Skip to main content
Glama
mpalermiti

outlook-mcp

by mpalermiti

outlook_search_mail

Read-only

Search emails across folders using KQL queries to locate messages by sender, subject, or date filters.

Instructions

Full-text search mail with KQL across all folders (or one, if folder is set).

Use this for "find emails about X"; use outlook_list_inbox for structured filters scoped to a single folder.

Example: outlook_search_mail(query="from:sarah@acme.com received>=2026-01-01", count=10) query is Microsoft KQL (from:, subject:, received>=, hasattachment:true, AND/OR/NOT). Operators must be UPPERCASE — lowercase and is matched as a literal term. Two terms with no operator between them broaden the search; use AND explicitly to narrow. Pass concise=True to drop large fields (preview, categories) — ~10x fewer tokens.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNo
queryYes
cursorNo
folderNo
conciseNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.14.0
  2. Removedv1.12.0
  3. First observedv1.11.0

TDQS

A4.7/5.0
Behavior4/5

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

Annotations only indicate readOnlyHint. The description adds behavioral details: it searches all folders unless folder is specified, explains the effect of concise=True on token count, and notes that lowercase operators are treated as literals, which affects query results. It does not contradict the read-only annotation.

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 a compact paragraph that covers purpose, alternative, example, query syntax, and output size option. Each sentence adds new information; no fluff. It is appropriately sized for the complexity of the query language.

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 absence of an output schema and the presence of sibling tools, the description provides sufficient context: how to invoke, what parameters mean, when to choose it over alternatives, and a note on output size. It does not need to explain return values because no output schema is provided.

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 has no parameter descriptions, so the description carries the burden. It explains query is KQL with specific fields and operators, explains folder scoping, and explains concise=True reduces output. Count and cursor are not explained but are standard; cursor is not mentioned. Overall, it adds significant meaning to most parameters.

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 it performs full-text search on mail using KQL, with scope across all folders or a specific one. It provides an example and distinguishes from outlook_list_inbox by noting that tool is for structured filters in a single folder.

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?

It explicitly says 'Use this for "find emails about X"; use outlook_list_inbox for structured filters scoped to a single folder,' and explains the query syntax, including operator case sensitivity and behavior of multiple terms, giving clear guidance on when and how to use.

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