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

With destructiveHint and idempotentHint annotations already declaring the destructive and idempotent nature, the description adds specific behavioral details: it aborts only starting/running runs, returns updated run details, and explains the graceful option with a 30-second timeout. This goes beyond the annotations without contradicting them, though it could mention edge cases like double-abort 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 description is well-structured: the main purpose is stated first, followed by behavioral details, then usage guidelines and examples. It is concise but includes an unnecessary repetition of 'Abort' in the USAGE line. The examples are helpful and the overall length is appropriate for the tool's complexity.

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 simplicity (2 params, one required), the description is complete: it explains the core action, the conditions under which it has no effect, the return of updated run details, the graceful option, and provides concrete examples. The presence of an output schema means the description need not detail return fields. 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?

Both parameters (runId and gracefully) are fully described in the schema (100% coverage), and the description repeats the graceful behavior without adding new information beyond the schema. Since the schema already explains the parameters thoroughly, the description provides no extra value here, warranting the baseline score of 3.

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 aborts an Actor run that is starting or running, and specifies that it has no effect on finished runs (SUCCEEDED, FAILED, ABORTING, ABORTED, TIMED-OUT). This precisely distinguishes it from sibling tools like get-actor-run (read-only) and call-actor (starts a run), leaving no ambiguity about the action and resource.

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 USAGE section explicitly says to use it when a run is 'taking too long or misconfigured,' and the no-effect statement implicitly warns against using it on already-finished runs. However, it does not explicitly name alternative tools (e.g., get-actor-run for checking status), so it falls short of the 'explicit alternatives' criterion for a 5.

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?

The description discloses important runtime behavior beyond the annotations: it waits up to waitSecs (default 30), returns run status and storage IDs, reports dataset field metadata when waitSecs > 0, and provides nextStep polling instructions for non-terminal runs. This goes well beyond the simple destructiveHint/openWorldHint annotations and helps an agent predict execution semantics.

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 with sections, but it repeats the dedicated-tool guidance in both the IMPORTANT block and the USAGE block, adding unnecessary length. The example is useful, but the duplication and verbose formatting could be tightened without losing 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?

For a complex dynamic-execution tool, the description is remarkably complete: it covers the prerequisite fetch-actor-details step, name resolution, MCP server usage, wait/result retrieval behavior, polling instructions, and explicit selection criteria against siblings. The presence of an output schema further reduces the need to document return values, and nothing critical appears 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?

Because schema description coverage is 100%, the baseline is 3, but the description adds meaningful semantics: it explains when to use waitSecs: 0 for long-running actors, clarifies that input should conform to the schema fetched via fetch-actor-details, and elaborates on callOptions like maxItems and maxTotalChargeUsd as pay-per-result/event caps. This supplements the schema with actionable usage context.

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 specific verb+resource statement, 'Call any Actor from the Apify Store,' and then differentiates itself from dedicated Actor tools and siblings like fetch-actor-details and search-actors. The MCP server format and the actor-name format are both specified, making the tool's 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?

Explicit guidance is given for when to use this tool versus alternatives: 'Always use dedicated tools when available' and 'Use the generic call-actor tool only if a dedicated tool does not exist.' It also tells the agent to use search-actors to resolve malformed names and fetch-actor-details to obtain the input schema before calling.

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 indicate read-only, idempotent, non-destructive behavior. The description adds useful details beyond that: default output fields, readme fallback behavior, and how the output parameter controls response content. No contradictions found.

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?

Well-structured and front-loaded with purpose, then usage constraints, output explanation, and examples. Slightly long but every section serves a clear function; no 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?

Given the nested output object with many flags, the description covers defaults, common patterns, and usage scenarios comprehensively. The output schema exists, so return values are well-specified. Agent has everything needed to call 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% with detailed parameter descriptions. The description adds extra value by explaining default behavior, common patterns, and examples, which goes beyond the schema's structure.

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 retrieves detailed Actor information by ID or full name, with a specific format example. It differentiates from siblings like search-actors and call-actor by focusing on detail retrieval rather than searching or execution.

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 instructs when to use this tool vs. search-actors, warns against constructing plausible names, and provides concrete usage scenarios (docs, input schema, MCP tools). This is exceptionally actionable 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, so the safety profile is clear. The description adds that it fetches 'full content', which is consistent but does not disclose any additional behavioral traits (e.g., rate limits, auth). Since annotations carry the burden, a score of 3 is appropriate.

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 concise, with two main sentences plus a usage section and examples. It is well-structured and front-loaded, containing no unnecessary words. Every part earns its place.

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 has a single parameter, an output schema (so return values are documented), and good annotations, the description is adequately complete. It explains when to use and provides examples. A slight improvement could be mentioning expected behavior for invalid URLs, but not required.

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 one parameter 'url' with a description, and schema description coverage is 100%. The tool description does not add additional semantic meaning beyond what the schema already provides, meeting the baseline of 3.

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 fetches the full content of an Apify or Crawlee documentation page by URL. The verb 'fetch' combined with the specific resource 'docs page' provides a clear purpose, and it distinguishes from sibling tool 'search-apify-docs' which searches rather than fetches.

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 to use this after finding a relevant page with 'search-apify-docs', providing context for when to use. It also includes usage examples. However, it does not explicitly state when not to use the tool, which would be helpful.

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 readOnly/idempotent annotations, the description discloses key behaviors: waitSecs blocks up to a cap, summary describes past execution, and nextStep prescribes a follow-up action with interpolated identifiers. It also clarifies that storages are returned as an alias map, giving the agent a realistic picture of the response.

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 a front-loaded purpose, a structured return summary, a dedicated USAGE section, and compact examples. Every sentence contributes either behavioral detail or invocation guidance, with no filler or redundancy.

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 simple two-parameter schema and the presence of an output schema, this description is complete: it explains what the tool returns, how waitSecs affects behavior, how summary and nextStep should be interpreted, and when to invoke it. An agent has everything needed 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 description coverage is 100%, so the runId and waitSecs parameters are already fully documented. The description adds a small amount of practical framing — "Pass waitSecs > 0 to block until terminal" — and concrete examples, but it mostly restates information already present in the input 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 — "Get detailed information about a specific Actor run" — and then lists the exact result fields. It clearly distinguishes this from siblings like get-dataset-items or fetch-actor-details by centering on run status and run-level metadata.

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 USAGE section explicitly says to use this tool to check the status of a run started by an Actor-running tool and explains when to use waitSecs. It does not explicitly name sibling alternatives or state when not to use this tool, so it falls short of full exclusion-based 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 annotations already declaring readOnlyHint=true and idempotentHint=true, the description adds valuable behavioral context: returns rows themselves, default limit of 20, and clean=true behavior. It also clarifies the nature of the return value beyond the 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?

Well-structured with clear USAGE and USAGE EXAMPLES sections. Some redundancy ('Get items (rows)...' and 'Returns the rows themselves...') but overall efficient and front-loaded with purpose.

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 has 8 parameters, an output schema, and rich annotations, the description adequately covers key behaviors, usage triggers, and examples. It doesn't explain every parameter but relies on the schema for that, which is appropriate.

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 baseline is 3. The description adds extra meaning by explaining default limit (20) and the clean=true shortcut, which supplements the schema. It also provides examples for fields usage, though most parameter semantics are in 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?

Description clearly states the tool gets items (rows) from a dataset and explicitly distinguishes it from metadata, counts, or schema. It names the exact resource (dataset items) and the action (get), and provides direct trigger phrases for when to use 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?

Provides explicit use cases ('when the user provides a datasetId and asks to retrieve results...'), and clarifies what it is not for (metadata, counts, schema). However, it does not name alternative tools explicitly, only implies 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?

Annotations already establish readOnlyHint, idempotentHint, and destructiveHint as false. The description adds valuable behavioral context beyond this: the exact key name requirement and the preservation of Content-Encoding with automatic client decompression. No contradictions with 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 well-structured with a concise opening definition, a short behavioral note, and clear usage examples. Every section earns its place without unnecessary 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 simple two-parameter read-only tool with full schema coverage, an output schema, and strong annotations, the description covers the essential context: how to target a record, what to expect (Content-Encoding), and when it applies. The sibling context also helps differentiate it from listing operations.

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 coverage is 100%, so the schema already documents both parameters. The description adds usage examples and the 'exact key name' requirement, but these are minimally additive beyond the schema's own property descriptions.

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: 'Get the value stored under a specific key in a key-value store.' It also distinguishes itself by explicitly stating this is 'a single record, not a listing of all keys,' which separates it from sibling tools like get-dataset-items.

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 a clear usage context: 'Use when you need to retrieve a specific record (JSON, text, or binary) from a store.' It does not name alternative tools explicitly, but the 'not a listing of all keys' phrase gives implicit exclusion guidance for listing-style operations.

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 provide no behavioral hints (all false), so the description carries the burden. It discloses that the report is sent to the Apify team and includes a clear warning: 'Do NOT include personal data, credentials, secrets, or verbatim private conversation content.' This adds meaningful context beyond the schema/annotations. However, it doesn't detail any side effects or confirmation behavior, which is a minor gap.

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 front-loaded with the purpose, followed by clear bullet-point usage conditions and a concise instruction. Each sentence earns its place without unnecessary fluff, staying around 80 words while conveying all key 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?

For a simple reporting tool with high schema coverage and an output schema present, the description covers the essential context: what the tool does, when to invoke it, and how to safely compose the message. It does not need to explain the return value because the output schema would cover that, and no additional caveats are required for this use case.

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 describes all four parameters with 100% coverage, giving a baseline of 3. The description adds semantic value by explaining how to compose the required message: 'Put what you were doing and what went wrong in "message"' and by instructing users to avoid sensitive data. The optional parameters are not elaborated, but the schema covers them sufficiently.

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 'Report a problem with Apify's MCP tools or Actors to the Apify team.' This is a specific verb+resource and distinguishes it from sibling tools that focus on searching, calling, or fetching actors/data. It leaves no ambiguity about 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?

It explicitly lists conditions: 'Call it when: A tool or Actor is missing, errors, times out, or returns a confusing, wrong, or empty result. You cannot complete the user's request with the available tools.' This provides clear when-to-use guidance and implies alternatives are the operational tools. It also instructs what to include in the message.

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, idempotentHint, and destructiveHint false, but the description adds valuable context: it returns only summary info (not full docs), excludes rental/restricted Actors, and lists exact output fields. No contradiction with annotations; description complements them with behavioral nuances.

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?

Though lengthy, every sentence adds value: clear sections for usage, limitations, and returns. Bullet points for returned data and examples for keyword usage make it scannable and actionable. The structure front-loads the core purpose and differentiators.

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 nuanced keyword semantics and output schema, the description is thorough: it covers what it returns, its limitations, alternatives, and explicit keyword guidelines. The output schema is present, so not having to explain return values, but description still lists all fields for clarity. Highly 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 already covers all 3 parameters (100% coverage), but the description significantly enhances keyword semantics with detailed rules, examples, and anti-patterns (e.g., 'Do NOT use ranking words'). Also clarifies default behavior for empty keywords and sort order, adding substantial value 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 clearly states the tool searches the Apify Store to discover scraping tools/Actors. It explicitly distinguishes from related tools by stating 'does NOT retrieve actual data' and mentions fetch-actor-details and apify/rag-web-browser as alternatives, providing clear differentiation from siblings.

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 explicit when-to-use scenarios (e.g., 'What tools can scrape Instagram?') and when-not-to-use (immediate data retrieval) with specific alternative tool (apify/rag-web-browser). Also includes usage strategies like broad keywords and mandatory two searches.

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?

The description discloses that the search returns only a limited content snippet and that full content must be fetched separately, which is important behavioral information. It also notes that it will not search the Apify Store, a limitation not present in annotations. While annotations already cover read-only and idempotent nature, the description adds useful context about output and limitations.

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 moderately concise, using bullet points for docSource and separating guidance for fetching full content. It has some redundancy in repeating the docSource details but is overall well-structured and not excessively verbose.

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 description provides sufficient context given the tool's simplicity, including output expectations (URL and content snippet) and a mention of the fetch-apify-docs tool for full content. It covers the main usage scenarios and does not leave major gaps in understanding.

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 provides comprehensive descriptions for all parameters, including the query, limit, offset, and docSource with enums. The tool description reiterates the docSource options but adds little additional semantic meaning beyond the schema. Since coverage is 100%, the parameter semantics are well-covered by the schema alone.

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: 'Search Apify and Crawlee documentation using full-text search.' It also distinguishes the scope from the Apify Store and provides a clear verb-object structure. This effectively communicates the tool's purpose and differentiates it from sibling tools like search-actors.

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 explicit usage instructions, such as 'You must explicitly select which documentation source to search using the docSource parameter' and recommends using fetch-apify-docs for retrieving full content. It also advises against searching the Apify Store unless specifically asked, guiding when to use this tool versus alternatives.

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