Tarot Draw

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

No annotations are provided, so the description carries full burden. It discloses that the tool selects the best data source and fills arguments automatically, which implies some decision-making but does not detail limitations or behaviors like latency, scope of data sources, or potential errors. Still, it is mostly transparent for a high-level 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 concise: three sentences plus examples. It is front-loaded with the main purpose and efficiently adds examples for clarity. Every sentence adds value.

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 parameter, no output schema), the description is sufficiently complete. It explains the input and the behavior. However, it lacks details on what happens if the tool cannot find an answer, but that is a minor gap for a high-level query 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 description coverage is 100%, and the description adds meaning beyond the schema by explaining that the question should be in natural language and providing examples of what constitutes a valid request. This compensates for the schema's minimal description.

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 answers questions in plain English by selecting the best data source and filling arguments. It distinguishes itself from sibling tools by abstracting away tool selection and schema learning, which is a unique value proposition.

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 (when you need an answer from data without browsing tools) and provides examples. It does not explicitly exclude cases or mention alternatives, but the sibling tools are different enough (e.g., discover_tools, remember) that the usage context is clear.

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 carries full burden. It discloses return format (paired data + URIs) and data sources (SEC EDGAR, FDA), but does not mention rate limits, auth, or other behavioral traits. Adequate but not rich.

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 sentences, front-loaded with purpose. No redundant information. Every sentence is valuable.

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 no output schema, description explains return format, data sources, and efficiency benefit. Covers key aspects for a comparison tool. Missing edge cases but acceptable.

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%, baseline 3. Description adds meaning by detailing the specific fields returned per 'type' (e.g., revenue for company, adverse events for drug), which is beyond the schema descriptions.

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 states verb 'compare', resource 'entities', and specifies data fields per type. Distinguishes from siblings like resolve_entity and ask_pipeworx by focusing on side-by-side comparison.

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?

Description provides context for when to use (side-by-side comparison, efficient replacement for multiple calls). Does not explicitly state when not to use or name alternatives, but the context is clear.

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?

Description clearly states it returns 'most relevant tools' and indicates it performs a search, which implies no side effects. No annotations provided, so the description carries full burden; it does well but could mention if it modifies state or has auth needs.

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, front-loaded with purpose and then usage guidance. No wasted words; every sentence adds value.

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 simplicity (2 params, no output schema, no nested objects), the description is nearly complete. It explains what the tool does, when to use it, and how to call it. Could optionally mention that it searches tool descriptions, but not necessary.

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 provides a concrete example query ('analyze housing market trends') that adds meaning beyond schema. For the limit parameter, description doesn't add extra semantics beyond schema's default and max, but overall 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?

Description clearly states it searches the Pipeworx tool catalog by describing what you need, returning relevant tools with names and descriptions. It uses a specific verb ('search') and resource ('tool catalog'), and distinguishes from siblings by its unique discovery purpose.

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 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This provides clear when-to-use guidance and implies it's a prerequisite before other tools.

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 bears full responsibility for behavioral disclosure. It clearly states what data the tool returns and that it outputs 'pipeworx:// citation URIs.' It implies a read-only, batched operation. However, it does not explicitly mention idempotency, side effects, or error conditions, though for a query tool this is minor. The description is transparent enough for safe 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 concise, using four sentences with no fluff. The first sentence states the overall purpose, followed by a list of included data, then output specification, and finally an alternative for a specific case. Structure is front-loaded and 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?

For a tool with two parameters and no output schema, the description provides a thorough overview of what the profile includes, the output format, and when to use an alternative. It covers the essential context an agent needs to 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?

Input schema covers 100% of parameters with descriptions. The description adds no new parameter-level information beyond what the schema provides (e.g., 'value: Ticker or zero-padded CIK'). Since schema coverage is high, the baseline score of 3 is appropriate; the description does not enhance parameter understanding.

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 this tool returns a 'Full profile of an entity across every relevant Pipeworx pack in one call' and lists the specific data sources (SEC filings, revenue, patents, news, LEI). It distinguishes itself from sibling tools by noting that for federal contracts one should call usa_recipient_profile, and indirectly from resolve_entity by stating names are not supported. The verb+resource is specific and leaves no 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?

The description provides explicit guidance on when not to use this tool: 'For federal contracts call usa_recipient_profile directly (too slow to bundle).' It also advises using resolve_entity first if only a name is available. This clearly helps the agent decide between 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?

No annotations provided, so description must cover behavioral traits. It says 'Delete' implying destructive action, but does not mention irreversibility, authorization needs, or what happens if key doesn't exist.

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?

Extremely concise: one sentence with essential info. No wasted words, but could be slightly improved by front-loading the key parameter importance.

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?

Tool is simple (1 param, no output schema), but description omits behavioral details (e.g., idempotency, error handling) that would help an agent use it 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% (1 parameter described in schema). Description adds no additional meaning 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?

The description clearly states the action ('Delete'), the resource ('stored memory'), and the identifier ('by key'). It distinguishes itself from sibling tools like 'recall' (retrieve) and 'remember' (store).

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 the tool is for deleting memories, but does not explicitly state when to use it vs. other tools or any prerequisites (e.g., memory must exist). Sibling tools are not 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?

No annotations are provided, so the description carries full burden. It discloses the rate limit and that the tool is 'Free.' While it doesn't detail backend behavior (e.g., who receives feedback, how it's stored), for a simple feedback tool this is adequate. No contradiction with 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 three sentences, each serving a distinct purpose: purpose, usage guidance, and rate limit. It is front-loaded with the key action and use cases, making it easy to parse 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?

Given the tool's simplicity (feedback submission), the description covers all essential aspects: purpose, what to include/avoid, and constraints (rate limit). It is complete for an agent to decide if and how to invoke 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?

Schema description coverage is 100%, with all parameters well-described. The description adds extra context beyond schema (e.g., 'do not include the end-user's prompt verbatim' and the rate limit), which helps an agent craft appropriate messages.

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 purpose ('Send feedback to the Pipeworx team') and explicitly lists use cases (bug reports, feature requests, missing data, praise). It distinguishes itself from sibling tools (e.g., ask_pipeworx, compare_entities) by being the designated feedback channel.

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: it tells what to include (what was tried in terms of Pipeworx tools/data) and what not to include (end-user prompt verbatim). It also specifies the rate limit (5 messages per day). Although it doesn't explicitly state when not to use it, the purpose is sufficiently scoped.

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 carries full burden. Clearly states that the tool retrieves or lists memories, which is non-destructive. However, does not mention any side effects, rate limits, or whether listing returns full contents or just keys. Still, core behavior is transparent.

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, no filler. Front-loaded with the core action. Every part adds value.

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, description could explain what is returned (e.g., full memory content or just summary). But for a simple retrieval tool, the description is adequate. No annotations, but the tool is straightforward.

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 baseline is 3. Description adds value by clarifying the two usage modes (with key vs without) and the purpose of the parameter. Slightly redundant with schema but useful context for an agent.

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 uses specific verbs ('retrieve', 'list') and a clear resource ('stored memory by key'). Distinguishes between single retrieval and listing all, and explicitly states the use case: 'retrieve context you saved earlier'. Differentiates from sibling tools like 'remember' and 'forget'.

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 guidance on when to use the tool ('to retrieve context you saved earlier'), and describes two distinct usage modes (with key vs omit key). Does not mention alternatives but clearly implies this is for retrieval, not storage or forgetting.

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 fully discloses behavioral traits: parallel fan-out to three sources, accepted date formats (ISO and relative), and return structure (structured changes + count + URIs). This is comprehensive and leaves no ambiguity about side effects or behavior.

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 remarkably concise: three sentences that front-load the purpose, then detail behavior and use cases. Every sentence adds essential information without 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?

Given the tool's complexity (parallel fan-out, multiple sources, date formats, rich return), the description covers all necessary aspects: what it does, how it works, input details, and output structure. No output schema exists, so the description's mention of 'structured changes + total_changes count + pipeworx:// URIs' fills that gap completely.

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?

Although schema coverage is 100%, the description adds significant value by explaining the since parameter's format options with examples ('7d', '30d', '3m', '1y'), clarifying the type parameter's current limitation ('Only company supported'), and specifying acceptable value formats (ticker or CIK). This enriches the schema's basic descriptions.

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: 'What's new about an entity since a given point in time.' It then elaborates on the specific behavior for company entities, detailing the parallel fan-out to SEC EDGAR, GDELT, and USPTO. This differentiates it from sibling tools like entity_profile, which likely provide static profiles rather than temporal changes.

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 recommends usage for 'brief me on what happened with X' or change-monitoring workflows, providing clear context. However, it does not mention when not to use this tool or name alternatives among siblings, missing an opportunity to guide agent decision-making.

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 carries full burden. It discloses persistence behavior: authenticated users get persistent memory, anonymous sessions last 24 hours. This adds important context beyond basic store 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?

Three sentences, each purposeful: function, use case, persistence details. No wasted words, 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 simple schema (2 string params), no output schema, and no nested objects, the description is complete. It covers what, why, and behavioral notes, leaving no gaps.

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 baseline is 3. Description adds value by explaining the purpose of stored data (findings, preferences, context) and providing example keys, enhancing agent understanding of parameter 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 states the tool stores a key-value pair in session memory, specifying it saves findings, preferences, or context. It clearly differentiates from sibling tools like 'forget' and 'recall' by focusing on 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?

The description gives clear usage: save intermediate findings, preferences, context. It implies use across tool calls but does not explicitly state when not to use it or compare with 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?

Without annotations, description carries full burden. It discloses that the tool is a read-like operation (resolves and returns data), lists return fields (ticker, CIK, name, URIs), and mentions it replaces multiple calls. No contradictions, but could mention non-destructive nature explicitly.

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?

Description is two sentences with no filler. First sentence states global purpose, second provides version details and examples. Every sentence is purposeful 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?

For a simple tool with two parameters and no output schema, the description covers purpose, input formats, output components, and efficiency benefit. No gaps given the tool's scope.

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?

Input schema has 100% coverage, description adds value by providing concrete examples for the value parameter (AAPL, 0000320193, Apple) and specifying that only 'company' is supported for type, reinforcing schema constraints.

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 across Pipeworx data sources, specifies supported type (company), and lists accepted input formats (ticker, CIK, name). It distinguishes from siblings by describing a specific entity resolution function that consolidates multiple 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 indicates this tool is for resolving entities in a single call instead of multiple lookups. It provides clear context for v1 (only company type), but does not explicitly state when not to use it or compare to alternatives like ask_pipeworx.

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