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 already declare readOnlyHint=true, so the description's job was to add context, and it does: the PUBLIC-groups-only coverage rule, the Limzo Score ranking when all filters are omitted, and the total_matches signal for truncated results. The 'meaningful share of what members write' qualifier for the lang filter is behavioral nuance the structured data cannot convey. Nothing contradicts 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 dense but every clause earns its place -- purpose, param semantics, default behavior, coverage limitation, row contents, and truncation flag. It is front-loaded with the purpose and has no filler, though it sits near the upper bound of comfortable length for a 3-parameter tool.

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 that an output schema covers return values and annotations cover the read-only safety profile, the description supplies everything else an agent needs: when to call it, param semantics, defaults, and behavioral caveats like public-only coverage and total_matches. The only omission -- an explicit word about get_global_stats -- does not undermine correct invocation.

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 still adds real meaning: query 'filters case-insensitively over group title, username, slug, and description', lang is pinned to ISO 639-1 with a meaningful-share semantic and an example, and the omit-both default is spelled out. It enriches both filters rather than merely restating 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 states a specific verb and resource -- 'discover Telegram groups tracked by Limzo' -- and names its immediate use case of resolving a slug for get_group_stats, so an agent can place it in the workflow. It is clearly distinct from the sibling stats 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?

The description gives explicit contexts for calling it: browsing the directory, filtering by language, or finding a slug for get_group_stats, and even maps a natural-language request ('find active Persian/Spanish groups') to the lang parameter. It does not explicitly contrast itself with get_global_stats or state when to avoid it, so exclusions are left to inference.

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