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MisarMail MCP Server

by io.github.Misar-AI

list_ab_tests

Read-onlyIdempotent

List A/B tests on the account with each variant's results and whether a winner has been picked yet.

Use it to see which tests are still running and which are waiting on a decision. Reading results here is safe and has no effect on the test — declaring a winner is a separate, irreversible action that sends to the held-back audience.

Reads only. Requires an API key. A test with no winner selected is still open; treat early results with care, since a lead that looks decisive on a small sample often is not.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number (default 1)
typeNoFilter by what is being tested
limitNoResults per page (default 20)

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate read-only and idempotent behavior. The description adds value by mentioning the API key requirement, clarifying that reading has no effect on the test, and warning about interpreting small-sample results. It does not contradict annotations.

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 concise but includes multiple useful points (purpose, use case, safety, caution). It is front-loaded with the core function and each sentence adds value, though it could be slightly tighter.

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?

For a simple list operation, the description explains what results are returned (variant results and winner status) and addresses edge cases (no winner selected, early sample caution). Without an output schema, it still conveys the essential information, though it could explicitly mention pagination fields.

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%, so the baseline applies. The description does not provide additional parameter explanations beyond what the schema already covers (page, type, limit). It adds no extra semantics, so a 3 is appropriate.

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 A/B tests with variant results and winner status, using a specific verb and resource. It distinguishes from sibling tools like create_ab_test and select_ab_test_winner by focusing on the read-only listing aspect.

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 explicitly states when to use it ('see which tests are still running and which are waiting on a decision') and provides exclusions, noting that declaring a winner is a separate irreversible action. It also advises caution with early results, giving clear usage context.

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.

Resources