Collect the results of a previously started actor run. Use this when another tool returned status "pending" with a run_id: wait a few seconds, then call this with that run_id. Repeat until it returns results.
| Name | Required | Description | Default |
|---|---|---|---|
| run_id | Yes | The run_id returned by a tool whose status was "pending" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It reveals that the tool may return a pending status and that results may not be immediate, requiring repeated calls. However, it does not mention error handling, failure scenarios, or rate limits, limiting transparency.
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: the first defines the purpose, the second provides usage instructions. It is front-loaded and contains no extraneous words. Every sentence 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 simplicity of the tool (one parameter, polling behavior), the description covers basic purpose and usage. However, it lacks information about the return format or status codes when results are not ready. Since there is no output schema, this gap reduces completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the parameter description already explains it is a run_id from a pending tool. The description adds value by explaining the usage pattern (polling), which enhances the semantic context beyond the schema. The agent learns that the parameter is used in a loop.
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 purpose: 'Collect the results of a previously started actor run.' It uses a specific verb (Collect) and resource (results of actor run), and distinguishes from sibling tools which are primarily data gathering tools, making it clear this is a result retrieval/polling 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?
The description provides explicit when-to-use instructions: 'Use this when another tool returned status "pending" with a run_id.' It also gives a clear procedure: wait a few seconds, call with run_id, repeat until results. This fully guides the agent on how to employ the tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target unique data sources or specific actions (e.g., search_zillow vs. get_zillow_property_details are clearly sequential). A few LinkedIn-related tools (find_linkedin_candidates vs. search_linkedin_employees) have overlapping purposes but their descriptions clarify distinct use cases. Overall, confusion is minimal and descriptions resolve ambiguity.
All tools follow a consistent verb_noun pattern in snake_case, using verbs like search, get, find, scrape, analyze, lookup, resolve, and verify. The pattern is predictable across the entire set, making it easy for an agent to infer function from name.
With 32 tools, the server exceeds the 'too many' threshold of 25+. While the broad scope of web data mining justifies some diversity, the count is unwieldy and could overwhelm an agent's selection process. A smaller, more focused set per domain would improve coherence.
The toolset covers a wide range of data retrieval needs: company research, real estate, job listings, academic research, and government records. For a read-only data aggregation service, there are no major lifecycle gaps, though some subdomains like social media scraping only cover Reddit and LinkedIn, missing other platforms. Overall, it is reasonably complete for its stated purpose.
