autosearch_get_run_status
Get the current status of a research run. Returns status, frontier_score, n_experiments, summary.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| run_id | Yes |
autosearch
by uk.exatoshi
TDQS
B3/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must fully disclose behavior. It states return fields but does not mention idempotency, side effects (none), or potential errors. Minimal behavioral context.
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?
Two sentences with no wasted words. Efficient and front-loaded with key action and return information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only tool with one parameter and no output schema, the description is adequate but minimal. Could add note about required run_id existence or that it does not mutate state.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and description adds no meaning to run_id. No hint about format, source, or validation rules. Parameter semantics are entirely absent.
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 specifies the verb 'Get' and resource 'status of a research run'. It distinguishes from siblings like autosearch_get_run_paper (gets paper) and autosearch_list_my_runs (lists runs).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives. Missing when-not criteria or context for choosing get_run_status over get_run_paper or list_my_runs.
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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TDQS
A3.7/5.0
Disambiguation5/5
Each tool has a distinct and clearly separated purpose: launching, status checking, listing runs, retrieving papers, searching evidence, and verifying DOIs. No two tools overlap in functionality.
Naming Consistency5/5
All tools follow a consistent 'autosearch_verb_noun' pattern in snake_case, with verbs like get, list, run, search, verify. The prefix is uniform, making the set predictable.
Tool Count5/5
With 6 tools, the server is well-scoped. It covers the essential operations for AI research automation without being excessive or insufficient.
Completeness4/5
The tools cover the core workflow of launching, monitoring, retrieving results, and auxiliary searches. Missing operations like canceling or deleting runs, but the surface is largely complete for typical use.