tgden — Telegram catalog & search

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

With no annotations provided, the description carries the burden of behavioral disclosure. It clearly states the ranking criterion (subscriber count) and lists the return fields (channel name, @username, subscriber count, language, description). It does not mention error handling, rate limits, or authentication, but for a simple read-only listing tool this is sufficient.

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 only two sentences long, front-loaded with the core behavior and immediately followed by usage examples and return fields. Every sentence earns its place with no redundant or vague wording.

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?

For a tool with only 2 parameters and no output schema, this description fully covers what the tool does, when to use it, and what it returns. The schema handles parameter details, making the combined set complete for straightforward usage.

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?

The input schema already provides full descriptions for both parameters ('category' with allowed values, 'limit' with min/max and default). The description adds no additional semantics beyond what the schema already covers, so a baseline score of 3 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's function: returning top Telegram channels ranked by subscribers for a given category. It distinguishes itself from sibling tools like search_telegram by focusing on category-specific ranking, making its purpose unambiguous.

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 'Use this when someone asks 'best telegram channels for [topic]' or 'top channels about [subject]',' giving clear trigger scenarios. However, it does not mention when not to use it or directly reference alternative sibling tools, leaving a slight gap in exclusion guidance.

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?

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It does specify the nature of the operation (retrieving a full record), but it does not disclose potential side effects, such as whether the channel must exist, what happens when a username is not found, or any rate limiting or authorization requirements. This is thinner than expected for a tool with no annotation support.

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, information-dense sentence that front-loads the core purpose ('Full record for one channel') and compresses the returned fields into a colon-separated list. There is no wasted text, and every word contributes to understanding the tool's function.

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?

For a one-parameter read-only lookup, the description is largely sufficient. It clearly identifies the input (username) and the output structure (subscribers, category, language, trust score, description). There is no output schema, so listing these fields is valuable. However, it omits edge-case behavior (e.g., nonexistent channel) and any prerequisites, though given the simplicity of the tool this is a minor gap. Overall, it feels complete for the tool's scope.

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?

The input schema has 100% coverage: the only parameter, 'username', is described as 'Channel @username (with or without @)'. The description's mention of '@username' aligns with the schema but adds no new semantic detail. Since the schema is complete and unambiguous, a baseline of 3 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 a specific verb-plus-resource: 'Full record for one channel by @username,' and enumerates the returned attributes (subscribers, category, language, trust score, description). This unambiguously distinguishes it from sibling tools like best_channels_by_category or market_listings, which serve different resources or purposes.

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 no explicit guidance on when to use this tool versus alternatives. It implies usage for fetching a single channel's details by username, but it does not mention when not to use it, any prerequisites, or reference sibling tools. Sibling names are present in context, but the description does not leverage them.

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?

No annotations exist, so the description must disclose behavior. It mentions freshness, source (Telegram), and filterability, but does not explicitly state it is read-only, nor describe pagination, rate limits, or output structure. This is a moderate coverage.

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 sentence conveys source, scope, and filters, but lists countries and types that already exist in the schema, causing mild redundancy. Still compact and front-loaded.

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?

For a simple listing tool, the description covers source, scope, and filters. But without annotations or output schema, it lacks an explicit read-only statement and return format details, leaving some uncertainty for an agent.

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 fully describes all three parameters with enums and default values. Description only repeats that filtering by country and type is possible, adding little beyond 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?

Description clearly states the tool browses fresh classified listings from Telegram groups across specified countries, with filtering by type and country. It distinguishes itself from sibling tools by focusing on marketplace ads rather than stats or generic post search.

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?

Provides clear context about the data source and filtering options, implying use for browsing classifieds. However, it does not explicitly mention when to use this over siblings like search_posts or market_stats, nor any exclusions.

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?

With no annotations, the description carries the burden of behavioral disclosure. It scopes the data source and suggests a read-only aggregation operation. However, it does not disclose return format, pagination, or any limitations, leaving some uncertainty for an agent.

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 sentence with no redundant content. It front-loads the core action ('Listing totals') and scopes it efficiently. There is no wasted language or unnecessary detail.

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?

No output schema exists, so the description needs to convey the response structure. It specifies grouping by country and type but does not state the exact shape or whether the response is a list or mapping. For a zero-parameter tool, this is a moderate gap in completeness.

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?

The tool has zero parameters, and the input schema is empty. The description adds context about the output dimensions (country and type), which is sufficient given the absence of parameters. This aligns with the baseline expectation for parameterless tools.

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 provides listing totals for the OTC marketplace, grouped by country and type. The specific country list and marketplace scope differentiate it from sibling tools like market_listings. However, 'Listing totals' is slightly ambiguous as a verb phrase, making it less crisp than it could be.

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?

No explicit guidance is given about when to choose this tool over siblings. The purpose statement implies it is for aggregated totals rather than raw listings, but no alternatives or exclusions are mentioned. There is no clear context about use cases or when not to use it.

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?

With no annotations, the description carries the transparency burden. It discloses the operation type (full-text search, implying read-only), the scope limitation (catalogued channels, 1k+ view posts), and what is returned. It does not mention ordering or pagination, but for a search tool this 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 sentence, front-loaded with the core functionality, followed by a succinct mention of return values. No redundant information or filler.

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?

For a simple tool with two parameters and no output schema, the description adequately covers the necessary context: what the tool searches, the constraints (catalogued, 1k+ views, recent), and what the response includes. No critical information is missing.

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?

The input schema has 100% description coverage for both parameters. The description adds no additional meaning beyond the schema; it does not explain query syntax or limit behavior. The baseline of 3 applies because the schema does the heavy lifting.

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 a specific action ('Full-text search') on a specific resource ('recent posts from catalogued channels (1k+ view posts)') and lists return items ('text snippets, channel and view counts'). It distinguishes from siblings like 'search_telegram' by narrowing to channel posts with a view threshold.

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 provides clear context for when to use this tool: searching recent posts from catalogued channels with at least 1k views. It does not explicitly mention alternatives or exclusions, but the scope is well-defined enough to infer appropriate usage relative to sibling tools.

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?

With no annotations, the description carries the full load and does disclose key traits: no auth/account requirement, public data source, and the kinds of values returned (subscriber counts, categories, trust scores). It says nothing about rate limits, result ordering, pagination behavior, or whether results are cached/stale, which matters for a tool capped at 25 results.

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?

Three tight sentences, front-loaded with what the tool searches and how large the corpus is, then the differentiating no-account fact, then the parameter hint. No filler and nothing redundant.

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?

For a read-only catalog search with no output schema, the description is nearly sufficient: it identifies the data source, the return fields, the auth model, and the filtering knob. It leaves result ordering and pagination/limits implicit, but those are minor for this tool's complexity.

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 schema already documents all three parameters including the type enum and limit bounds. The description adds only a small hint ('use type to restrict to one entity kind') that largely restates the schema, so the baseline 3 applies.

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?

States a specific verb and resource ('full-text search over the tgden catalog') and scopes it precisely with catalog size and entity types (channels, group chats, bots). It implicitly separates itself from search_posts and channel_stats by describing the catalog rather than posts or a single channel, but it never names a sibling to sharpen the boundary.

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?

Gives clear usage context: it works without a Telegram account and surfaces communities the user has NOT joined, which tells an agent when this discovery tool is the right pick over account-scoped tools. It offers no explicit exclusions or named alternatives, so the agent must still infer when search_posts or best_channels_by_category would be better.

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