actors-mcp-server

Official

by apify

Overview Schema Related Servers Score Discussions

TypeScript

Hybrid

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.15.3
Disambiguation5/5
Each tool targets a distinct resource and action: actor discovery, documentation search, actor details, running actors, run status, dataset items, key-value store records, aborting runs, and problem reporting. Shared verbs like search/fetch/get are always disambiguated by their noun objects.
Naming Consistency5/5
All tools follow a consistent lowercase hyphenated verb-object pattern: search-* for discovery, fetch-* for full-content retrieval, get-* for reading specific resources, and call/abort/report for actions. There is no mixing of naming conventions or vague generic verbs.
Tool Count5/5
Ten tools is well-scoped for an Apify Actors server. It covers discovery, configuration, execution, monitoring, output retrieval, docs access, and support without feeling redundant or bloated.
Completeness5/5
The toolset covers the full Actor lifecycle: search the store, inspect input schemas, run an Actor, check or abort the run, and retrieve results from datasets or key-value stores. Documentation search/fetch and problem reporting round out the surface with no obvious dead ends.
Average 4.5/5 across 10 of 10 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 88 of 112 community issues answered or closed in the last 6 months
- No commit activity data available
- Last stable release on August 26, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
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.
Conciseness5/5
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.
Completeness4/5
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.
Parameters3/5
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.
Purpose5/5
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.
Usage Guidelines4/5
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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructive and idempotent behavior, and the description adds useful context: no effect on terminal statuses and that the result includes updated run details. This goes beyond the structured annotations without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear core statement, a short usage guideline, and relevant examples. It is not overly verbose, though the examples are somewhat redundant with the schema and could be trimmed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the two simple parameters, an output schema, and annotations covering destructiveness and idempotency, the description provides sufficient context: when it works, when it does nothing, and what the response includes. It is complete for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the description does not need to explain parameters in depth. The gracefully parameter's 30-second timeout is already in the schema, and the description adds only usage examples rather than new semantic detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
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 currently starting or running, using a specific verb and resource. It also distinguishes itself from siblings by specifying the lifecycle states it applies to and the no-op statuses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says to use it when a run is taking too long or is misconfigured, and it lists terminal statuses where the call has no effect. It does not explicitly name alternative tools, but the usage context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only and idempotent. The description adds valuable context on the output parameter's default behavior (all fields true except mcpTools) and the readme field's fallback logic (summary when available, otherwise full README). No contradiction 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized and front-loaded, with a brief main purpose followed by parameter guidance and examples. Every sentence contributes, though the examples section could be trimmed, but it is not redundant.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the nested output object and an existing output schema, the description adequately covers input semantics and provides examples. It doesn't over-explain return values since output schema exists. Minor gap: it doesn't mention that output is a nested JSON object, but the schema handles that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description enriches the output parameter with usage patterns (e.g., 'inputSchema only', 'mcpTools for MCP Actors') and clarifies the readme field's behavior, going beyond the schema's boolean descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb+resource: 'Get detailed information about an Actor by its ID or full name.' It clearly distinguishes from siblings like call-actor (executes) and get-actor-run (retrieves runs), and provides explicit examples of the resource type (e.g., 'apify/rag-web-browser').
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states specific use cases: 'Use when querying Actor details, documentation, input requirements, or MCP tools.' It gives concrete examples but does not explicitly mention alternatives like search-actors for finding actors, so it lacks a formal when-not/alternative statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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.
Conciseness4/5
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.
Completeness4/5
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.
Parameters4/5
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.
Purpose5/5
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.
Usage Guidelines4/5
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.
Behavior4/5
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.
Conciseness5/5
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.
Completeness5/5
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.
Parameters3/5
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.
Purpose5/5
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.
Usage Guidelines4/5
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.
Behavior4/5
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.
Conciseness4/5
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.
Completeness4/5
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.
Parameters3/5
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.
Purpose5/5
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.
Usage Guidelines5/5
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.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as readOnly and idempotent. The description adds significant context beyond that: it explains the return fields (summary describes the past, nextStep prescribes a follow-up), and details the waitSecs behavior including its range and default. This enriches the agent's understanding of side effects and output semantics without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a summary, return explanation, USAGE section, and examples. It is front-loaded and avoids fluff, but the USAGE section partially repeats waitSecs information already covered in the first paragraph, adding slight redundancy. Overall, it is concise and easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read tool with two fully documented parameters and an output schema available, the description covers essential aspects: what it returns, the wait behavior, and usage examples. It explains key return fields (summary, nextStep) that the output schema might not elaborate. While it could mention pagination or error handling, those are likely covered by the output schema, so this is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters. The description adds value by contextualizing waitSecs as 'block until terminal (or until the cap elapses)' and providing examples, but it primarily re-emphasizes schema details. Given high schema coverage, a baseline of 3 applies, and the description's additional usage hints justify a 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool gets detailed information about a specific Actor run, with a specific verb and resource. It distinguishes from siblings like abort-actor-run (mutation) and call-actor (starting a run) by focusing on read-only retrieval. The mention of return contents (status, storages, stats, etc.) further clarifies its purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The USAGE section explicitly says to use it to check the status of a run started by any Actor-running tool, and provides guidance on using waitSecs to block for terminal status. It also includes two usage examples covering typical scenarios. While it doesn't explicitly mention alternatives, the sibling list (e.g., abort-actor-run) and the read-only annotation make the intended use clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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.
Conciseness5/5
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.
Completeness5/5
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.
Parameters4/5
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.
Purpose5/5
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.
Usage Guidelines5/5
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.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=false, destructiveHint=true, openWorldHint=true, idempotentHint=false, and the description adds substantial context beyond them: waitSecs blocking behavior with non-terminal nextStep polling instructions, return of run status and storage IDs, dataset field metadata reporting when waitSecs > 0, memory quota rejection retry advice, and pay-per-result/event charge caps in callOptions. This is rich behavioral disclosure consistent with 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is lengthy but well-structured with WORKFLOW, IMPORTANT, USAGE, and EXAMPLES headers. There is some redundancy: the 'There are two ways to run Actors' paragraph repeats the 'always use dedicated tools' and 'only if no dedicated tool exists' guidance that appears again in USAGE. Slight trimming would help, but every section earns its place given the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 4 parameters (one a nested object), an output schema, and complex runtime behavior, the description is remarkably complete. It covers prerequisites, MCP server actor invocation format, waiting semantics, result retrieval flow, polling for long runs, and edge cases like name resolution. With an output schema present, not explaining return values is acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds meaningful value beyond the schema: practical waitSecs guidance ('Default 30s returns results for fast actors. Use waitSecs: 0 to start and return immediately'), the workflow hint to fetch the input schema before calling, and the requirement to resolve non-'username/name' actor names via search-actors. It doesn't exhaustively document every callOptions field, but the schema already covers those.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
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.' It clearly distinguishes itself from siblings by positioning dedicated tools as the preferred alternative and this tool as the generic dynamic path. References to fetch-actor-details, search-actors, and get-dataset-items for related workflow steps further differentiate it.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Extremely explicit guidance: 'Always use dedicated tools when available' and 'Use the generic call-actor tool only if a dedicated tool does not exist for your Actor.' It also names the exact alternative for each step (fetch-actor-details for schemas, search-actors for name resolution, get-dataset-items for results) and gives a concrete when-to-use example (dynamically running any Actor without dynamic tool registration).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
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.
Conciseness5/5
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.
Completeness5/5
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.
Parameters5/5
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.
Purpose5/5
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.
Usage Guidelines5/5
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.

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