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MisarReach MCP Server

by io.github.Misar-AI

process_sales_agent

Run the sales-agent pipeline over one conversation: decide the next action and carry it out.

This ACTS on a real conversation — depending on what it decides, it can reply to the prospect, create a deal, or book a meeting. It is not a dry run and there is no preview, so call it only when the user wants the agent to take its turn on that specific conversation. For what it has already done, use get_sales_agent_actions.

CONSUMES AI CREDITS. Not idempotent: calling twice processes the conversation twice and can send two messages. Requires an API key, and the agent's configured confidence threshold still governs whether it acts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
conversationIdYesUUID of the conversation to process

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations, the description discloses several important behavioral traits: it can reply to prospects, create deals, or book meetings; it consumes AI credits; calling twice processes twice and may send two messages; it requires an API key; and the configured confidence threshold governs action. This significantly exceeds what annotations alone provide.

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?

The description is front-loaded with the core purpose, then each subsequent sentence adds essential operational context: real-world effects, non-reversibility, idempotency warning, credit consumption, API key requirement, and confidence threshold. No sentence is wasted.

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?

Given the tool's side-effectful nature and lack of output schema, the description provides comprehensive context: it warns about irreversible actions, duplicate invocations, credit consumption, and prerequisites. It also clarifies what it will not do (dry run/preview), making the tool's behavior sufficiently predictable for safe 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?

The input schema already fully describes conversationId as 'UUID of the conversation to process' with 100% coverage. The description repeats that it operates over 'one conversation' but adds no additional parameter-level nuance, so the schema carries the burden and the description is not penalized heavily.

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?

The description states a specific verb and resource: 'Run the sales-agent pipeline over one conversation: decide the next action and carry it out.' It clearly distinguishes itself from siblings by emphasizing that it ACTS on a real conversation, is not a dry run, and refers to get_sales_agent_actions for past actions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit usage guidance is provided: 'call it only when the user wants the agent to take its turn on that specific conversation.' It also clarifies what it is not for ('not a dry run and there is no preview') and points to the alternative get_sales_agent_actions for already-completed actions.

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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TDQS

A4.3/5.0
Disambiguation5/5

Every tool targets a distinct resource and action: deals, leads, lists, autopilot runs, sales agent config, channels, and discovery. Even where tools share an entity, they are clearly differentiated (e.g., list_deals vs get_pipeline, update_deal vs move_deal_stage), with descriptions explicitly calling out when to use which.

Naming Consistency5/5

All 27 tools follow a strict verb_noun snake_case pattern: create, get, list, update, move, search, score, sync, start, submit, verify, etc. No mixed conventions or vague verbs—each name precisely signals its function.

Tool Count3/5

27 tools is on the heavier side and slightly exceeds the typical well-scoped range. However, the server covers multiple subdomains (deals, lead discovery, autopilot, sales agent, channels), so the count is justified by the breadth of the domain, though it feels dense.

Completeness4/5

Core lifecycles are covered: deals (create, list, update, move), leads (search, sync, list, enrich, score, verify, send), autopilot (start, list, status), sales agent (config, actions, process), and channels (status, update). Minor gaps include no delete tool for deals/leads and no stop-autopilot, but these are likely intentional and not blocking.

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