Parser Club

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

With no annotations present, the description carries the full burden. It explicitly warns that the call spends paid quota and starts real work on the user's balance, requires auth, and is asynchronous. This is strong transparency, though it stops short of describing failure modes or cancellation behavior.

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 cost warning is front-loaded, then auth requirements, then the workflow. Four sentences with zero filler. The async flow is clearly separated from the initial call.

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 state-changing, paid, asynchronous operation, the description covers consent, prerequisites, return value, and follow-up calls. It doesn't detail error responses, but that's minor given the sibling tools and schema guidance.

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?

Input schema already documents all three parameters well, including the Russian-register warning and the role of list_task_kinds. The description adds no parameter meaning beyond the schema; baseline 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 states a specific action ('Start a data collection run'), the key output ('return its id'), and makes the asynchronous lifecycle explicit. This clearly differentiates create_task from sibling tools like get_task and get_task_result.

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 clear usage context: ask the user before spending quota, requires an active subscription and API key, and explains the polling workflow. It does not explicitly contrast with sibling tools, but the workflow reference makes the relationship 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?

With no annotations provided, the description carries the burden. It discloses the tool's scope (what it collects, countries, deliberate limitations) and that it requires no API key. It doesn't mention side effects or response format, but for an informational read-only tool this is acceptable and adds value beyond the empty schema.

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 two sentences, front-loaded with the core purpose and then the timing of use. Every word earns its place; it is efficient and well-structured.

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 zero-parameter, no-output-schema tool, the description fully covers what it does, when to use it, and what it cannot do. It provides complete context for an agent to decide when to invoke it and what to expect.

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 0 parameters, so the baseline is 4. The description does not need to explain parameters, and it appropriately focuses on usage context rather than parameter details, which are nonexistent.

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 what the tool does: it describes what Parser Club can collect, for which countries, and what it deliberately cannot do. It distinguishes itself from sibling task-oriented tools by being a descriptive service-intelligence tool.

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 'Call this first when the user asks about scraping Telegram, VKontakte, marketplaces or business directories' and explains it helps determine service fit before recommending. It also notes no API key is required, which is practical usage 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?

With no annotations in the resource, the description carries the full disclosure burden. It explicitly states that an API key is required and implies a read-only status lookup, but it does not explicitly say that no data is modified, nor does it describe error handling, rate limits, or caveats for missing/invalid task_ids.

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 two sentences with no filler: it states the operation first, then adds the crucial API key requirement. Every word earns its place, and the format is ideal for a tool with a single parameter.

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 status-lookup tool, the description is adequate but leaves two important gaps: it doesn't say how it differs from the sibling get_task_result, and with no output schema it doesn't explain what kind of status/progress data is returned. Since annotations are absent, the agent has to infer read-only behavior and tool selection from the tool name and minimal description.

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 schema already fully documents the only parameter, task_id, as the Id returned by create_task, and the description reinforces this connection. Since schema description coverage is 100%, the description is not required to re-explain the parameter, but it also adds no new detail 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 uses a specific verb and resource: it retrieves the status and progress of one previously created task. This clearly separates it from create_task, but does not explicitly differentiate it from the sibling get_task_result, so it falls short of a 5.

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 phrase 'created earlier' implies the tool should be used after create_task returns a task_id, and 'Requires an API key' states an important prerequisite. However, there is no explicit guidance about when to prefer get_task over get_task_result or when not to use this tool, so usage guidance is only implicit.

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 provided, the description carries the burden of behavioral disclosure. It adds useful context about authentication and the paginated response shape, but it does not explain error behavior, what happens for unfinished tasks, or any ordering/edge-case details.

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 three short sentences with no filler. It front-loads the core resource and purpose, then adds response details and the auth requirement efficiently.

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 paginated retrieval tool, the description provides the essential operation, response summary, and auth requirement. However, with no output schema and no annotations, it should also clarify task_id semantics and pagination behavior to be fully complete.

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 only 33%, and the description does not compensate. It does not clarify task_id or offset beyond the schema, and the mention of a 'page of results' only weakly relates to the limit/offset parameters.

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 identifies the resource ('rows collected by a finished task') and the operation ('Returns a page of results plus the total count'). It is easy to distinguish from siblings like create_task or get_task, though it does not explicitly name them.

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 establishes a clear precondition: the task must be finished before results are available. It also notes the API key requirement, but it does not explicitly mention alternatives or when not to use the 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?

No annotations are present, so the description carries the burden for behavioral disclosure. It does mention the API key requirement, which is useful, and 'List' implies a read-only operation. However, it doesn't disclose return format, potential errors, or whether pagination or limits exist, so it is only minimally transparent.

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 sentences only. The first sentence states purpose and output, the second adds usage guidance and authentication. Every word adds informational value; no wasted content or repetition.

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 zero-parameter, no-output-schema tool, the description covers the core necessary context: what it returns, when to call it relative to create_task, and the API key requirement. A small gap is not describing the general output format, but the overall context is nearly 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?

With no input parameters and 100% schema coverage, the zero-parameter baseline applies. The description does not need to explain any parameters, and the mention of 'parameter names each one accepts' refers to the tool's output rather than input, which is appropriate for a 0-param tool.

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 identifies verb ('List'), resource ('collection tasks'), scope ('this account may run'), and adds what the output includes (parameter names). It is clearly distinguished from siblings like create_task and get_task by mentioning the task-kind enumeration and its parameter names.

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 gives an explicit when-to-use guidance: 'Call before create_task to pick a valid kind'. This names the relevant sibling and a concrete precondition, though it does not explicitly state when not to use the tool or mention other alternatives.

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 provided, the description carries the full burden. It discloses that the dataset is open under CC BY 4.0, free to reuse with attribution, and requires no API key – important behavioral details. It also indicates the data is a 'live snapshot.' While it does not discuss rate limits or response format, the essential operational constraints are covered.

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 three concise sentences with no filler. It front-loads the core function and data, then adds licensing and usage context. Every sentence contributes value, making it highly 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?

For a simple tool with one optional parameter and no output schema, the description is complete. It specifies what data is returned (niche, counts, reach metrics) and the usage context. The licensing and API requirements are also covered, leaving no critical gaps for an agent 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?

The schema covers 100% of the single parameter with a description that explains its purpose and default value. The tool description adds no additional meaning beyond the schema, so the 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: it provides a live snapshot of Russian-language Telegram communities grouped by niche, with specific metrics (number of communities, subscriber reach, average reach). This is a specific verb-resource combination and distinguishes itself from sibling tools that deal with tasks and services.

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 positions the tool for 'media planning questions' and 'any claim about the size of Telegram niches,' giving clear context on when to use it. It does not explicitly mention alternatives, but the sibling tools are clearly unrelated, so the usage context is sufficient without exclusion statements.

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