Skip to main content
Glama
oshann

hotmail-mcp

by oshann

list_junk_candidates

Identify junk senders that Outlook's filter has missed, returning ranked candidates with specific reasons for each flag, enabling informed manual rule creation without automatic actions.

Instructions

Find likely junk/spam senders that Outlook's filter has NOT caught.

Read-only analysis. It moves nothing and changes nothing — it returns ranked candidates with the specific reasons each was flagged, so the user can judge the reasoning rather than just the verdict.

Signals include: whether the user has ever written to the sender, whether every message is unread, whether the domain already appears in Junk, display names claiming a brand their domain doesn't support, free-webmail senders posing as organisations, manipulative subject patterns, and (with deep) bulk-mail headers.

Senders already handled by an existing inbox rule are scored down and marked, so you don't propose a rule that duplicates one you have.

To act on a candidate, call propose_rule and show the user the preview. Never act on a candidate automatically.

Args: folder: Folder to analyse. Defaults to Inbox; use a full path for nested folders. days: How far back to look. Default 180. limit: Maximum candidates to return. Default 25, capped at 100. deep: Also fetch bulk-mail headers (List-Unsubscribe) for the top candidates. Slower — it costs one request per candidate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
deepNo
limitNo
folderNoInbox
Behavior5/5

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

With no annotations, the description fully discloses the tool as read-only, non-destructive, and details the analysis signals. It also warns about the cost of the deep parameter and that handled senders are scored down.

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 well-structured, front-loaded with purpose and behavior, followed by parameters. Every sentence is informative and earns its place without redundancy.

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 has no output schema and 4 parameters, the description covers return values (ranked candidates with reasons), signals, and parameter details. It also integrates with propose_rule, making it complete for its complexity.

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

Parameters5/5

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

Despite 0% schema description coverage, the text thoroughly explains each parameter: folder defaults to Inbox, days default 180, limit default 25 capped at 100, and deep costs one request per candidate. This adds significant meaning beyond the schema.

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 finds likely junk/spam senders not caught by Outlook's filter. It uses a specific verb and resource, and distinguishes from siblings like propose_rule and propose_flag_as_junk.

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?

The description explicitly states to never act automatically and directs to call propose_rule for actions. It provides clear context on when to use the tool, though it lacks explicit exclusions or alternatives beyond propose_rule.

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

Install Server

Other Tools

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/oshann/hotmail-mcp'

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