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draiqw

telegram-mcp

by draiqw

tg_activity

Identify chats with any conversation during a period, counting incoming and outgoing messages separately. Ideal for daily activity recaps.

Instructions

Which chats had any conversation in a period — "where did I talk today".

Unlike tg_unread this covers chats that are already read and chats where only the owner wrote, so it is the right starting point for a daily recap. Counts incoming and outgoing separately and scans the archive too.

Args: since: "today" (default, local midnight), an ISO datetime, or a relative offset like "-6h". until: upper bound, ISO datetime; omit for "up to now". limit_chats: cap on chats returned. kind: keep one type only — "user", "bot", "group", "channel". include_own: false drops chats where nobody but the owner wrote. per_chat: also include this many messages from each chat as a sample.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNo
sinceNotoday
untilNo
per_chatNo
include_ownNo
limit_chatsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses that it counts incoming and outgoing separately, scans the archive, includes owner-only chats, and explains the effect of include_own. These are meaningful behavioral details beyond what annotations would provide.

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: an introductory summary followed by a clear list of arguments with brief explanations. Every sentence contributes useful information, and it remains readable despite covering six parameters.

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?

For a read query tool with six optional parameters, the description covers scope, filtering, counting behavior, archive scanning, and parameter effects. An output schema is present to document return values, so the description is sufficiently complete for an agent to correctly invoke the tool.

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?

The input schema has zero descriptions for parameters, but the description fully compensates by explaining each parameter's meaning, allowed values (e.g., kind types), and examples (e.g., since formats like '-6h'). It adds significant value beyond the raw 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 what the tool does: identifies chats with conversation in a period, with the relatable example 'where did I talk today'. It also explicitly distinguishes itself from the sibling tool tg_unread, making its purpose and scope unambiguous.

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?

The description provides explicit usage guidance by contrasting with tg_unread ('Unlike tg_unread this covers chats that are already read...') and states when it is appropriate ('right starting point for a daily recap'). It clearly communicates when to choose this tool over alternatives.

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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