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draiqw
by draiqw

tg_chat_info

Read-onlyIdempotent

Retrieve details about a Telegram chat or person: id, username, type, member count, bio. Optionally count stored media (photos, videos, files) and discover similar channels.

Instructions

Details about a chat or person: id, username, type, member count, bio.

Args: chat: chat id, @username or exact title. counters: how much is stored in the chat — photos, videos, files, music, voice, round videos, gifs, links, locations, pinned messages. Counted by the server in one request, no history download, so this answers "how much is there to fetch" before fetching. Zero counts are omitted; pass false to skip the request. similar: for channels only — other channels Telegram recommends on the same topic; may come back empty. If Telegram cuts the list short (it does that for accounts without Premium), the reply carries total and truncated.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chatYes
similarNo
countersNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare the operation read-only and idempotent, and the description adds meaningful behavioral detail: counters are server-counted with no history download, zero counts are omitted, similar results may be empty or truncated, and truncation is signaled in the reply. This goes well beyond the structured metadata.

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?

The description is well-structured, front-loaded with a concise summary followed by an Args block. Each parameter explanation adds value, though the similar explanation is slightly elongated with caveats; still, no sentence is wasted.

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?

Without an output schema, the description still surfaces the expected fields, counter categories, and special reply behavior such as total/truncated for similar. An agent has enough information to decide whether to call this tool and how to configure its parameters.

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?

Schema description coverage is 0%, so the description carries the full burden. It thoroughly explains chat accepted formats (id, @username, exact title), counters semantics and behavior, and similar's channel-only scope plus truncation behavior. This is rich, actionable parameter documentation.

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?

The description clearly identifies the tool's purpose: retrieving details about a chat or person, including id, username, type, member count, and bio. It goes beyond a tautology by enumerating what 'info' means, but it does not explicitly distinguish itself from sibling tools like tg_person or tg_status.

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 gives practical guidance for using optional parameters: counters should be used to see how much content is available before fetching, and similar is channel-only. It provides clear context for parameter choices, though it does not explicitly say 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.