animequotes

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the tool selects data sources automatically, fills arguments, and returns results. However, it lacks details on limitations (e.g., data source availability, error handling, or rate limits), which would be helpful for a tool with no 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 well-structured and front-loaded, starting with the core functionality. Each sentence adds value: explaining the mechanism, contrasting with alternatives, and providing concrete examples. There is no wasted text, making it efficient and easy 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?

Given the tool's complexity (automated source selection) and lack of annotations/output schema, the description is mostly complete. It covers purpose, usage, and behavior, but could benefit from more details on limitations or response format. The examples help, but without structured output, some gaps remain.

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 100%, so the schema already documents the 'question' parameter as 'Your question or request in natural language.' The description adds minimal value beyond this, with examples like 'Look up adverse events for ozempic' that illustrate parameter usage but don't provide additional syntax or format 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 tool's purpose: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer'), and mechanism ('Pipeworx picks the right tool, fills the arguments'). It distinguishes from siblings by emphasizing natural language input versus structured tool selection.

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 guidance on when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It contrasts with alternatives by implying that other tools might require browsing or schema knowledge. Examples like 'What is the US trade deficit with China?' illustrate appropriate use cases.

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 provides behavioral context by detailing what data is returned for each type and mentioning resource URIs. However, it doesn't explicitly state that the operation is read-only or disclose potential side effects, 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 very concise (two sentences) yet packed with essential information: core function, type-specific metrics, output format, and efficiency benefit. No superfluous 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?

Given the moderate complexity and absence of output schema, the description covers purpose, input constraints, and expected output well. It could be more complete by hinting at error cases or output structure, but it's sufficient for the task.

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 the description adds value beyond schema by providing concrete examples for each parameter (e.g., tickers/CIKs for company, drug names) and reinforcing the enum values. This aids in parameter selection.

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 compares 2-5 entities side by side, specifies exact metrics for company and drug types, and mentions return format including resource URIs. It distinguishes from siblings like resolve_entity (single entity) and ask_pipeworx (general query).

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 efficient multi-entity comparison, replacing multiple sequential calls. It doesn't explicitly state when not to use or name alternatives, but the context is clear given 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?

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it's a search/read operation (implied by 'search' and 'returns'), it prioritizes relevance ('most relevant tools'), and it has a specific use case (large tool catalogs). However, it doesn't mention potential limitations like rate limits, authentication needs, or error conditions, which would be helpful for a tool with no annotation coverage.

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 efficiently structured in two sentences: the first states the core functionality, and the second provides critical usage guidance. Every phrase adds value, with no redundant or vague language, and key information is 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's moderate complexity (search operation with 2 parameters), 100% schema coverage, and no output schema, the description is largely complete. It covers purpose, usage context, and behavioral intent. However, without annotations or output schema, it could benefit from more detail on return format or error handling, though the guidance on when to use it compensates well.

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 100%, so the schema already documents both parameters thoroughly. The description adds marginal value by implying the query should be task-oriented ('what you need'), but doesn't provide additional syntax, format, or usage details beyond what the schema specifies. This meets the baseline for high 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 with specific verbs ('search', 'returns') and resource ('Pipeworx tool catalog'), distinguishing it from siblings like random_quote or search_by_anime by focusing on tool discovery rather than content retrieval. It explicitly mentions the catalog context and output format (tools with names and descriptions).

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 guidance on when to use this tool ('Call this FIRST when you have 500+ tools available and need to find the right ones for your task'), including a quantitative threshold (500+ tools) and a clear alternative strategy (use this before trying other tools). It also implies when not to use it (for smaller tool sets or non-discovery tasks).

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 this is a deletion operation, which implies destructive behavior, but doesn't specify whether deletion is permanent, reversible, or requires specific permissions. No information about side effects, error handling, or confirmation requirements is included.

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, efficient sentence with zero wasted words. It's front-loaded with the core action and immediately specifies the target, making it easy to parse. Every word earns its place in communicating the essential function.

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 destructive operation with no annotations and no output schema, the description is incomplete. It doesn't explain what happens after deletion (success confirmation, error responses), whether there are constraints on which keys can be deleted, or what constitutes a valid memory key. The agent lacks sufficient context for safe invocation.

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 100%, so the schema already documents the single 'key' parameter adequately. The description adds minimal value beyond restating 'by key' from the tool name. Baseline 3 is appropriate when the schema does the heavy lifting for parameter documentation.

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 specific action ('Delete') and target resource ('a stored memory by key'), distinguishing it from sibling tools like 'recall' (which likely retrieves) and 'remember' (which likely stores). It uses precise terminology that directly communicates the tool's function.

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 guidance is provided on when to use this tool versus alternatives. While the purpose is clear, the description doesn't mention prerequisites (e.g., needing an existing memory key), error conditions, or when to choose 'forget' over other memory-related operations like 'recall' or 'remember'.

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?

Discloses rate limit constraint and the instruction to avoid including user prompts. No annotations present, so description carries full burden. Could improve by noting if a confirmation response is sent.

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 tight sentences: purpose, usage rules, rate limit. No wasted words, front-loaded with main function.

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?

Covers all necessary aspects: purpose, intended use cases, constraints, parameter guidance (via schema), and rate limit. No output schema needed for this simple send action.

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 covers all parameters with descriptions (100% coverage). The tool description does not add extra parameter detail beyond schema, so baseline 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?

Explicitly states it sends feedback to the Pipeworx team for bugs, features, data gaps, or praise. Clearly distinct from sibling tools which are for asking questions or managing data.

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 (bug reports, feature requests, etc.) and concrete guidelines: describe tools/data attempted, avoid end-user prompt, rate limit of 5 per day. No ambiguity.

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 are provided, so the description carries the full burden. It discloses key behavioral traits: the tool can retrieve by key or list all, and memories persist across sessions. However, it lacks details on error handling, format of returned data, or any limitations (e.g., memory size, retrieval speed).

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 front-loaded with core functionality in the first sentence and uses a second sentence to provide context. Every sentence adds value without redundancy, making it efficient and well-structured for quick comprehension.

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 no annotations and no output schema, the description adequately covers basic purpose and usage but lacks details on return values, error cases, or performance constraints. For a tool with 1 parameter and simple functionality, it's minimally viable but could be more 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 description coverage is 100%, so the schema already documents the parameter. The description adds meaningful context by explaining the semantic effect of omitting the key (lists all memories) and linking it to session persistence, which enhances understanding beyond the schema's technical specification.

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 with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from siblings by specifying retrieval of saved context versus creation (remember), deletion (forget), or unrelated search tools.

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 when to use this tool ('to retrieve context you saved earlier') and provides clear alternatives ('omit key to list all keys'). It differentiates from siblings by focusing on retrieval of stored memories rather than other memory operations or unrelated functions.

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 the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool performs a write operation (implied by 'Store'), specifies persistence differences based on authentication, and mentions session duration for anonymous users. It doesn't cover error conditions or rate limits, but provides sufficient context for safe use.

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 efficiently structured in two sentences: the first states the core purpose and usage, the second adds critical behavioral context about persistence. Every sentence adds value without redundancy, making it easy to parse and understand quickly.

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 write operation tool with no annotations and no output schema, the description does well by explaining what the tool does, when to use it, and key behavioral aspects (persistence rules). It could mention what happens on duplicate keys or error cases, but covers the essential context given 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?

Schema description coverage is 100%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain key constraints or value formatting). This meets the baseline expectation when schema coverage is complete.

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 specific action ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from siblings like 'recall' (retrieval) and 'forget' (deletion). It explicitly mentions what can be stored ('intermediate findings, user preferences, or context across tool calls'), making the 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 explicit guidance on when to use this tool ('to save intermediate findings, user preferences, or context across tool calls') and distinguishes it from alternatives by specifying persistence behavior ('Authenticated users get persistent memory; anonymous sessions last 24 hours'), which helps in choosing between this and other storage mechanisms.

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 alone must convey behavior. It states the tool is for resolution (read operation), accepts multiple input formats, and returns multiple identifiers. It does not discuss auth, rate limits, or error conditions, but the behavior is sufficiently clear for a lookup 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 two sentences, front-loading the purpose. Every sentence adds value: first defines the tool, second details supported input and output. 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?

Given the tool's simplicity (2 parameters, no output schema), the description covers all essential aspects: purpose, input format, output fields, and benefit over alternatives. It is complete for the task.

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%, but the description adds significant value beyond the schema by providing usage examples (e.g., 'AAPL', '0000320193', 'Apple'), clarifying the value parameter accepts multiple formats, and explaining the return structure (ticker, CIK, name, pipeworx URIs).

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 resolves an entity to canonical IDs in a single call, with specific verb 'resolve' and resource 'entity to canonical IDs'. It distinguishes from siblings by focusing on entity resolution, contrasting with general query or memory tools.

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 indicates when to use (when needing canonical IDs for a company) and notes it replaces multiple lookup calls. It also restricts to type 'company' for v1, implying exclusion for other entity types. However, it does not explicitly mention when not to use or specify alternative tools.

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