get_pyq_sampleReal UPSC Prelims previous-year questions with answers and explanations — the free 3-question sample per year (full year papers are on the platform). Includes the year's total validated PYQ count.
| Name | Required | Description | Default |
|---|---|---|---|
| year | Yes | UPSC Prelims year | |
| language | No | ISO code: en, hi, te, ta, or, bn, mr, pa, kn | en |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds meaningful context: the sample size (3 questions), inclusion of answers and explanations, the total validated PYQ count, and the limitation that full papers are not served by this tool.
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, well-structured sentence that front-loads the core function, then clarifies the sample scope and included count. Every phrase earns its place with no redundancy.
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 simple read-only tool with 2 parameters and no output schema, the description sufficiently conveys the response contents (answers/explanations, sample size, count) and scope. It could mention language behavior, but the schema default covers that.
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 coverage is 100%—both 'year' and 'language' have descriptions in the input schema. The tool description does not add extra parameter-specific semantics, so the baseline of 3 applies since the schema handles the burden.
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 clearly states the tool retrieves real UPSC Prelims previous-year questions with answers and explanations, specifically a free 3-question sample per year. This is a specific verb+resource+scope, and it distinguishes from siblings like get_current_affairs or start_practice_quiz.
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 implies usage for accessing sample previous-year questions and notes that full papers are not provided (they're on the platform), but it does not explicitly name alternatives or give when-to-use/when-not-to-use directives. Guidance is implied but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool addresses a distinct task: current affairs listing vs. detail, PYQ sample, topic catalog, knowledge search, and quiz start/submit. There is no functional overlap between the tools, and descriptions make the boundaries clear.
All tool names follow a consistent verb_noun pattern using snake_case (get_, list_, search_, start_, submit_). The naming scheme is predictable and easy to infer.
With 7 tools, the set is well-scoped for an IAS preparation server. Each tool serves a clear purpose, and the count is within the ideal range for a focused domain.
The toolset covers core UPSC prep needs: current affairs, static knowledge search, PYQ sampling, and quiz practice. The only minor gap is the lack of full PYQ papers, but the sample tool intentionally points to the platform for complete sets.