autosearch

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

With no annotations, the description carries full burden. It discloses return formats but lacks details on error behavior (e.g., if run_id is invalid or run not completed). It does not confirm read-only nature or side effects.

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 very concise with two sentences: one stating the purpose and one listing return types. Every word is necessary; no 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?

Given the absence of output schema and annotations, the description provides the core functionality but lacks parameter explanations, error conditions, or usage context. It is adequate for a simple retrieval tool but could be more thorough.

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 0%, so the description should add meaning to parameters. It does not describe 'run_id' or 'format' beyond what the schema provides. The enum values for format are clear, but the description adds no extra context about valid values or usage.

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's function: 'Get the generated paper for a completed run' and specifies the return types (markdown, JSON metadata, PDF download URL). It differentiates itself from siblings like 'autosearch_get_run_status' which retrieves run status, not the 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 implies usage for completed runs, providing some guidance. However, it does not explicitly mention when not to use (e.g., if run is incomplete) or recommend alternatives like 'autosearch_get_run_status' to check completion status first.

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?

No annotations provided, so description must fully disclose behavior. It states return fields but does not mention idempotency, side effects (none), or potential errors. Minimal behavioral context.

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 sentences with no wasted words. Efficient and front-loaded with key action and return information.

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 one parameter and no output schema, the description is adequate but minimal. Could add note about required run_id existence or that it does not mutate state.

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 0% and description adds no meaning to run_id. No hint about format, source, or validation rules. Parameter semantics are entirely absent.

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?

Description clearly specifies the verb 'Get' and resource 'status of a research run'. It distinguishes from siblings like autosearch_get_run_paper (gets paper) and autosearch_list_my_runs (lists runs).

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?

No explicit guidance on when to use this tool vs alternatives. Missing when-not criteria or context for choosing get_run_status over get_run_paper or list_my_runs.

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?

No annotations exist, so the description must carry the full burden. It mentions 'latest' but does not specify how runs are ordered (e.g., by creation date) or if there are any side effects. Missing details on pagination, filtering, or what 'status' and 'frontier score' represent.

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 sentence that front-loads the purpose without extraneous words. Every word 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 the simple tool (one optional parameter, no output schema), the description provides adequate high-level purpose. However, it lacks details on return structure, sorting, and precision of 'latest'. The absence of an output schema places more burden on the description, which is only partially fulfilled.

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 only parameter, limit, is not mentioned in the description. With 0% schema description coverage, the description fails to add any meaning beyond what the schema already provides (default 20, max 100). This is a significant gap.

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 action (List), the resource (runs), the scope (authenticated user's latest), and the included fields (status and frontier score). It effectively distinguishes this tool from siblings like autosearch_get_run_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?

No explicit guidance is provided on when to use this tool versus alternatives. The context signals list siblings, but the description does not differentiate usage scenarios, e.g., when to use list vs get_run_status.

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 no annotations, the description carries full burden. It discloses the async nature ('Returns run_id immediately. Use get_run_status to poll'), which is crucial. However, it omits other behavioral traits like resource usage, cancellability, or rate limits, making it minimally adequate for a simple launch 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 extremely concise with two sentences, no fluff, and front-loads the primary action. Every word adds value, making it efficient for an AI agent to parse.

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 tool with 2 parameters and no output schema, the description covers the basic workflow (launch and poll) and return value. However, it lacks explanation of the depth parameter's impact and potential long-running nature, leaving gaps for optimal usage.

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 description adds no information about parameters beyond the schema. Schema coverage is 50% (prompt described, depth not), and the description fails to explain the depth enum values or prompt quality guidelines. The description should compensate for low coverage but does not.

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 'Launch an AI scientific research run', with a specific verb and resource. It distinguishes itself from siblings like autosearch_get_run_status (polling) and autosearch_get_run_paper (retrieving papers), making the tool's purpose 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?

The description provides clear usage context: use this tool to start a run, then poll with get_run_status. It implies when to use this tool vs alternatives but does not explicitly state when not to use it or list all sibling alternatives directly.

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 no annotations provided, the description carries full burden for behavioral disclosure. It states the tool searches and returns top N papers with metadata, implying a read-only operation. However, it omits details on result ordering, error handling, data freshness, and whether results are real-time or cached.

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 sentences, 16 words. Front-loaded with the action and result. Every word earns its place. No redundancy or 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 absence of output schema and annotations, the description is too brief. It does not explain what metadata is returned, how results are sorted, pagination, or constraints like the limit maximum. It leaves significant gaps for an agent to properly invoke and interpret 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 descriptions coverage is 0%, so the description must compensate. It explains the 'query' parameter via 'Search peer-reviewed evidence' and 'limit' via 'Returns top N papers', but does not detail the 'sources' parameter beyond 'academic sources', leaving the agent to infer from the enum list in the schema. It adds minimal semantic value.

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 'Search' and the resource 'peer-reviewed evidence across academic sources', distinguishing it from the sibling tool 'autosearch_run_research' which initiates a full research run. It specifies the output: 'top N papers with metadata'.

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 sets context by stating 'without launching a full research run', implying when to use this lightweight search instead of the more thorough 'autosearch_run_research'. It lacks explicit when-not-to-use guidance but provides clear context for 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?

With no annotations provided, the description must disclose behavior. It accurately describes the tool as a read-only verification that returns metadata, implying no side effects. However, it does not explicitly state safe, non-destructive behavior, which would be beneficial.

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 (two sentences) and front-loaded with the primary verb 'Verify', efficiently summarizing purpose and output without 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?

For a simple verification tool with one parameter and no output schema, the description is fully complete. It explains the action, the data source (Crossref), and the returned fields, leaving no ambiguity.

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 provides 100% coverage for the single parameter 'doi', including a clear description of accepted formats. The description adds no further parameter information, so it meets the baseline for full schema coverage.

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's purpose: 'Verify a DOI via Crossref.' It specifies the output fields (validity, title, authors, year, journal), making the functionality unambiguous and distinguishable from sibling tools like autosearch_run_research or autosearch_search_evidence.

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 context (DOI verification via Crossref) but does not specify when to prefer this tool over alternatives or mention any prerequisites or exclusions. No explicit 'when to use' or 'when not to use' guidance is provided.

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