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MisarMail MCP Server

by io.github.Misar-AI

list_inbox_conversations

Read-onlyIdempotent

List unified-inbox conversations (threads) with their status and detected intent. Use this for triage; use list_emails for individual messages in a folder.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFree-text search across the thread
limitNoResults to return (default 20)
intentNoFilter by detected intent, e.g. interested, unsubscribe, question
offsetNoOffset for pagination
statusNoFilter by conversation status
channelNoFilter by channel, e.g. email

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds useful context about unified-inbox and detected intent, indicating the tool provides an overview. It does not contradict annotations. However, it does not detail pagination behavior or access scope, but with strong annotation coverage, this is a minor gap.

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 two sentences, front-loading the core purpose and then providing usage differentiation. Every sentence adds value; no filler or redundancy.

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?

With 6 parameters and no output schema, the description sufficiently explains the tool's purpose and filtering options. It mentions return fields (status and intent) but lacks explicit return format or pagination details. Given the complexity, it is mostly complete for an AI agent to use effectively.

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?

All 6 parameters are fully described in the input schema (100% coverage). The tool description does not add new meaning beyond what the schema already provides, so baseline 3 is appropriate. No additional semantic benefit is gained from the description.

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 clearly states the tool lists unified-inbox conversations (threads) with status and detected intent, and distinguishes from sibling tool 'list_emails' by specifying it is for individual messages in a folder. The verb 'List' plus the resource 'conversations' is specific and unambiguous.

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?

The description gives explicit usage guidance: 'Use this for triage; use list_emails for individual messages in a folder.' This directly tells when to use this tool and when to use an alternative, making the selection clear for an AI agent.

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

A3.7/5.0
Disambiguation3/5

Most tools are cleanly separated by resource and action, and the descriptions do a good job of cross-referencing related tools. However, there are several close clusters—get_analytics vs generate_report, get_deliverability_score vs run_deliverability_audit, check_dmarc vs verify_domain, and list_emails vs list_inbox_conversations vs get_email—that could cause an agent to pick the wrong one. The detailed descriptions reduce but do not eliminate this ambiguity.

Naming Consistency5/5

Tool names follow a consistent snake_case verb_noun pattern throughout: create_, get_, list_, send_, toggle_, and so on. Even multi-word actions like select_ab_test_winner and categorize_inbox_emails stay uniform. The only slight deviation is the bare verb upgrade, but it is readable and does not break the overall pattern.

Tool Count2/5

54 tools is far beyond the typical well-scoped MCP surface and lands heavily in the 'too many' range. While the domain is broad, many tools could be consolidated—multiple analytics/reporting tools, several deliverability checks, and separate email/inbox listing tools create redundancy. The sheer number increases selection overhead and makes the toolset harder for an agent to navigate reliably.

Completeness3/5

The core email marketing lifecycle is represented: domains, contacts, campaigns, templates, automations, sends, and analytics all have main operations. However, there are notable gaps—no update/delete for campaigns, templates, forms, or automations; no create/update/delete for forms; no sandbox enable/disable; and no way to install marketplace items. These are workable gaps but would cause failures for agents trying to perform full lifecycle management.

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