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teams-chat-mcp

teams-chat-mcp

by hanshengcc
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

export_chats

Export Microsoft Teams chat history to local files, selecting a folder, formats like txt, md, or json, and a date filter to save messages on or after a chosen date.

Instructions

Export full chat history to local files.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "directory": "<directory>"
    },
    "name": "export_chats"
  }
}

Input Schema

NameRequiredDescriptionDefault
sinceNoOnly messages on/after this date
formatsNoAny of txt, md, json; default txt and json
directoryYesLocal folder to write chats.<format> into
conversation_matchNoOnly conversations whose name contains this

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false, openWorldHint=true and destructiveHint=false, so the write-side safety profile is covered. The description confirms the local-file write target, which is useful context, but says nothing about overwriting existing files, output size, or whether the directory must exist.

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?

A single tight sentence that front-loads the verb, scope, and destination with no filler. It is appropriately sized, though its brevity is partly what leaves usage gaps elsewhere.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists and the input schema is fully documented, so return-value explanation is unnecessary. However, for a tool that writes files into a user-supplied directory, the description should at minimum mention overwrite behavior or expected output layout; that gap keeps it at minimum-viable.

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 every parameter (since, formats, directory, conversation_match) is already documented in the schema, including the txt/md/json format values and the default set. The description adds no filtering or format semantics beyond that, so baseline 3 applies.

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?

States a specific verb (Export) and resource (full chat history) plus the destination (local files), so the agent knows this is a file-producing operation rather than a read. It does not explicitly differentiate itself from siblings like list_conversations or get_messages, but the export-to-disk framing is distinct enough in practice.

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

Usage Guidelines2/5

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

The description gives no when-to-use guidance, no prerequisites, and never references the sibling tools. An agent must infer that this is the tool to pick when materializing chats on disk rather than querying them.

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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