qubitsok — Quantum Computing Jobs, Papers & Researchers

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

Annotations confirm readOnlyHint=true (safe read), while description adds valuable return value structure (lists specific fields: visa sponsorship, salary, etc.) and error behavior ('Returns error if ID not found'). Adds meaningful context beyond structured 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?

Four efficient sentences cover purpose, usage context, return payload, prerequisites, and error handling. Front-loaded with action verb, no redundancy, every sentence earns its place despite listing multiple return fields.

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?

Compensates effectively for missing output schema by enumerating return fields (job summary, skills, salary, etc.) and error conditions. Complete coverage for a single-parameter retrieval tool with clear annotations.

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 has 100% coverage but description adds semantic context that the ID comes from searchJobs results ('Requires a valid job_id from searchJobs results'), reinforcing parameter relationships and workflow that pure schema doesn't convey.

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?

Clear specific verb ('Get') + resource ('full details for a specific quantum computing job') + scope ('by its numeric ID'). Explicitly distinguishes from sibling searchJobs by specifying this retrieves a specific job by ID versus searching.

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?

Explicitly states 'Use after searchJobs when the user wants more information about a specific position' and notes prerequisite 'Requires a valid job_id from searchJobs results.' Clear workflow guidance distinguishing from the search sibling.

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?

With readOnlyHint=true in annotations, the description adds valuable context about the return structure (title, authors, AI-generated hook, tags) and scope (most recent day only). No contradictions with 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?

Four well-structured sentences: purpose, usage trigger, return values, alternatives. Zero waste. Front-loaded with the core action and scoped domain.

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?

No output schema exists, so description compensates by detailing return fields (hook, tags, etc.). Single optional parameter is fully documented in schema. Sufficient for a simple read-only aggregator tool.

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%, establishing baseline 3. The phrase 'no parameters needed' adds critical semantic context that the tool operates with zero required configuration, guiding the agent to invoke without user input gathering.

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?

Specific verb 'Get' + resource 'quantum computing papers' + source 'arXiv'. Explicitly distinguishes from sibling 'searchPapers' (for date-range/topic-filtered) and 'getPaperDetails' (for full abstracts), making selection unambiguous.

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?

Explicit trigger phrases ('what's new in quantum computing?', 'daily paper briefing') tell exactly when to invoke. Names specific alternatives for different use cases: 'searchPapers' for filtering and 'getPaperDetails' for specific paper analysis.

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 establish read-only safety; description adds valuable behavioral context by enumerating exact return fields (total active jobs, top hiring companies, papers published, etc.) and clarifying the aggregate 'snapshot' nature of the data.

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?

Three well-structured sentences: purpose with constraint, usage with examples, and returns with alternatives. Zero waste, properly 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?

Despite missing output schema, description fully compensates by listing all return data points. Covers scope, usage triggers, and sibling differentiation, making it complete for this simple read-only aggregate tool.

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 contains zero parameters; description appropriately notes 'no parameters needed' which satisfies the baseline requirement for zero-parameter tools.

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 uses specific verb 'Get' with clear resource 'snapshot of the quantum computing landscape' and explicitly distinguishes from sibling search tools by stating this is for broad questions vs specific searches.

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 when-to-use with concrete examples ("how's the quantum job market?", "trending topics?") and clear when-not-to-use instruction directing users to 'dedicated search tools instead' for specific lookups.

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?

While annotations declare readOnlyHint=true (safe read), description adds valuable behavioral context: enumerates specific return fields (AI-generated tags with reasons, hook, methodology, etc.) and error handling ('Returns error if not found'). Does not contradict 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?

Five sentences arranged logically: purpose → usage guidance → output contract → input requirement → error handling. No redundant information; each sentence advances understanding of how/when to invoke.

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 single-parameter read tool, description compensates for missing output schema by detailing return structure (abstract, authors, tags, etc.). Domain context (quantum computing) and sibling relationships are clear. No gaps given tool simplicity.

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 complete parameter documentation. Description adds semantic context beyond schema: validity constraint ('valid paper_id') and provenance guidance ('from search results'), helping the agent understand proper parameter sourcing.

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?

Specific verb 'Get' with clear resource 'full details for a specific quantum computing paper' and scope 'by its arXiv ID'. Distinguishes from sibling searchPapers by specifying this is for 'dive deep' vs search/listing behavior.

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?

Explicitly states when to use ('Use after searchPapers or getLatestPapers'), names specific sibling alternatives, and clarifies prerequisite ('Requires a valid paper_id from search results'). Also documents error condition.

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?

Rich disclosure beyond annotations: explains AI-powered decomposition of natural language, data provenance ('arXiv papers published in the last 12 months'), return structure ('profiles with affiliations, domains, publication count...'), and result limits ('Max 50'). Annotations only indicate read-only safety; description carries full burden for behavioral traits.

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?

Information-dense structure flows logically: scope → usage conditions → exclusions → mechanism → data source → limits → examples. No redundant sentences; each clause adds distinct decision-critical information for an agent selecting between multiple search tools.

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?

Comprehensive despite no output schema. Description fully compensates by detailing return values ('profiles with affiliations... recent papers') and explaining the 12-month data window, which is essential for temporal relevance expectations. Adequately covers the 3-parameter 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?

Schema coverage is 100% establishing baseline 3. Description adds significant value by explaining the NLP processing ('decomposes natural language into structured filters') and providing concrete query patterns ('quantum error correction researchers at Google', 'John Preskill') that clarify how the query parameter handles complex semantic searches beyond simple keyword matching.

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 specific action ('Find'), resource ('quantum computing researchers'), and scope ('1000+ active profiles'). Explicitly distinguishes from sibling tools searchJobs and searchPapers by name, clarifying this is for people, not positions or publications.

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 when-to-use ('user asks about specific researchers, who works on a topic') and when-not-to-use ('NOT for jobs... or papers') plus named alternatives. Clear trigger conditions minimize confusion with sibling search 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 indicate read-only operation; description adds crucial behavioral context: temporal limits (90 days), result limits (max 20), ranking behavior (promoted listings first), and workflow guidance (suggest getJobDetails for full info).

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?

Information-dense with clear structure: purpose → usage → exclusions → capabilities → limitations → workflow → examples. Every sentence provides actionable guidance 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?

Without output schema, description adequately covers result scope (max 20), data freshness (90 days), special handling (promoted listings), and clear next-step guidance (getJobDetails). Complete for a search tool.

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?

Despite 100% schema coverage (baseline 3), description adds valuable semantic context about the 'query' parameter: it supports AI query decomposition for filters like role type, seniority, location, salary, remote status, and technology tags.

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 specific verb (Search) + resource (quantum computing job listings) + scope (500+). Explicitly distinguishes from siblings searchPapers and searchCollaborators with clear exclusion clauses.

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?

Contains explicit 'Use when' clause covering job openings/career opportunities/hiring. Provides clear 'NOT for' guidance naming specific alternatives (searchPapers, searchCollaborators).

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 declare readOnlyHint=true; description adds critical constraints missing from annotations: 'max lookback 12 months', 'Returns newest first', and explains AI decomposition behavior. 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?

Well-structured progression: purpose → usage → exclusions → behavioral constraints → related tools → examples. Each sentence provides unique value; no redundancy despite information density.

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?

No output schema exists, but description compensates by documenting result ordering, limits (max 50), and directing to getPaperDetails for full data. All 4 parameters well-covered; completeness is high for a search tool.

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 has 100% coverage (baseline 3). Description adds 'max lookback 12 months' constraint not in schema, and consolidates query examples that illustrate natural language capabilities, adding semantic context beyond structured descriptions.

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?

Specific verb 'Search' + specific resource 'quantum computing research papers from arXiv'. Distinguishes from siblings by explicitly stating 'NOT for jobs (use searchJobs) or researcher profiles (use searchCollaborators)' and clarifies relationship to getPaperDetails.

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?

Explicitly states when to use ('when the user asks about recent research, specific papers, or academic topics') and when not to use with named alternatives. Also directs to getPaperDetails for full abstracts, creating clear decision boundaries.

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