Sambodh IAS

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description's burden is lower. It adds value by describing the output format (concept cards with explanations), going beyond annotations. No contradictions.

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 only two sentences, front-loaded with the main action and output format. Every sentence is valuable and no unnecessary words.

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?

The tool has 6 parameters but none required, and no output schema. The description adequately explains the return format (cards with details) and usage context. It does not cover edge cases or pagination, but for a read retrieval tool, it is largely complete.

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 83%, with most parameters having descriptions in the schema. The description does not elaborate on parameters beyond what the schema provides, so it meets the baseline but does not add significant extra meaning.

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 daily UPSC-relevant news distilled into NewsPulse concept cards, specifying what is included (what happened, why it matters, syllabus concepts). It distinguishes itself from siblings like get_news_card_detail (individual card) and get_pyq_sample (past questions).

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 advises using this tool for any 'current affairs for UPSC' request, providing clear context. It does not explicitly state when not to use it, but the sibling tools cover other use cases, making the guidance sufficient.

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

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

Annotations already declare readOnlyHint and idempotentHint, indicating safe read behavior. The description adds the specific content returned, but does not disclose any additional behavioral traits beyond what annotations provide.

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 efficient sentence that front-loads the core value ('full concept card') and lists key components. No extraneous content.

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 the tool's simplicity (2 parameters, read-only, no output schema), the description adequately covers what the tool returns. It could mention that no further interaction is needed, but is complete enough.

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%, so parameters are well-described in the schema itself. The description does not add new semantic meaning beyond what is already in 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?

The description clearly states the tool retrieves the full concept card behind a NewsPulse headline and lists its components (bullets, relevance, explainers, links, citation). It effectively communicates the purpose but lacks explicit differentiation from sibling tools like get_current_affairs.

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 the tool is used to get full details after viewing a headline, but it does not provide explicit guidance on when to use or avoid it, nor does it mention alternatives among siblings.

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

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

The description adds value beyond annotations by explaining the tool returns 3 questions per year, includes explanations, and the year's total validated PYQ count. It is consistent with readOnlyHint and other annotations, providing useful behavioral context without contradiction.

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, information-dense sentence that conveys the key purpose, sample size, and additional content. 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 retrieval tool without an output schema, the description adequately explains what is returned: a 3-question sample, answers, explanations, and total PYQ count. It covers the core return value, though it omits details like data format or pagination.

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?

Both parameters (year and language) are already well-described in the input schema with types, ranges, and defaults. The tool description does not add additional meaning or usage details beyond what the schema provides, so a baseline score of 3 is appropriate.

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. It distinguishes itself from sibling tools like get_current_affairs and start_practice_quiz by focusing on PYQ samples.

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 the tool is for getting a free sample of PYQs for a specific year, but it does not explicitly state when not to use it or mention alternatives like full practice quizzes. The usage context is implicit, not directive.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the tool is clearly safe and idempotent. The description adds no behavioral detail beyond this, meeting the baseline but not exceeding it.

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 clear sentences with a colon separator; no wasted words. The purpose and usage are front-loaded.

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?

The description fully covers what the tool returns (subjects, PYQ years, current-affairs months, NewsPulse values). Given no output schema and no parameters, this is complete for the tool's complexity.

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?

The tool has no parameters, so the description adds no parameter semantics. Per guidelines, 0 parameters gives baseline 4; no additional value was needed.

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 explicitly states the tool provides an index of subjects/topics, PYQ years, current-affairs months, and NewsPulse values, which distinguishes it from sibling tools like search_sambodh_knowledge or get_current_affairs.

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 directly advises 'Call this first when unsure which parameter values are valid,' providing clear when-to-use guidance and positioning it as a prerequisite for other tools.

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

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

Annotations already specify readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds behavioral context: it performs semantic search (not exact match), returns source-attributed passages, and instructs synthesis and citation. No contradictions; it enriches understanding beyond annotations.

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 consists of two concise sentences. The first sentence clearly states the purpose, and the second provides usage guidance. Every word adds value, 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 search tool over a static corpus, the description covers purpose, usage context, and return format (passages with sources). It doesn't mention result ranking or pagination, but with annotations and schema (top_k default 5, max 8) it is reasonably complete. Sibling tools are present and differentiated.

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 67% (query and subject have descriptions, top_k lacks). The description does not elaborate on any parameters beyond what the schema provides. For example, it doesn't explain the effect of top_k or the format of query. Given moderate coverage, the description should compensate but does not, resulting in a below-baseline score.

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 it performs semantic search over Sambodh's indexed UPSC corpus (NCERTs and standard references), explicitly distinguishing it from sibling tools like get_current_affairs (current events) and get_pyq_sample (previous year questions). The verb 'search' matches the tool name and resource.

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 specifies 'Use for static-syllabus questions (history, polity, geography, economy...),' indicating the appropriate context. It provides instructions to synthesize answers from returned passages and cite sources, but does not explicitly mention when not to use it or give alternatives for other question types.

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

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

The description adds behavioral context beyond annotations by stating the interactive quiz nature and emphasizing not to reveal answers. Annotations already indicate read-only, idempotent, non-destructive, and the description aligns with and enriches these with specific usage rules.

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 extremely concise: two sentences that pack purpose and critical usage guideline. No wasted words; the behavioral instruction is front-loaded. It earns its place by being efficient and direct.

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?

While purpose and guidelines are clear, the description lacks any mention of what the tool returns (e.g., quiz questions, session ID). With no output schema, this omission is significant. The tool's behavior is partially described, but the expected output is missing, making it incomplete for an agent to fully understand the outcome.

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 high (83%), so baseline is 3. The description does not add extra semantics for parameters beyond what the schema provides. While the schema details year, month, source, etc., the description merely mentions starting a quiz without parameter-specific details.

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 verb 'Start' and the resource 'MCQ quiz from Sambodh's validated free question bank.' It distinguishes from siblings by specifying the source (Sambodh) and the interactive nature, setting it apart from other tools like get_current_affairs or get_pyq_sample.

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 explicitly warns never to reveal answers before user attempt and instructs to use submit_quiz_answers for grading, providing clear when-to-use and when-not-to-use guidance. It effectively directs the AI away from using this tool for grading.

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

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

Annotations already indicate idempotent and non-destructive behavior. The description adds that it's server-side grading with verdicts and score, but lacks details on side effects like locking or preventing further edits. With annotations, the bar is lower, and the description adds moderate value.

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 two sentences, front-loaded with the primary action, and contains no extraneous information. It achieves conciseness without sacrificing clarity.

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?

The tool has two parameters fully documented in the schema. The description covers return values (verdicts, score) and usage timing, making it complete for a simple grading tool. No output schema is needed as the description explains the response.

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% with descriptions for both parameters. The description does not add significant new meaning beyond what the schema already provides, resulting in a baseline score.

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 grades a quiz attempt server-side and returns per-question verdicts with explanations and overall score. It distinguishes the tool from sibling tools like start_practice_quiz by specifying the action (grading) and when to call it.

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 explicitly says to call once the user has answered fully or partially, providing clear when-to-use guidance. No alternatives or when-not-to-use are needed given the tool's straightforward use case and unrelated siblings.

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