List open research tasks
research_queueReturn bounded historical and interpretive tasks with context, required tools, and explicit success conditions.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
research_queueReturn bounded historical and interpretive tasks with context, required tools, and explicit success conditions.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this is read-only, idempotent, and non-destructive. The description adds some useful context by specifying that returned tasks include context, required tools, and explicit success conditions. It does not describe ordering, pagination, or empty behavior, but the annotations cover the key safety profile.
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 a single sentence with no filler. It immediately says what the tool returns and then lists the meaningful contents, which is exactly the right framing for a zero-parameter list operation.
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 no-parameter, read-only list tool, the description is mostly complete: it identifies the task type, the fields included, and lets the annotations convey safety behavior. It could be slightly more explicit about the 'open' nature of the queue or any limits on the list, but no critical information 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?
This tool takes zero parameters, so there are no parameter semantics to document. A no-parameter tool gets a baseline of 4, and the description correctly focuses on the return value instead.
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 and resource: it returns bounded historical and interpretive tasks and lists what those tasks contain (context, required tools, success conditions). It is clear about what the tool does, but it does not explicitly distinguish itself from sibling tools such as get_assignment, 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?
The description says what the tool returns but gives no guidance about when to use it versus alternatives. There are no stated exclusions, prerequisites, or 'rather than X' guidance, so an agent must infer the appropriate situation from the title and description alone.
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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