wikiviews

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes key traits: the tool selects the best data source and fills arguments automatically, implying it handles tool orchestration internally. However, it lacks details on potential limitations, such as rate limits, authentication needs, error handling, or the types of data sources available, which are important for a tool that performs automated queries.

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 the core purpose in the first sentence, followed by explanatory details and examples. Every sentence adds value: the second explains the automation mechanism, the third highlights the benefit of no manual tool selection, and the examples concretely illustrate usage. It is appropriately sized with no redundant or vague phrasing.

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 querying with internal tool selection) and lack of annotations or output schema, the description is moderately complete. It covers the purpose, usage, and parameter semantics well, but does not address behavioral aspects like response format, error conditions, or data source limitations. This leaves gaps for an AI agent to fully understand how to invoke and interpret results from this 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?

The input schema has 100% description coverage, with the 'question' parameter documented as 'Your question or request in natural language.' The description adds value by emphasizing 'plain English' and providing examples like 'Look up adverse events for ozempic,' which clarifies the expected format and scope beyond the schema. However, it does not detail constraints or best practices for parameter usage, so it 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: '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'), distinguishing it from sibling tools like discover_tools or recall by emphasizing natural language querying without manual 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 clear context on when to use this tool: for asking questions in plain English to get automated answers, eliminating the need to browse tools or learn schemas. It includes examples like 'What is the US trade deficit with China?' to illustrate appropriate use cases. However, it does not explicitly state when not to use it or name alternatives among siblings, such as for specific data retrieval tasks that might require direct tool invocation.

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 states the tool returns 'the most relevant tools with names and descriptions,' which adds context about output format. However, it lacks details on rate limits, error handling, or performance characteristics, leaving gaps for a tool with potential high usage.

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 the core purpose in the first sentence, followed by usage guidance, with zero wasted words. Both sentences earn their place by providing essential information without redundancy, making it highly efficient.

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 (2 parameters, no output schema, no annotations), the description is mostly complete. It covers purpose and usage well but lacks details on behavioral aspects like error handling or output structure, which would be beneficial for a discovery tool in a large catalog.

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 no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain query formatting or limit implications), resulting in a baseline score of 3 as the schema does the heavy lifting.

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 the Pipeworx tool catalog') and resource ('tool catalog'), and explicitly distinguishes it from siblings by emphasizing its role in finding tools among 500+ options, unlike the sibling tools which focus on retrieving specific data views.

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 usage guidelines: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This clearly indicates when to use it (for discovery in large catalogs) and implies alternatives are not needed for initial tool discovery, though it doesn't name specific 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a deletion operation, implying it's destructive, but doesn't specify whether deletion is permanent, reversible, requires specific permissions, or has side effects. For a destructive tool with zero annotation coverage, this is a significant gap.

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 resource, making it immediately scannable and understandable.

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 tool with no annotations and no output schema, the description is incomplete. It doesn't explain what happens after deletion (e.g., confirmation message, error if key doesn't exist), behavioral constraints, or how this tool fits with siblings like 'recall' and 'remember'.

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 fully. The description adds no additional semantic context beyond what's in the schema (e.g., what constitutes a valid key, format examples). Baseline 3 is appropriate when the schema does the heavy lifting.

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 ('Delete') and resource ('a stored memory by key'), making the purpose immediately understandable. It doesn't explicitly distinguish from sibling tools like 'recall' or 'remember', but the action is specific enough to imply differentiation.

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 like 'recall' (which likely retrieves memories) or 'remember' (which likely stores them). The description only states what it does, not when it's appropriate or what prerequisites might exist.

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. It discloses the date format constraint (YYYYMMDD) which is useful behavioral context, but doesn't mention rate limits, authentication needs, response format, pagination, or error conditions. The description doesn't contradict any 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?

Two sentences with zero waste. First sentence states purpose clearly, second sentence provides critical format requirement. Perfectly front-loaded and appropriately sized for this tool.

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 read-only query tool with 100% schema coverage but no output schema, the description is adequate but could be more complete. It doesn't describe the return format (daily counts structure) or potential limitations, which would be helpful given the lack of output schema.

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 all three parameters. The description adds the date format requirement which is already covered in the schema descriptions. No additional parameter semantics beyond what the schema provides.

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 ('Get daily pageview counts'), resource ('for a specific Wikipedia article'), and scope ('over a date range'). It distinguishes from sibling tools by focusing on individual article views rather than project-level views or top articles.

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 through the date range requirement but doesn't explicitly state when to use this tool versus alternatives like 'get_project_views' or 'get_top_articles'. No guidance on exclusions or prerequisites is provided.

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. It discloses the date format constraint ('Dates must be in YYYYMMDD format'), which is useful behavioral context. However, it doesn't mention other important traits like rate limits, authentication needs, pagination, or what the return format looks like (though there's no output schema).

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 with zero waste. The first sentence states the purpose and scope, and the second provides a critical constraint. Every word earns its place, and it's appropriately sized for this simple tool.

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 (date-range query with no output schema), the description is adequate but has gaps. It covers the purpose and date format, but without annotations or output schema, it should ideally mention more about the return values (e.g., that it returns aggregate totals) or any limitations. It's minimally viable but not fully 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 fully documents both parameters (start and end dates with format examples). The description adds the date format requirement, but this is redundant with the schema. No additional parameter meaning is provided beyond what's 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 specific action ('Get aggregate daily pageview totals'), resource ('all of English Wikipedia'), and scope ('over a date range'). It distinguishes from sibling tools like get_article_views (which likely gets views for specific articles) and get_top_articles (which likely gets ranking 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?

The description implies usage context by specifying 'over a date range' and 'for all of English Wikipedia', which suggests when to use this tool. However, it doesn't explicitly state when NOT to use it or mention alternatives like the sibling tools, leaving some guidance gaps.

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. It discloses key behavioral traits: the tool returns ranked data, has a limit of 1000 articles, and focuses on view counts. However, it doesn't mention rate limits, authentication needs, data freshness, error conditions, or pagination behavior, leaving gaps for a read operation.

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 with zero waste. The first sentence establishes purpose and scope, the second adds crucial behavioral details (limit and ranking). Every word earns its place, and information is front-loaded appropriately.

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 read-only tool with 3 parameters and 100% schema coverage but no output schema, the description provides adequate purpose and scope. However, without annotations or output schema, it should ideally mention more about return format (e.g., structure of article data) or error handling to be fully complete for agent use.

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%, with all three parameters clearly documented in the schema. The description adds no parameter-specific information beyond implying date-based filtering. This meets the baseline of 3 when the schema does the heavy lifting, but doesn't provide additional semantic context.

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 ('Get the most viewed Wikipedia articles') and resource ('Wikipedia articles'), with precise scope ('for a specific day', 'ranked by view count', 'up to 1000 articles'). It distinguishes from siblings by focusing on top articles rather than individual article views or project-level 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?

The description implies usage context through 'for a specific day' and 'ranked by view count', suggesting this tool is for popularity analysis rather than detailed tracking. However, it doesn't explicitly state when to use this versus sibling tools like get_article_views or get_project_views, nor does it mention any prerequisites or exclusions.

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 explains the dual functionality (retrieve by key vs list all) and mentions persistence across sessions ('in previous sessions'), which is valuable context. However, it doesn't disclose potential limitations like memory size constraints, retrieval failures, or what happens when a non-existent key is provided. For a tool with zero annotation coverage, this leaves some behavioral aspects unclear.

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 perfectly concise and well-structured in two sentences. The first sentence establishes the core functionality with clear conditional logic. The second sentence provides important context about when to use the tool. Every word earns its place with zero redundancy or unnecessary elaboration.

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 (dual functionality, session persistence) and 100% schema coverage but no output schema or annotations, the description does well. It explains the two operational modes and persistence scope. However, without an output schema, it doesn't describe what gets returned (memory content format, list structure), leaving some uncertainty about the tool's complete behavior.

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 has 100% description coverage, so the baseline is 3. The description adds meaningful context by explaining the semantic implication of omitting the key parameter: 'omit to list all keys' and 'list all stored memories (omit key)'. This provides important usage guidance beyond the schema's technical description. However, it doesn't add details about key format, constraints, or examples.

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 like 'remember' (store) and 'forget' (delete) by focusing on retrieval operations. The description explicitly mentions retrieving context saved earlier in the session or previous sessions, which adds important scope information.

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 vs alternatives: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' It clearly explains the two modes of operation (retrieve specific vs list all) and when to use each. While it doesn't name specific sibling alternatives, it provides complete operational guidance for the tool's intended 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 provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool performs a write operation ('store'), specifies persistence characteristics ('authenticated users get persistent memory; anonymous sessions last 24 hours'), and implies it's for session-scoped data. It does not cover rate limits, error handling, or authentication requirements beyond persistence, but adds substantial value beyond basic purpose.

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 appropriately sized and front-loaded, with the core purpose stated first ('Store a key-value pair in your session memory'), followed by usage guidance and behavioral details. Both sentences earn their place by providing essential context without redundancy, making it efficient and well-structured.

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 (a write operation with persistence nuances), no annotations, and no output schema, the description is largely complete. It covers purpose, usage, and key behavioral traits like persistence rules. However, it lacks details on error cases (e.g., what happens if the key already exists) or return values, which would be needed for full completeness, especially without an output schema.

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 has 100% description coverage, with both parameters ('key' and 'value') well-documented in the schema itself. The description does not add any parameter-specific details beyond what the schema provides, such as examples or constraints not in the schema. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description adds no extra parameter semantics.

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 ('store a key-value pair') and resource ('in your session memory'), distinguishing it from sibling tools like 'recall' (which retrieves) and 'forget' (which removes). It specifies the type of data that can be stored ('intermediate findings, user preferences, or context across tool calls'), making the purpose explicit and differentiated.

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 context for when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), which implicitly distinguishes it from siblings like 'recall' (for retrieval) or 'get_article_views' (for analytics). However, it does not explicitly state when not to use it or name alternatives, such as using 'forget' to remove stored data, which prevents a perfect score.

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