get_research_prompt

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description bears full burden. It states the tool reads research files and returns a prompt, implying read-only behavior but no explicit mention of side effects, auth, or rate limits. Adequate but not comprehensive.

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?

Two paragraphs plus args list; front-loaded with main purpose. Every sentence adds value without repetition. Efficient use of space.

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 tool complexity (generating a prompt), the description covers inputs, what the prompt extracts, and the follow-up action. Output schema exists but is not shown; description compensates by explaining expected prompt content. Minor gap: does not clarify if prompt is returned as a string or object.

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 0%, but the description adds meaningful context for both parameters: project_path is 'Absolute path to the project directory' and source_files is 'Optional list of specific file names to analyze.' Adds value beyond the schema structure.

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?

Description starts with a clear verb+resource: 'Generate an analysis prompt from research data.' It specifies inputs and outputs, and distinguishes from sibling tools like get_generate_prompt by mentioning the workflow to call save_patterns afterward.

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?

Provides explicit workflow: use this to generate a prompt, process it with an LLM, then call save_patterns. Does not state when not to use or compare to alternatives, but the context (siblings) and description imply usage scope.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.