search_questionsSearch AI Engineer and Investment Banking interview questions and model answers by keyword to find relevant practice material; optionally include answers for review.
Keyword search across questions and model answers, e.g. 'KV cache', 'accretion dilution', 'prompt injection'.
| Name | Required | Description | Default |
|---|---|---|---|
| n | No | Max results | |
| role | No | Limit to one role | |
| query | Yes | Keywords | |
| include_answers | No | Include model answers |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the description isn't required to cover safety. It usefully adds that this is keyword matching (not semantic search), but it says 'across questions and model answers' without noting that answers are only included when include_answers=true (default false), which could mislead about return content.
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 front-loaded sentence with zero filler; the examples earn their place by illustrating query shape.
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?
Adequate for a simple search tool with fully documented params and no output schema, but it omits result-size behavior (n defaults to 5, max 20) and, more importantly, the conditional nature of answer inclusion, which matters for interpreting results.
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%, so the schema already explains query, n, role and include_answers; 3 is the baseline. The example keywords add a little intuition about query content but no syntax, matching behavior, or result-sizing details beyond the schema.
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?
States a specific verb+resource ('Keyword search across questions and model answers'), which cleanly separates it from listing tools like get_questions and list_topics. The examples reinforce the keyword-search intent, though it doesn't explicitly name a sibling as an alternative.
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?
Usage is implied by 'keyword search' versus the sibling listing/retrieval tools, but there is no explicit 'use this when...' guidance, no mention of when not to use it, and no named alternative (e.g. get_questions for browsing).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.