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squirrelscan

by com.squirrelscan

List notifications

list_notifications
Read-only

Read the organization's notification feed, newest first: what finished, what broke, and what changed since you last looked. Categories include audit_complete, issues_detected, audit_failed, schedule_paused. Use this to catch up at the start of a session ("did last night's scheduled audit run?", "what failed?") instead of polling every website. Each entry carries the category, the human-readable title and body, a data payload with the ids involved (website_id, run_id, report_id), and whether a human has read it in the dashboard. Filter with category, or unread_only to see just what nobody has looked at yet. Paginated with limit/offset. Read-only: this never marks anything read.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax notifications to return (default 20, max 100).
offsetNoRows to skip (default 0).
categoryNoRestrict to these categories, e.g. ["audit_failed"]. Omit for every category.
unread_onlyNoOnly notifications no one has read in the dashboard yet.

Schema Changelog

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

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

Discloses ordering (newest first), the data shape (category, title, body, payload ids, read state), filtering options, pagination, and the critical read-only behavior that it never marks anything read. This goes well beyond the annotations (readOnlyHint=true) by detailing what the response contains and how the feed behaves.

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?

Every sentence earns its place: purpose, content, use case, filtering, pagination, and read-only safety. It is front-loaded with the main purpose and remains readable despite covering many details.

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?

With no output schema, the description carries the full burden of explaining return values—and it does: category, title, body, data payload, read status. It also covers ordering, filters, pagination, and the use case. Nothing essential is missing for an agent to call this correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers 100% of parameters, but the description adds contextual meaning: unread_only is explained as 'what nobody has looked at yet', and pagination via limit/offset is explicitly mentioned. This enriches the schema's baseline without needing to describe syntax.

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 opens with a specific verb ('Read') and resource ('organization's notification feed'), and immediately tells the agent what it will find: what finished, broke, or changed. It is clearly distinct from siblings like list_audits or list_issues by focusing on notifications and enumerating the exact categories.

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?

Explicitly recommends using this at the start of a session to catch up on scheduled audit results, and contrasts with 'polling every website'. It doesn't name a specific sibling tool as an alternative, but gives enough context to know when to invoke it.

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

A4.4/5.0
Disambiguation5/5

Each tool maps to a distinct resource-action pair: websites, audits, issues, rules, credits, notifications, and account info are cleanly separated. Even the close pairs (delete_website vs delete_websites, get_rule vs list_rules) are clear singular/bulk or fetch-all vs fetch-one distinctions.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern: add_, delete_, get_, list_, run_, comment_, send_, create_. The only outlier, whoami, is a conventional standalone command and does not break the overall predictability.

Tool Count4/5

At 20 tools, the surface is slightly heavy, but the count is justified by the wide domain coverage: audit lifecycle, issue tracking, rule catalog, billing/credits, notifications, and account management. Each tool appears to earn its place, and none feel redundant.

Completeness4/5

The core workflow is well covered: run an audit, poll it, fetch a report, compare audits, list and comment on issues, browse rules, and manage websites. Minor gaps exist—no cancel-audit tool, no update_website, and issues cannot be status-changed—but agents can work around these using the existing tools.

Resources