Ship On Friday

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

The description discloses that the tool picks the best data source, fills arguments, and returns results, implying it may invoke other tools. It does not require annotations since no annotations are provided, and the description adds value by explaining the autonomous behavior. However, it does not discuss limitations, error handling, or potential 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 concise, using four sentences to convey purpose, behavior, and usage. It is front-loaded with the key action and resource, and includes examples without unnecessary detail. Every sentence serves a purpose.

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 a simple single-parameter input, no output schema, and no annotations, the description provides sufficient context for the agent to use it correctly. It explains the tool's autonomous behavior and gives examples. A score of 5 would require more detail on return format or error handling, but the current level is adequate.

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% coverage for the single parameter 'question', so the baseline is 3. The description adds context by specifying the parameter should be a natural language question and provides examples, which is helpful but not essential since the schema already describes it as 'Your question or request in natural language'.

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 accepts natural language questions and returns answers from the best available data source. It specifies the verb 'ask' and resource 'pipeworx', and distinguishes itself from siblings by explaining it selects and uses other tools internally, unlike sibling tools like discover_tools or recall.

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: when you want to ask a question in plain English without browsing tools or learning schemas. It does not explicitly exclude scenarios, but the context implies it is a general-purpose query tool. The absence of alternatives or when-not-to-use scenarios prevents a score of 5.

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 must convey behavioral traits. It states that it returns 'the most relevant tools with names and descriptions' and implies it is a search operation. However, it does not disclose whether the tool is read-only, whether it has side effects, or any rate limits. With no annotations, more detail would be beneficial, but the description is not misleading.

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 long, front-loading the core purpose and a clear usage directive. Every sentence adds value: the first states the action, the second describes the output, the third gives when-to-use guidance. No wasted 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 that there is no output schema and only two parameters (both well-described), the description is largely complete. It explains the tool's purpose, input, output, and usage context. It could be improved by mentioning whether results include tool IDs or how to subsequently invoke them, but it is sufficient for an agent to use 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 description coverage is 100%, so the schema already documents both parameters. The description adds value by providing an example query ('analyze housing market trends'), which clarifies the intended use of the 'query' parameter. For 'limit', it restates the default and max values (already in schema) but no additional semantics. Overall, the description enhances understanding 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's purpose: to search the Pipeworx tool catalog by describing what you need and return the most relevant tools. It specifies the action ('Search'), the resource ('Pipeworx tool catalog'), and the input type (natural language description). This distinguishes it well from siblings like ask_pipeworx (which likely answers questions) or recall/remember (which handle memory).

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 tells when to use this tool: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This provides clear usage guidance, implying that for a large toolset, discovery should precede invocation. No alternatives are explicitly named, but the context of sibling tools and the description's directive are sufficient.

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 full burden. It states deletion but does not disclose whether deletion is irreversible, whether it affects other data, or any 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?

Single sentence, zero waste. Every word is necessary and directly conveys the action.

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 (one required param, no output schema), the description is adequate but lacks behavioral details (e.g., what happens if key doesn't exist) that would make it 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 coverage is 100%, and the description adds no additional meaning beyond what the schema's parameter description ('Memory key to delete') already provides. 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?

The description uses the specific verb 'Delete' and the resource 'stored memory by key', clearly distinguishing it from siblings like 'recall' (retrieval) and 'remember' (storage).

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 when-to-use or when-not-to-use guidance is provided, but the purpose is clear enough that an agent can infer it is for deletion when a memory is no longer needed. No alternative tools are mentioned.

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, description must disclose behavior. It explains both retrieval and listing modes. Could add that retrieval is read-only and has no side effects, but the description is clear enough.

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: first states function, second explains when to use. No filler, front-loaded with key action.

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 tool with one optional parameter and no output schema, the description covers retrieval and listing, and hints at cross-session persistence. Fully adequate.

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 one parameter 'key' already described. Description adds that omitting key lists all memories, which is not in schema description. Adds 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?

Clearly states the tool retrieves a memory by key or lists all if key is omitted. The verb 'retrieve' and resource 'memory' are specific, and the scope is well-defined.

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 says when to use (to retrieve context saved earlier) and what to do if no key (list all). Among siblings like 'remember' and 'forget', this distinguishes itself clearly.

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 persistence behavior (authenticated vs anonymous session lifetime) and that it is a write operation. It does not mention overwrite behavior on same key, which is a minor 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?

Two sentences with no redundancy. The first sentence states the action and resource, the second adds usage guidance and persistence details. 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?

Given no output schema and simple input schema, the description covers purpose, usage, and persistence. It lacks details on overwrite behavior or maximum value size, but overall is sufficient for a straightforward memory 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 value by explaining the purpose of the parameters in context (e.g., 'save intermediate findings, user preferences') and provides example keys in the schema description. This exceeds the baseline.

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 ('store') and resource ('key-value pair in session memory'), clearly distinguishing its purpose from siblings like 'recall' (retrieve) and 'forget' (delete). It explicitly states it is for saving context across tool calls.

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 explains when to use this tool ('save intermediate findings, user preferences, or context across tool calls') and hints at persistence differences for authenticated vs anonymous users. However, it does not explicitly state when not to use it or mention alternatives like 'recall' for retrieval.

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