Get follow-up answers
get_deep_threadQuestions asked about a deep research run and the committee's answers.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| analysis_id | Yes | Deep research id |
get_deep_threadQuestions asked about a deep research run and the committee's answers.
| Name | Required | Description | Default |
|---|---|---|---|
| analysis_id | Yes | Deep research id |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds that the payload is the questions and the committee's answers, which is useful content context, but says nothing about ordering, pagination, or behavior when no follow-ups exist. Some value added beyond annotations, hence a 3.
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?
A single short sentence with no filler, and the key content ('questions' and 'answers') is front-loaded. It is a fragment without an explicit verb, but it wastes nothing.
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 one-parameter read tool with no output schema and full annotation coverage, the description is minimally sufficient but skips usage routing and return-shape detail (no pagination or ordering). It tells the agent what the data is but not how to decide to call it or what to expect structurally.
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% and the single analysis_id parameter is documented in the schema as 'Deep research id'. The description adds no format, source, or semantics beyond what the schema provides, so the baseline 3 applies.
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 reads as a noun phrase ('Questions asked about a deep research run and the committee's answers') rather than a specific verb+resource, so it only implicitly conveys that it retrieves a follow-up Q&A thread. It does identify the resource content, but gives no differentiation from siblings like get_deep_analysis or ask_deep_analysis. Adequate but vague on the action.
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?
There is no statement of when to use this tool versus alternatives. The presence of ask_deep_analysis (which likely submits questions) creates a natural contrast the description never addresses. Context is entirely absent.
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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