full schema coverage.

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?

Description uses a specific verb+resource ('Run the AI sales agent pipeline on a conversation') and clarifies it 'decides the next action and optionally generates a reply,' which distinguishes it from pure read or write tools. It doesn't name sibling alternatives, so no differentiation from e.g. get_sales_agent_actions, but the purpose is clear.

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 implies it should be used to process a conversation through the sales agent, but offers no explicit when-to-use or when-not-to-use guidance. It doesn't mention alternatives like get_sales_agent_config or get_sales_agent_actions for inspecting pipeline behavior. Usage context is implied but not articulated.

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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