Skip to main content
Glama

Mark All Notifications Read

fizzy_mark_all_notifications_read

Mark all notifications as read at once for a specified account, clearing the notification inbox after you review updates.

Instructions

Mark all notifications in an account as read at once. Use this to clear your notification inbox after reviewing all updates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
account_slugYesThe account slug identifier (e.g., '123456' or '/123456'). This identifies which Fizzy account to operate on. Get available account slugs from fizzy_get_identity or fizzy_get_accounts.

Schema Changelog

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

  1. First observedv1.1.0

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already indicate a mutating, non-destructive operation. The description adds that the operation is bulk and intended after reviewing updates, but it does not disclose side effects, whether notifications are only marked read and never deleted, or any limits/reversibility information. This is acceptable but not richly transparent.

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?

Two short sentences, first stating the operative action and second stating the use case. There is no filler, repetition of the name, or unnecessary detail.

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 one-parameter bulk action with complete schema coverage and annotations, the description covers what the tool does, at what scope, and when to use it. It does not describe the return value, but there is no output schema, and the success behavior is predictable for a notification-read action.

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 schema description has 100% coverage for the single account_slug parameter, including an example and a pointer to fizzy_get_identity/fizzy_get_accounts. The tool description adds no additional parameter-level meaning, so the baseline score of 3 is appropriate.

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?

The description states a specific verb and resource: 'Mark all notifications in an account as read at once.' This clearly distinguishes the bulk operation from the sibling single-notification tool by making 'all' and 'at once' explicit. It does not explicitly name the alternative sibling, but the scope is unambiguous.

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

Usage Guidelines4/5

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

It gives a clear when-to-use instruction: 'Use this to clear your notification inbox after reviewing all updates.' It does not explicitly list exclusions or point to mark_notification_read for handling individual notifications, but the context is specific enough for an agent to select this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Fabric-Pro/fizzy-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server