qubitsok — Quantum Computing Jobs, Papers & Researchers

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

With annotations already declaring readOnlyHint=true, the description adds useful behavioral context: it returns error if ID not found and enumerates the fields returned. This goes beyond the annotation to tell the agent what to expect.

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 well-structured and front-loaded with the core purpose. It lists return fields efficiently, gives usage context, and mentions error behavior without unnecessary words. Every sentence earns its place.

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 having no output schema, the description explains the full return payload (job summary, skills, salary, etc.) and error case. With only one parameter and a simple task, the description is complete and leaves no ambiguity for the agent.

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%, and the schema already describes job_id as 'Numeric job ID from searchJobs results'. The description repeats this requirement but adds no new parameter-specific meaning beyond what the schema provides, so the baseline 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 gets full details for a specific quantum computing job by numeric ID. It distinguishes from the sibling tool searchJobs by focusing on a specific job's details rather than listing jobs.

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 instructs to use after searchJobs when more information is needed about a position. States the prerequisite of a valid job_id from searchJobs results, which is clear context for when to use the tool.

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 the description builds on that by disclosing that no parameters are required, that it returns the most recent day's papers, and that outputs include an AI-generated hook and tags. It also clarifies the arXiv source and quantum computing scope, adding context beyond the annotations without contradicting them.

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 sentences, each earning its place: the first states purpose, the second gives usage triggers, the third enumerates return fields, and the fourth directs to alternatives. Information is front-loaded and there is 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 list tool with one optional parameter and no output schema, this description fully covers what the tool does, when to use it, what it returns (including field names), and how it relates to sibling tools. Nothing essential is missing.

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 input schema fully documents the single optional 'limit' parameter with type, default, min/max, and description (100% coverage). The description simply says 'no parameters needed', reinforcing that the parameter is optional but adding no new meaning 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?

The description opens with a specific verb and resource: 'Get today's quantum computing papers from arXiv', clearly stating both the action and the source. It also distinguishes itself from siblings by explicitly contrasting with searchPapers (date-range/topic-filtered) and getPaperDetails (specific paper).

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 provides explicit when-to-use triggers ('what's new in quantum computing?' or daily briefing) and names two alternatives with their exact purposes: searchPapers for date-range/topic-filtered search, and getPaperDetails for full abstract/analysis. This leaves no ambiguity about tool selection.

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, so the read-only nature is established. The description adds that it requires no parameters and returns a snapshot of current data, which conveys a point-in-time behavior. It doesn't discuss potential limitations like data freshness, but for a simple read operation this is adequate.

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 tightly written, leading with the purpose, then usage conditions, return payload, and alternatives. Every sentence contributes information without fluff.

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 (no params, no output schema), the description fully covers functional purpose, use cases, and output contents. It also directs to sibling tools for specific queries, making the overall context 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?

The tool has zero parameters and the schema coverage is 100% (empty). The description correctly notes 'no parameters needed', making parameter semantics unnecessary. Baseline of 4 applies.

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 a specific verb ('Get a snapshot') and resource ('quantum computing landscape'), clearly distinguishing itself from sibling search tools by stating it provides a broad overview. It also enumerates the exact data returned, removing ambiguity.

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 broad questions like...') and when not to use ('For specific job/paper/researcher searches, use the dedicated search tools instead'), naming the alternatives.

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?

Beyond the readOnlyHint annotation, the description details the return contents (abstract, authors, tags, hook, methodology, gist, findings) and failure behavior (returns error if not found). This adds valuable context for the agent.

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 three sentences, each carrying useful information: what the tool does, when to use it, and what it returns. It is appropriately concise and 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?

Given the tool has only one parameter and no output schema, the description is complete: it explains the input format, usage context, return fields, and error condition. Nothing essential is missing.

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 schema already describes paper_id with an example, so the description adds little new semantic value. The mention of 'valid from search results' is more usage guidance than parameter 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 uses a specific verb 'Get full details' for a specific resource (quantum computing paper by arXiv ID). It also names sibling tools like getJobDetails and getLatestPapers, making the tool's distinct purpose clear.

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 states to use this tool after searchPapers or getLatestPapers, giving clear context for when it should be invoked. It also notes the requirement for a valid paper_id from search results, providing actionable guidance.

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?

Beyond the read-only annotation, the description reveals that the tool is AI-powered and decomposes natural language into structured filters, returns specific profile fields (affiliations, domains, publication count, top tags, recent papers), uses arXiv data from the last 12 months, and caps results at 50. This adds substantial behavioral context without contradicting the 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 is concise and well-structured: it opens with the purpose, states usage conditions, gives exclusions, explains behavior, provides output details, and includes examples. Every sentence delivers useful information 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?

The description is complete given the absence of an output schema. It explains what the tool returns (profiles with affiliations, domains, publication count, top tags, recent papers), data source and time window, result limit, and example queries. It provides sufficient context for an agent to decide when and how to invoke it.

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 all three parameters, so the baseline is 3. The description adds value by providing concrete examples ('quantum error correction researchers at Google', 'trapped ions', 'John Preskill') and explaining how the AI decomposes natural language into structured filters, which clarifies query semantics 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?

The description clearly states the tool finds quantum computing researchers and potential collaborators from 1000+ active profiles, using a specific verb and resource. It explicitly distinguishes itself from sibling tools searchJobs and searchPapers, making its purpose unmistakable.

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?

It provides explicit when-to-use guidance: 'Use when the user asks about specific researchers, who works on a topic, or wants to find collaborators.' It also gives clear exclusions and alternatives: 'NOT for jobs (use searchJobs) or papers (use searchPapers).' This fully satisfies the when/not/alternatives criteria.

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 openWorldHint=false, and the description adds valuable behavioral details: 90-day time window, max 20 results, promoted listings appear first and marked, and suggestion to follow up with getJobDetails. This goes beyond annotations 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 paragraph with several sentences, but each sentence serves a purpose: purpose, usage, exclusions, capabilities, limitations, follow-up suggestion, and examples. It is well-structured and front-loaded, though slightly longer than necessary; still earns its place.

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 there is no output schema, the description effectively conveys expected results by mentioning 'max 20 results', 'promoted listings appear first (marked)', and recommending getJobDetails for full info. It covers limitations and filtering behavior, making it 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?

Schema coverage is 100%, so baseline is 3. The description adds meaning by listing supported filter dimensions (role type, seniority, location, company, salary, remote, technology tag) and providing concrete query examples that clarify parameter usage. This extra context justifies a 4.

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 states a specific verb+resource ('Search 500+ quantum computing job listings') and explicitly distinguishes from siblings by saying 'NOT for research papers (use searchPapers) or researcher profiles (use searchCollaborators)'. It also lists common use cases like job openings and career opportunities.

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?

It clearly says when to use ('Use when the user asks about job openings, career opportunities, hiring, or specific positions in quantum computing') and explicitly names alternative tools for other intents. This is exemplary guidance.

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 discloses important behavioral traits beyond the readOnlyHint annotation: natural language queries are decomposed via AI into structured filters, date range defaults to last 7 days with a 12-month max lookback, results are returned newest first with a max of 50. These details inform the agent about constraints and default behavior, complementing the annotation.

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 information-dense and well-structured: it opens with the core purpose, then usage context, exclusions, behavioral details, and examples. Every sentence adds practical value without redundancy or filler.

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 complex tool relying on AI decomposition and date filtering, the description covers usage conditions, defaults, limits, sorting, and fallback to getPaperDetails. Since there is no output schema, this is sufficient for an agent to select and invoke the tool correctly.

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 all parameters are already described. The description adds value by providing concrete examples for the query parameter and clarifying date defaults and the AI decomposition mechanism, which enriches understanding but does not fundamentally alter the schema's 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 that the tool searches quantum computing papers on arXiv, using a specific verb ('Search') and resource ('quantum computing research papers'). It also explicitly distinguishes from siblings by saying 'NOT for jobs (use searchJobs) or researcher profiles (use searchCollaborators)', making it unambiguous when to choose this tool.

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 says 'Use when the user asks about recent research, specific papers, or academic topics in quantum computing' and provides exclusions with explicit alternatives (searchJobs, searchCollaborators). It also mentions using getPaperDetails for full abstract and analysis, giving clear when-to-use and when-not-to-use guidance.

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