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

Annotations already declare readOnlyHint=true and openWorldHint=false. The description adds useful scope context: network-wide coverage, 7-day/all-time windows, durable lifetime totals, and top groups. It does not contradict annotations. Since the output schema exists, the lack of return format description is acceptable.

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, front-loaded sentence listing key outputs, followed by one concise sentence for guidance. No wasted words; every clause adds 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?

The tool is simple (0 params) and has an output schema. The description thoroughly covers the returned data (metrics, timeframes, totals, top groups) and provides usage context. It is complete and self-sufficient.

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?

There are zero parameters and schema description coverage is 100% (vacuously). No parameter explanation is needed. The description adds meaning about what data is returned, which is beyond the empty 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 states a specific verb ('Call this for network-wide numbers') and clearly resources ('all public Telegram groups tracked by Limzo'), with an enumerated list of metrics. It is distinct from siblings: 'For finding a specific group, prefer list_groups' explicitly differentiates it.

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?

It provides an explicit alternative: 'For finding a specific group, prefer list_groups.' This gives clear when-to-use/when-not-to-use guidance and names the sibling tool.

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

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

Beyond the read-only annotation, it discloses that verbatim member messages are never included, that private groups withhold identities/description/member_count and return an anonymised leaderboard, and that wider ranges silently fall back to 7d unless the group is on a paid plan. These are important behavioral caveats an agent needs to set correct expectations.

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 trigger is front-loaded, every sentence carries distinct information, and the detailed return-field list and caveats are justified given the tool's complexity. Nothing feels redundant or padded.

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 annotations, parameter schema, and output-schema presence, the description fully covers when to call, what to pass, what to expect, and edge cases such as paid-plan fallback and private-group anonymisation. An agent has enough context to call the tool correctly without additional inference.

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 coverage is 100%, so the baseline is 3, but the description adds actionable meaning: slug is tied to the URL pattern with an example, and range's default/paid-plan/fallback behavior is spelled out with the instruction to check range.key. That goes beyond simply restating parameter names.

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 opens with a clear trigger: 'Call this when the user asks about... activity, health, leaderboard, mood, or trends of a specific Telegram group' and the title states the resource as 'public stats for one Telegram group'. This clearly distinguishes it from global-stats or group-listing tools.

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?

It explicitly says when to call the tool and scopes it to a specific group, which separates it from the global/listing siblings. It does not name the alternatives directly or state explicit when-not-to-use conditions, but the trigger phrasing and 'specific group' scope are strong enough to make intended use clear.

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

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

Annotations are minimal (readOnlyHint: true, openWorldHint: false). The description goes beyond that by disclosing important behavioral details: it filters case-insensitively, has a language matching nuance ('meaningful share'), only includes public groups (with a caveat about /s/ pages), and returns a `total_matches` field to indicate when there are more results than returned. It also explains language detection ('top language' vs admin-chosen). This is rich behavioral context that is not in the 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?

The description is comprehensive but a bit long. However, it is well-structured, front-loading the tool's purpose, then explaining each parameter's usage, and ending with output details. Every sentence adds value. The length is justified by the tool's complexity and the need to distinguish sibling usage, but it could be slightly tightened.

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 tool's moderate complexity (3 params, no required ones) and the existence of an output schema (which covers return structure), the description is complete. It explains the output fields (language mix, slug, title, etc.), the `total_matches` behavior, and the scope (public only). There's no missing information an agent would need to call it correctly.

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 coverage is 100%, and the schema descriptions are already detailed. The tool description adds further value by specifying that `query` filters over four fields, that `lang` is ISO 639-1 and explains the 'meaningful share' semantics, and that omitting both yields top groups by Limzo Score. This goes beyond the schema's param descriptions, which are already good but less detailed about the nuances.

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's purpose: to discover Telegram groups tracked by Limzo, with options to browse, filter by language, or find a group's slug for get_group_stats. It uses a specific verb-resource combination ('Call this to discover Telegram groups') and distinguishes its two main modes: browsing and searching. This sets it apart from siblings like get_global_stats and get_group_stats, which are about stats rather than discovery.

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?

The description explicitly explains when to use this tool versus alternatives: it mentions using get_group_stats as a follow-up after finding a group's slug, and it details when to use `query` vs `lang` vs neither. It also clarifies that it only lists public groups, which is a key exclusion criterion. This provides clear usage context and routing to siblings, leaving no ambiguity.

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