Read-only public statistics for Telegram groups tracked by Limzo (https://limzo.com), a group analytics bot. Two tools: get_global_stats (network-wide totals) and get_group_stats (activity, engagement, top members, achievements for a group by its public slug - the last part of its https://limzo.com/s/ URL; browse https://limzo.com/groups/ to find groups). Privacy-safe: payloads never include verbatim member messages. No authentication required. Docs: https://limzo.com/docs/
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.7/5 across 3 of 3 tools scored.
Each tool targets a distinct purpose: global network stats, specific group stats, and group discovery. No overlap in functionality.
All names follow a consistent verb_noun pattern with snake_case: get_global_stats, get_group_stats, list_groups.
With only 3 tools, the set is minimal but well-scoped for the domain of Telegram group stats. Each tool earns its place, though slightly thin.
Covers global stats, per-group stats, and group discovery. Minor gap: no tool for comparing multiple groups directly, but core workflows are supported.
get_global_statsGet network-wide Limzo totalsACall this for network-wide numbers across all public Telegram groups tracked by Limzo (group count, messages, active users, replies, reactions, karma — 7-day and all-time) plus the current top groups by Limzo Score. For finding a specific group, prefer list_groups.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| ok | Yes | |
| docs | No | Human-readable API documentation. |
| stats | Yes | |
| openapi | No | Machine-readable OpenAPI document for the REST API. |
| top_groups | No | Current top public groups by Limzo Score. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true. The description adds context on returned metrics and timeframes, but does not cover potential caveats like data freshness or rate limits. Nonetheless, it adds meaningful behavioral information beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences: first defines purpose and lists outputs, second gives usage guidance. No redundant or unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters, the presence of an output schema, and sibling tools, the description fully covers what the tool does, what it returns, and when to use it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist (0 params), so schema coverage is 100% trivially. The description does not need to add parameter information, and the baseline score of 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves network-wide numbers across all tracked public Telegram groups, listing specific metrics (group count, messages, active users, etc.) and timeframes (7-day and all-time). It distinguishes itself from sibling tool 'list_groups' by its scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly specifies when to use ('Call this for network-wide numbers') and provides an alternative for specific group queries ('For finding a specific group, prefer list_groups').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_group_statsGet public stats for one Telegram groupACall this when the user asks about the activity, health, leaderboard, mood, or trends of a specific Telegram group tracked by Limzo. slug is the last part of the group's public page URL, limzo.com/s/ (e.g. "hipo"). Returns the curated limzo.public_stats/v1 JSON: messages, replies, active members, daily series, top members, mood, reactions, language mix, the Limzo Levels ladder (lifetime XP + badge tiers), and weekly-league standings when the group has the league enabled. Optional range: 7d (default), 30d, all — the wider ranges only serve real data for groups on a paid plan and otherwise silently fall back to 7d, so check range.key in the result. Never includes verbatim member messages.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The group's public slug — the last part of its limzo.com/s/<slug> URL. | |
| range | No | Stats window. Defaults to 7d. 30d/all require the group's paid plan; free groups silently fall back to 7d. |
| Name | Required | Description |
|---|---|---|
| ok | Yes | |
| docs | No | Human-readable API documentation. |
| group | Yes | |
| range | Yes | The range actually served — compare `key` with what you requested to detect the plan fallback. |
| stats | Yes | |
| schema | Yes | |
| openapi | No | Machine-readable OpenAPI document for the REST API. |
| generated_at | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes well beyond the readOnlyHint annotation by detailing the exact returned data (messages, replies, active members, etc.), explaining the silent fallback behavior for range values on free plans, and confirming no verbatim messages are included. This adds significant behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph that efficiently conveys key information. While it is relatively long, each sentence provides necessary guidance. It could be slightly more concise, but it is well-structured and front-loaded with the primary purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (not shown but referenced), the description sufficiently details the return value. It addresses the tool's behavior for different parameter values and clarifies limitations. The description feels complete for a stats retrieval tool with good annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the schema already documents both parameters well. The description adds extra meaning for 'slug' (explaining it as the last part of a URL) and for 'range' (describing the fallback behavior), enhancing the agent's understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description begins with a specific verb and resource ('Get public stats for one Telegram group'), immediately clarifying the tool's purpose. It lists concrete use cases (activity, health, leaderboard, mood, trends) and distinguishes from siblings (get_global_stats, list_groups) by focusing on a single group.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to call the tool based on user queries about a specific group's stats. It provides usage guidance on the range parameter, noting paid-plan limitations and silent fallback. However, it does not explicitly mention when not to use or name alternatives, though siblings are provided in context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_groupsList or search public Telegram groupsACall this to discover Telegram groups tracked by Limzo — to browse the directory, filter by language, or find a group's slug for get_group_stats. Optional query filters case-insensitively over group title, username, slug, and description. Optional lang (ISO 639-1, e.g. "fa", "es") keeps only groups where that language is a meaningful share of what members write — the way to answer "find active Persian/Spanish groups". Omit both to list the top groups by Limzo Score. Each row carries a language mix (primary language + top languages as percentages); rows also include slug, title, username, plan, member_count, 7-day messages and active members, score and page URLs, plus total_matches so you can tell when more groups matched than were returned.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | ISO 639-1 language code (e.g. "fa", "es", "en"). Keeps only groups where that language is a meaningful share of the group. | |
| limit | No | Maximum groups to return. Defaults to 20, capped at 50. | |
| query | No | Keyword matched case-insensitively against group title, username, slug, and description. Omit to list the top groups by Limzo Score. |
| Name | Required | Description |
|---|---|---|
| ok | Yes | |
| docs | No | Human-readable API documentation. |
| lang | No | The applied ISO 639-1 language filter, or null when not filtering by language. |
| query | No | The normalized search keyword, or null when listing top groups. |
| groups | Yes | |
| openapi | No | Machine-readable OpenAPI document for the REST API. |
| total_matches | Yes | Groups that matched, before the limit was applied. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=true; description adds that query matches case-insensitively on specific fields, lang filters by meaningful language share, returns detailed row data including language mix, total_matches, etc. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single paragraph, but each sentence adds value. Front-loaded with purpose, then parameter details, then result fields. Slightly dense but clear and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Description covers purpose, parameters, usage scenarios, and result fields in detail. With output schema existing, the field list is adequate. Complements sibling tool usage. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. Description adds extra context: query fields enumerated, lang behavior ('meaningful share'), limit defaults and cap. Provides operational nuance for parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Title and description clearly state the tool lists/searches Telegram groups. It specifies actions: browse directory, filter by language, find slug for get_group_stats. Distinguishes itself from siblings get_group_stats and get_global_stats.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use: to browse, filter, or find slug. Provides parameter behavior: optional query filters case-insensitively, optional lang filters by language, omit both to list top groups. Implicitly suggests alternatives for stats.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
