zenquotes

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 that Pipeworx 'picks the right tool, fills the arguments, and returns the result,' which covers the automation aspect. However, it doesn't disclose potential limitations like rate limits, error handling, authentication needs, or what happens with ambiguous questions, leaving gaps 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 front-loaded with the core functionality in the first sentence, followed by explanatory details and concrete examples. Every sentence earns its place by clarifying use cases or benefits, with no redundant information. It's appropriately sized for a single-parameter tool with a clear 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's complexity (natural language processing to select and invoke tools), lack of annotations, and no output schema, the description is somewhat incomplete. It explains the high-level process but doesn't cover behavioral aspects like error cases, performance, or result formatting. For a tool with no structured safety or output information, more detail would be beneficial.

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 the parameter 'question' fully documented in the schema. The description adds minimal value beyond the schema by emphasizing 'plain English' and 'natural language,' but doesn't provide additional syntax, format, or constraint details. This meets the baseline of 3 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 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 list_quotes or discover_tools. The examples further clarify its specific use case for natural language queries.

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: 'No need to browse tools or learn schemas — just describe what you need.' This provides clear guidance that it's an alternative to manually selecting and configuring other tools, and the examples illustrate appropriate use cases like factual queries or data lookups, helping differentiate it from 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?

Describes market resolution, bet classification, fan-out to packs, and output comparison. Adds behavioral context beyond annotations (readOnly, openWorld) without contradiction.

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 paragraph with front-loaded purpose, no fluff. Every sentence adds value, covering input, process, output, and usage efficiently.

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?

Comprehensive for a complex tool: input formats, behavioral logic, output description, and use cases. No missing critical info despite no 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?

For depth, explains 'quick' vs 'thorough' with evidence source counts. For market, provides three input formats and example slug, exceeding 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?

Clearly states the tool 'researches a Polymarket bet by pulling relevant Pipeworx data'. Specific verb, resource, and input examples distinguish it from siblings like ask_pipeworx.

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 lists use cases ('should I bet on X?', 'what does the data say?') and contrasts with alternative discovery approach, guiding agent when to invoke.

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 data sources (SEC EDGAR, FAERS, FDA) and return type (paired data + citation URIs), but lacks details on side effects, authentication requirements, rate limits, or error handling. Additional context on behavior beyond the basic operation 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 moderately concise with six sentences, each serving a purpose. It is front-loaded with the core function and usage triggers. However, it could be slightly tighter without losing clarity, and some redundancy exists (e.g., mentioning 'paired data' and 'citation URIs' could be combined).

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 covers the purpose, input details, and high-level output. It mentions return values but lacks specifics about the structure of the paired data. Given the complexity of multi-entity comparison, additional details on the output format or examples of returned data would improve completeness.

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 with descriptions for both parameters. The description adds value by explaining the meaning of type values and providing examples for the values array (tickers vs drug names). It goes beyond the schema by clarifying the entity-specific input formats and constraints like 2-5 items.

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 companies or drugs side by side, with specific data points for each type. It uses strong verbs and resource specification, and the distinction between company and drug types is explicit, differentiating it from potential sibling tools like entity_profile which likely handles single entities.

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 when-to-use examples like 'compare X and Y' and 'how do X, Y, Z stack up', and highlights efficiency by replacing 8-15 sequential calls. However, it does not mention when not to use the tool or alternative tools like entity_profile for single entities.

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 the tool's behavior: it's a search operation that returns relevant tools with names and descriptions, and it should be called first in specific scenarios. However, it doesn't mention potential limitations like rate limits, authentication needs, or error conditions.

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 with two sentences that each earn their place. The first sentence explains the core functionality, and the second provides crucial usage guidance. There's no wasted language or 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 moderate complexity (search functionality with 2 parameters), no annotations, and no output schema, the description does a good job of explaining purpose and usage. However, it doesn't describe what the return format looks like (beyond 'names and descriptions') or potential error cases, leaving some gaps in completeness.

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 doesn't add any additional parameter semantics beyond what's in the schema (e.g., it doesn't explain query formatting nuances or limit implications). The baseline score of 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 specific action ('Search the Pipeworx tool catalog'), the resource ('tool catalog'), and the method ('by describing what you need'). It distinguishes this from sibling tools like list_quotes, random_quote, and today_quote by focusing on search functionality rather than quote retrieval.

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 specific threshold (500+ tools) and context (finding tools for a task). It clearly differentiates this from the quote-related sibling 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?

No annotations are present, so the description carries full burden. It details what data is returned: SEC filings, fundamentals, patents, news, LEI, plus citation URIs. It does not mention side effects, but it's a read-only operation; a 4 is appropriate for good 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 compact and front-loaded with use cases. It is slightly dense but every sentence adds value. A minor trim could improve conciseness, but it remains effective.

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, the description enumerates all returned data categories and specifies input constraints. It addresses the common scenario of needing to resolve names first, making it complete for its 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 coverage is 100% but description adds critical context: type is restricted to "company", value expects ticker or CIK, and explicitly states names are unsupported with a pointer to resolve_entity. This goes 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: "Get everything about a company in one call" and provides specific example queries. It distinguishes itself from sibling tools like resolve_entity by clarifying that it requires a ticker or CIK, not a name.

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 provides when to use (user asks about company) and when not to (if only have name, use resolve_entity first). It also suggests alternatives, making it easy for the agent to decide.

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. While 'Delete' implies a destructive mutation, it doesn't specify whether the deletion is permanent, reversible, requires specific permissions, or has side effects (e.g., affecting other tools). It lacks details on error handling, rate limits, or response format, leaving significant behavioral gaps.

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 that directly states the tool's purpose with zero wasted words. It is appropriately sized for a simple tool and front-loaded with the essential action, making it easy for an agent 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 (1 parameter, 100% schema coverage, no output schema), the description is adequate as a minimum viable explanation. However, for a destructive operation with no annotations, it should ideally include more behavioral context (e.g., permanence, permissions) to be fully complete, leaving room for improvement.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds meaningful context by specifying that the 'key' parameter refers to a 'memory key', which clarifies the parameter's purpose beyond the schema's generic 'Memory key to delete'. With 100% schema description coverage and only one parameter, this additional semantic detail elevates the score above the baseline of 3.

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 resource ('a stored memory by key'), distinguishing it from sibling tools like 'recall' (likely retrieves) and 'remember' (likely stores). It uses precise verb+resource terminology that leaves no ambiguity about 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?

The description provides no guidance on when to use this tool versus alternatives like 'recall' or 'remember', nor does it mention prerequisites (e.g., needing an existing memory key) or exclusions. It states what the tool does but not when it should be selected over other tools in the context.

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 batch size (50) and randomness, but doesn't mention other behavioral traits like rate limits, whether quotes are unique per call, or if there's pagination for more than 50. For a tool with zero annotation coverage, this leaves significant gaps.

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 that front-loads key information (action, quantity, type). There's zero waste—every word contributes to understanding the tool's purpose and scope.

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 low complexity (0 parameters, no output schema), the description is adequate but minimal. It covers what the tool does but lacks details on output format (e.g., structure of quotes) or behavioral context, which would be helpful despite the simplicity.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, earning a baseline score of 4 for not adding unnecessary information.

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 ('Get') and resource ('inspirational quotes'), specifying quantity ('batch of 50') and selection method ('random'). It distinguishes from siblings by not being a single quote (random_quote) or date-specific (today_quote), though it doesn't explicitly name them.

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?

Usage is implied by the description: use when you need multiple random quotes rather than a single one or today's quote. However, it doesn't explicitly state when to choose this over random_quote or today_quote, 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, description discloses rate limit (5 per identifier per day), free usage, no quota consumption, and that team reads digests daily affecting roadmap. Lacks mention of authentication or anonymity but adequate.

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 paragraph, front-loaded with purpose, then when-to-use, then behavioral details. Every sentence informative; no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers input semantics, usage context, rate limits, and impact for a simple feedback tool; no apparent gaps for an AI to invoke 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%; description echoes schema details (e.g., enum meanings, context object purpose) without adding new semantic value beyond reinforcement.

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?

Clear verb 'tell' and resource 'Pipeworx team' with specific feedback types (broken, missing, needs to exist). Distinct from sibling tools as no other tool handles feedback.

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 states when to use (bug, feature/data_gap, praise) and what not to do (don't paste end-user prompt). Also mentions rate limits and quota-free nature.

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 what the tool does but doesn't describe how it behaves - no information about response format, whether quotes are filtered by category, if there are rate limits, or what happens on failure. 'Get' implies a read operation, but that's the extent of behavioral insight.

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 that communicates the core functionality without any wasted words. It's appropriately sized for a simple tool and front-loads the essential information immediately.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read-only tool with no parameters and no output schema, the description provides the basic 'what' but lacks important context about what to expect in return. Without annotations or output schema, the agent doesn't know the response format, potential errors, or any behavioral constraints. The description is minimally adequate but leaves significant 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?

The tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the parameter situation. The description appropriately doesn't mention parameters since none exist, which is correct. Baseline for 0 parameters is 4.

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 ('Get') and resource ('a single random inspirational quote'), making the purpose immediately understandable. It distinguishes from siblings by specifying 'random' (vs. 'list' or 'today'), though it doesn't explicitly name the alternatives.

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 'random inspirational quote' - suggesting this is for when a user wants an arbitrary motivational quote. However, it doesn't explicitly state when to use this versus list_quotes or today_quote, nor does it provide any exclusion criteria or prerequisites.

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: it can retrieve from current or previous sessions, and supports both single-key retrieval and listing operations. However, it doesn't mention error handling for invalid keys, persistence mechanisms, or performance characteristics.

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 well-structured sentences with zero waste. The first sentence states the core functionality, the second provides usage context. Every word earns its place, and the most important information (retrieve/list behavior) 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?

For a single-parameter tool with good schema coverage but no output schema or annotations, the description provides adequate context about functionality and usage. It could be more complete by describing return format or error cases, but covers the essential operations clearly given the tool's simplicity.

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%, providing a solid baseline. The description adds meaningful context by explaining the dual functionality (retrieve vs list) based on parameter presence, and clarifies the purpose ('memory key to retrieve'). This goes 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 like 'remember' (store) and 'forget' (delete) by focusing on retrieval operations.

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 ('retrieve context you saved earlier') and when to omit parameters ('omit key to list all keys'). It distinguishes from 'list_quotes' and 'random_quote' by specifying memory retrieval versus quote operations.

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 that the tool fans out to SEC EDGAR, GDELT, and USPTO in parallel, and describes the return structure (structured changes, total_changes count, citation URIs). It explains the 'since' parameter formats. It lacks details on rate limits or authentication, but overall provides key behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise: a few sentences with purpose, usage guidance, parameter hints, and output structure. It fronts the core question and examples, with no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is moderately complex (parallel fan-out, 3 required params) with no output schema. The description covers input, behavior, and output format adequately. It could mention potential errors or internet dependency but is largely complete for an agent.

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%, but the description adds value by explaining 'since' formats and recommending '30d' or '1m', clarifying 'type' is only 'company', and noting 'value' can be ticker or CIK. This goes beyond 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 'What's new with a company in the last N days/months?' and provides concrete example queries like 'what's happening with X?'. It specifies the data sources (SEC EDGAR, GDELT, USPTO) and differentiates itself from sibling tools such as entity_profile and compare_entities by focusing on recent 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 lists when to use the tool with natural language examples (e.g., 'what's happening with X?', 'any updates on Y?'). It also gives guidance on the 'since' parameter ('use 30d or 1m for typical monitoring'). However, it does not explicitly state when not to use it or name alternatives, though siblings are not directly comparable.

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 storage duration differences between authenticated users (persistent) and anonymous sessions (24 hours), and hints at session-scoped memory. However, it doesn't mention potential limitations like storage capacity, key uniqueness constraints, or error conditions.

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 front-loaded with the core purpose in the first sentence. The second sentence provides usage guidance with concrete examples, and the third adds important behavioral context about authentication differences. Every sentence earns its place with no wasted words, 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?

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 like persistence differences. However, it doesn't describe what happens on success (e.g., confirmation message) or failure (e.g., error conditions), which would be helpful given the lack of output schema. The parameter coverage is adequate due to the comprehensive 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 schema description coverage is 100%, so the schema already fully documents both parameters. The description adds minimal value beyond the schema by mentioning examples of what to store ('intermediate findings, user preferences, or context'), but doesn't provide additional syntax, format, or constraint details. 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 ('store a key-value pair') and resource ('in your session memory'). It distinguishes from siblings like 'recall' (which likely retrieves) and 'forget' (which likely deletes) by focusing on storage. The description goes beyond the name 'remember' to explain what kind of data is stored and where.

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.' It distinguishes from potential alternatives by specifying the storage mechanism (session memory) and doesn't overlap with sibling tools like 'list_quotes' or 'random_quote'. The authentication context further clarifies usage scenarios.

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 the tool returns IDs plus 'pipeworx:// citation URIs'. It doesn't mention potential delays, authentication, or rate limits, but as a read-only lookup, the description is largely sufficient.

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?

Four tightly-written sentences with zero waste. Purpose stated first, followed by examples, return content, and usage recommendation. 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?

Despite no output schema, the description fully explains what the tool returns (IDs plus citation URIs). Given the tool's simplicity and the detailed examples, the description is contextually 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% with descriptive parameter docs. The description adds significant value by providing concrete examples ('Apple', 'Ozempic') and explaining acceptable input formats (ticker, CIK, name for company; brand/generic for drug).

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: looking up canonical identifiers for companies or drugs. It provides specific examples (Apple → AAPL/CIK, Ozempic → RxCUI) and lists the identifier systems (CIK, ticker, RxCUI, LEI). This distinguishes it from sibling tools like entity_profile or compare_entities.

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 advises to use this tool before others that need official identifiers, and notes it 'replaces 2–3 lookup calls'. This gives clear when-to-use guidance and implies efficiency benefits.

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 and effectively discloses key behavioral traits: it's a read-only operation (implied by 'Get'), has no parameters, and includes important context about caching behavior ('same quote for all requests within a given day'). It doesn't mention rate limits or authentication needs, but covers the essential behavior well.

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 with two sentences that each earn their place: the first states the purpose, and the second adds crucial behavioral context about daily consistency. There's zero wasted language or 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 simplicity (0 parameters, no annotations, no output schema), the description is nearly complete. It explains what the tool does and its caching behavior. A minor gap is the lack of information about the return format (e.g., text structure), but for a straightforward tool, this is 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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, maintaining focus on the tool's purpose and behavior, which aligns with the baseline expectation for zero-parameter tools.

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 quote of the day') and resource ('from ZenQuotes'), distinguishing it from siblings like 'list_quotes' and 'random_quote' by specifying it's a daily curated quote rather than a list or random 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 implicitly provides usage context by stating 'Returns the same quote for all requests within a given day,' which helps differentiate it from siblings that might return varying quotes. However, it doesn't explicitly name alternatives or provide when-not-to-use guidance.

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

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

With no annotations, the description carries the full burden. It discloses supported domain (company-financial claims for US public companies) and return values (verdicts, extracted form, actual value with citation, percent delta). It does not contradict any annotations (none provided).

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 informative without being overly verbose. It front-loads the core purpose and provides necessary details. Every sentence adds value, though it is slightly longer than strictly necessary.

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, the description explains return values. It also specifies domain limitations. However, it does not mention potential errors or rate limits, which would enhance completeness.

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% for the single 'claim' parameter, so baseline is 3. Description adds examples and clarifies expected natural-language format but does not add substantial new meaning beyond the schema's own parameter 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?

Description clearly states the tool's purpose: fact-checking natural-language factual claims against authoritative sources. It provides specific verbs (fact-check, verify, validate, confirm/refute) and gives example phrasings, along with the domain (company-financial claims via SEC EDGAR+XBRL). It also distinguishes itself by noting it replaces 4-6 sequential 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 explicitly states when to use: 'Use when an agent needs to check whether something a user said is true' with specific example phrasings. However, it does not explicitly state when not to use or mention alternatives among siblings, though it implies specialized scope.

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