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

No annotations are provided, so the description carries the full burden. It explicitly discloses the critical side effect: building a dataset will stop the crawler, de-register it from the network, and prevent further data collection. It also sets expectations by describing the 10-step build progress.

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 well-structured and front-loaded: purpose, usage trigger, warning, parameter list, return values, and example. Every section adds necessary value, and the critical de-registration warning is placed prominently.

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 4-parameter mutation tool with no annotations, this description is complete. It explains when to use the tool, how to obtain the required parameter, what side effects to expect, what will be returned, and provides a runnable example. An agent has enough information to invoke the tool correctly and decide whether it is safe to proceed.

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 0%, so the description must document all parameters, and it fully does. It defines crawler_id, max_rows, email, and redirect_url with their types, defaults, and purposes, and even tells the agent to source crawler_id from get_gravity_task_status's 'crawler_ids' field. The example further clarifies 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?

The description states a specific action and resource: 'Build a dataset from collected data before the 7-day task completion.' It clearly differentiates this from sibling tools like get_dataset_status or cancel_dataset, and the purpose is immediately understandable.

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 says when to use the tool: 'Use this when you have enough data and don't want to wait for the full collection period.' It does not name a specific alternative tool or state explicit when-not conditions, but the timing guidance and the warning about stopping the crawler provide solid usage context.

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 of disclosure. It mentions purge/cancel intent, but does not explain whether purge is irreversible, what permissions are required, what happens to an in-progress build, or other side effects. The generic 'Success or error message' return line adds little behavioral detail.

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 compact, front-loaded with the primary purpose, and organized into Parameters, Returns, and Example sections. There is no filler or redundant expansion beyond a helpful example.

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 tool with an output schema, the core invocation is covered. However, because there are no annotations and the operation includes a destructive 'purge', the missing side-effect and cancellation-semantics context leaves a clear gap.

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 0%, but the description compensates by defining dataset_id with its type, required status, meaning, and a concrete example. This adds meaningful context beyond the raw 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 first line states a specific resource and actions: 'Cancel a dataset build or purge a completed dataset.' It clearly identifies the target as a dataset, though it does not explicitly contrast with sibling tools like cancel_gravity_task, so it stops 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?

Usage context is implied by the resource: use this when a dataset build needs cancellation or a completed dataset needs purging. However, there is no explicit when/when-not guidance or mention of alternatives such as build_dataset, get_dataset_status, or cancel_gravity_task.

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 behavioral burden. It discloses the concrete effect 'stop data collection' and mentions success/error messages, but it does not cover idempotency, permissions, reversibility, whether already-completed tasks can be canceled, or what happens to data collected before cancellation.

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 compact and front-loaded with the core purpose. The parameter explanation, return line, and example each earn their place without redundancy or fluff.

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-required-parameter cancellation tool, the description is nearly complete: it names the effect, documents the parameter, gives an example, and mentions the return type. It lacks only a small amount of behavioral context, such as whether cancellation preserves collected data or requires special permissions, but an agent has enough to invoke 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 input schema has 0% description coverage, but the description documents the single parameter's purpose, marks it required, and provides a concrete example value. This fully compensates for the bare schema in this simple one-parameter case.

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 ('Cancel') with a specific resource ('a running Gravity task') and adds the effect 'stop data collection.' It is clearly distinguishable from siblings like create_gravity_task and get_gravity_task_status at a glance.

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 'a running Gravity task' implies that this tool is for tasks currently in progress, but it does not explicitly say when to prefer this over cancel_dataset or when not to use it. No alternatives or exclusions are mentioned.

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 carries the full burden. It discloses important asynchronous behavior: the task registers within 20 minutes, collects data for 7 days, sends an email notification, and returns a gravity_task_id. It could also mention costs, cancellation, or failure handling, but the key lifecycle behavior is 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?

The description is well-structured and front-loaded: purpose, usage distinction, lifecycle, parameter details, returns, and examples. There is minor redundancy—'up to 7 days' appears twice and collection duration is restated—but overall each section earns its place and the examples are valuable.

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 complex asynchronous collection tool with no annotations, the description covers purpose, when to use it, parameter semantics, behavior after invocation, and return value. The output schema exists, so return details need no further expansion. No critical information is missing for selecting and invoking the tool 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 0%, and the schema only describes tasks as an array of objects with additionalProperties true. The description fully compensates by documenting the task object structure, platform values ('x' or 'reddit'), topic constraints (#/$ prefix for X, r/ for Reddit), keyword filtering behavior, and all optional parameters with examples.

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 first sentence states a specific verb and resource: 'Create a Gravity task for large-scale data collection from X (Twitter) or Reddit.' It also differentiates this from query_on_demand_data by contrasting long-running collection (up to 7 days) with quick queries, so an agent can tell them apart immediately.

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 says when to use this tool: 'Use this for collecting large datasets over time (up to 7 days).' It also states an exclusion and routes to the alternative: 'For quick queries (up to 1000 results), use query_on_demand_data instead.' This is clear when/when-not 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, the description carries the burden of explaining behavior. It clearly describes progress steps, completion state, download URLs, and file metadata, giving a solid picture of how the tool behaves. It does not explicitly state that the operation is read-only or what happens if the build fails, but the status-oriented design is sufficiently 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?

The description is well organized with Purpose, Parameters, Returns, and Example sections. It is not overly long and each section adds useful information, though the Returns section partly restates the opening sentence about download links.

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 single-parameter status tool, the description covers the essential usage: the required parameter, the return values, and a full example. The existence of an output schema reduces the need to detail every field, and nothing critical is missing for 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?

The schema only provides the parameter name and type, but the description fully compensates by describing dataset_id as 'The ID of the dataset to check' and providing a concrete example. This makes the parameter's meaning and format clear despite 0% schema coverage.

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 ('Get') with a clear resource ('dataset build') and explicitly includes download links. It distinguishes itself from sibling tools like get_gravity_task_status by focusing on dataset build status. The example reinforces the tool's purpose.

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 implies this tool is for checking build status and fetching download links, but it does not explicitly say when to use it versus alternatives like build_dataset, cancel_dataset, or get_gravity_task_status. The usage context is inferred rather than stated.

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 carries the full burden. It clearly indicates this is a read-style operation ('Get the status') and details the conditional behavior of include_crawlers. It doesn't mention auth, rate limits, or error cases, but for a simple status getter the disclosed behavior 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 well-organized with clear Parameters, Returns, and Example sections. Each section earns its place, and the example clarifies real usage without redundancy or fluff.

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?

Despite no annotations and a minimal schema, the description provides all necessary operational context: how to invoke it, what the include_crawlers flag changes, what output to expect, and how the result connects to build_dataset. An agent can confidently select and 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 description coverage is 0%, and the description fully compensates. It explains what gravity_task_id refers to, gives the default for include_crawlers, and states the concrete effect of setting it to True (seeing records_collected and bytes_collected per crawler).

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 names a specific verb ('Get'), a specific resource ('Gravity task'), and the key insight ('how much data has been collected'). It clearly distinguishes from siblings like get_dataset_status by focusing on Gravity task status.

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 context by explaining what the tool returns, and even flags that crawler IDs are needed for build_dataset, implying workflow usage. However, it does not explicitly state when to prefer this tool over alternatives like get_dataset_status or how it relates to create_gravity_task/cancel_gravity_task.

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 burden and excels at it. It discloses default date behavior (last 24 hours), limit range and default, keyword_mode semantics, return envelope contents, UTC timestamps, and platform-specific constraints such as username filtering being unavailable for Reddit. No behavioral contradictions or hidden surprises remain.

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 well-organized and front-loaded with the most important constraint, but it is somewhat repetitive: the 'Default Behavior' section restates date defaults already given under start_date and end_date, and the examples are more numerous than strictly necessary. Still, each section has a clear purpose and the overall structure aids comprehension.

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, 7 parameters, no annotations, and 0% schema coverage, the description provides everything needed for correct invocation: required values, supported platforms, parameter semantics, default behavior, return format, and platform-specific limitations. The presence of an output schema further reduces the need to document return values in detail.

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 0%, but the description fully compensates by explaining every parameter: accepted values for source, username limits and @ handling, keyword interpretation per platform, date formats and defaults, limit range, and keyword_mode options. This goes far beyond the bare schema definitions.

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 identifies a specific action ('Fetch real-time social media data') and a specific resource ('X and Reddit through the Macrocosmos SN13 network'), and clarifies the exact data platforms involved. It is clearly distinguishable from the sibling tools, which all concern task/dataset creation and cancellation.

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 strong usage context: required source values, case sensitivity, platform-specific capabilities, and multiple concrete invocation examples. It does not explicitly contrast this tool with alternatives, but the sibling tools are unrelated in function, so this is not a significant gap.

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