diceuear

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 behaviors: the tool automatically selects data sources and fills arguments, handles natural language questions, and returns results. However, it doesn't mention potential limitations like rate limits, authentication needs, or error conditions, leaving some behavioral aspects unspecified.

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

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

The description is efficiently structured: it starts with the core purpose, explains the automation benefit, and provides concrete examples. Every sentence adds value without redundancy, making it easy to understand quickly. The examples are well-chosen to illustrate scope without being verbose.

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 with automated tool selection), no annotations, and no output schema, the description does well by explaining the core functionality and providing examples. However, it lacks details on response format, error handling, or data source limitations, which would be helpful for a tool of this nature.

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 'question' parameter. The description adds minimal value beyond the schema by emphasizing 'plain English' and 'natural language,' but doesn't provide additional syntax, format details, or constraints. 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: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer'), and distinguishes it from siblings by emphasizing natural language processing and automated tool selection, unlike other tools like discover_tools or list_styles.

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.' It provides clear alternatives (implicitly suggesting not to use other tools for complex queries requiring manual tool selection) and includes specific examples like 'What is the US trade deficit with China?' to illustrate appropriate use cases.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: it fans out to the 'right packs' based on bet classification, returns an evidence packet and a market-vs-model comparison. There is 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 somewhat lengthy but well-structured: it starts with the primary action, then details inputs, process, and usage examples. Every sentence adds value, though minor trimming could improve conciseness.

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 and the absence of an output schema, the description covers the return value sufficiently ('evidence packet plus a simple market-vs-model comparison'). It explains the fan-out logic and provides usage context. Some agents might desire more detail on the output structure, but it's adequate for most cases.

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

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

Schema coverage is 100%, so the schema already documents both parameters. The description adds meaning by explaining the 'quick vs thorough' depth options and clarifying the 'market' parameter input formats (slug, URL, or question text). 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 provides a specific verb ('Research') and resource ('Polymarket bet'), and clearly distinguishes the tool from sibling tools by focusing on Polymarket bets and summarizing Pipeworx data. It explains the resolution, classification, and fan-out process, making it unique among siblings like ask_pipeworx or validate_claim.

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 the tool with examples like 'should I bet on X?', 'what does the data say about this Polymarket market?', or 'is there edge in this bet?'. It does not mention when not to use it or alternative tools, but the context is clear enough for an 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?

No annotations provided, so description carries full burden. It discloses the tool retrieves data from specific sources and returns URIs, indicating a read-only, non-destructive operation. However, it lacks details on error handling, rate limits, or authentication requirements.

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 packing essential information without redundancy. Front-loaded with core purpose, then details, then impact statement. 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, the description mentions 'paired data + URIs' but does not detail the structure of the returned data (e.g., keys for metrics). For a tool with two parameters and moderate complexity, this is a notable gap but not critical.

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 clear parameter descriptions. The description adds value by providing examples and context (e.g., 'tickers/CIKs' for company, 'drug names') that reinforce and clarify schema semantics beyond 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 clearly states the tool compares 2-5 entities side by side, with distinct data fields for 'company' and 'drug' types. It specifies data sources (SEC EDGAR, FDA) and output format (paired data + resource URIs), distinguishing it from sibling tools like resolve_entity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for efficient multi-entity comparison, noting it replaces 8-15 sequential calls, but does not explicitly state when to use this tool over siblings (e.g., ask_pipeworx, resolve_entity) or provide 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 describes the search functionality and return behavior ('Returns the most relevant tools with names and descriptions'), but lacks details on error handling, performance expectations, or authentication needs, leaving 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 front-loaded with key information in two concise sentences, each earning its place by stating the tool's purpose and usage guidelines without any redundant or unnecessary details.

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 is mostly complete but could benefit from clarifying return format or limitations. It adequately covers purpose and usage but leaves some behavioral aspects unspecified.

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 ('query' and 'limit') adequately. The description adds minimal value beyond the schema by implying natural language input but does not provide additional syntax or format details, meeting the baseline for high coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('search', 'returns') and resource ('Pipeworx tool catalog'), distinguishing it from siblings like 'generate_avatar' and 'list_styles' by focusing on tool discovery rather than generation or listing of specific resources.

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?

It 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') and includes a usage context ('Search... by describing what you need'), clearly differentiating it from alternatives without being misleading.

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 exist, so the description carries the burden. It mentions the return format (pipeworx:// citation URIs) and the data scope, but does not disclose error handling for missing entities or potential response size limits. However, the read-only nature is implied.

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

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

The description is two sentences, front-loading the main purpose. It is efficient but could be slightly more structured (e.g., bullet points for data types). Still, 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, the description explains the return format (citation URIs) and data sources. It does not cover edge cases like missing data or response size, but for a profile tool with clear scope, it is adequately 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%, so baseline is 3. The description adds value by explaining type enumeration (only 'company'), value formats (ticker or CIK), and providing guidance to use resolve_entity for names, which 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 returns a full profile of an entity across multiple packs, listing specific data types (SEC filings, XBRL, patents, news, LEI). It distinguishes itself from usa_recipient_profile for federal contracts, which is a direct sibling.

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 (for companies, bundling multiple data sources) and when not to (federal contracts: use usa_recipient_profile). It also advises using resolve_entity first if only a name is available, and states it replaces 10-15 sequential calls.

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 states 'Delete', implying a destructive mutation, but doesn't disclose critical behavioral traits: whether deletion is permanent, requires specific permissions, has side effects, or what happens on success/failure. For a destructive tool with zero annotation coverage, this is a significant gap in transparency.

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 waste. It's front-loaded with the core action ('Delete') and resource, making it immediately understandable. Every word earns its place, and there's no redundant or verbose 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 destructive nature, lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'stored memory' entails, the implications of deletion, error conditions, or return values. For a mutation tool with minimal structured data, the description should provide more context to guide safe and effective 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?

The schema description coverage is 100%, with the parameter 'key' fully documented in the schema. The description adds no additional meaning beyond the schema's 'Memory key to delete'. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate or add value beyond the structured data.

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 unambiguous. It distinguishes from siblings like 'recall' (retrieve) and 'remember' (store), though it doesn't explicitly mention these alternatives. The purpose is specific but lacks explicit sibling 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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing memory key), exclusions, or contextual cues. Without annotations or output schema, this leaves the agent guessing about appropriate 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?

With no annotations provided, the description carries the full burden. It discloses the deterministic behavior ('Same seed + style always produces the same avatar') and output format ('SVG URL'), but lacks details on rate limits, error conditions, or performance characteristics that would be helpful for an agent.

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

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

The description is efficiently structured in two sentences: the first states the purpose and parameters, the second clarifies the return value. Every sentence adds value with zero 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?

Given the tool's low complexity (2 simple parameters, no output schema, no annotations), the description is reasonably complete. It covers purpose, deterministic behavior, and output format, though additional context like error handling or usage examples could 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 description coverage is 100%, so the schema already fully documents both parameters. The description adds no additional parameter semantics beyond what the schema provides, maintaining the baseline score 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 specific action ('Generate a DiceBear avatar SVG URL'), resource ('avatar'), and output type ('URL that renders the avatar inline'). It distinguishes from the sibling tool 'list_styles' by focusing on generation rather than listing available styles.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for generating avatars with specific styles and seeds, but provides no explicit guidance on when to use this tool versus alternatives or any prerequisites. The context is clear but lacks comparative or exclusionary advice.

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 only states what the tool does at a high level without mentioning any behavioral traits like whether this is a read-only operation, if there are rate limits, what format the list returns, or if authentication is required.

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 clearly states the tool's purpose with zero wasted words. It's appropriately sized for a simple list operation and front-loads the essential 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?

For a simple list tool with no parameters and no output schema, the description provides the basic purpose but lacks important context. Without annotations or output schema, it should ideally mention what the return format looks like (e.g., list of style names, objects with metadata) or any behavioral constraints.

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 lack of parameters. The description appropriately doesn't add parameter information beyond what's already covered, maintaining a baseline of 4 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 action ('List') and resource ('all available DiceBear avatar styles'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from the sibling tool 'generate_avatar' beyond the obvious list vs. generate distinction.

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 about when to use this tool versus alternatives. The description doesn't mention the sibling 'generate_avatar' tool or provide any context about when listing styles is appropriate versus generating an avatar.

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 rate limiting (5 messages per identifier per day) and that it is free. However, it does not describe what happens after sending (e.g., whether there is a response, if the feedback is stored, or any side effects). The word 'Send' implies a write operation, but no mutability or idempotency details are given.

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 four sentences, front-loaded with the core purpose, then usage guidelines and rate limit. Every sentence adds value with no redundancy. It is optimally concise for an AI agent.

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 3 parameters (one nested) and no output schema, the description covers purpose, use cases, content constraints, and rate limit. It does not explain the return value or authentication needs, but for a feedback submission tool, this is adequate. The missing output description is a minor gap.

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. The description adds guidance for the 'message' parameter (do not include prompts verbatim, be specific) but does not add meaning beyond the schema for the 'type' and 'context' parameters. Overall, minimal additional 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?

The description clearly states the verb 'Send feedback' and the resource 'Pipeworx team', and lists specific use cases (bug reports, feature requests, missing data, praise). It distinguishes from sibling tools like 'ask_pipeworx' by focusing on providing feedback rather than asking questions.

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 guidance ('Use for bug reports, feature requests, missing data, or praise') and content instructions ('Describe what you tried in terms of Pipeworx tools/data — do not include the end-user's prompt verbatim'). However, it does not mention when NOT to use or alternatives for related tasks like asking questions.

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?

Annotations (readOnlyHint=true, openWorldHint=true) are consistent. Description adds behavioral details: mode behavior, cross-event search, and return of ranked opportunities. No contradictions, and enough context beyond 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?

Single well-structured paragraph with clear mode separation using uppercase headers. Efficiently packed with information without being verbose, though slightly dense could be broken into bullet points.

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?

No output schema exists, but description states returns 'ranked opportunities with suggested trade direction + reasoning', sufficient for agent usage. All key behaviors (two modes, search, monotonicity check) are covered, making the description complete for this complex 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 covers both parameters with descriptions. Tool description greatly enriches meaning: explains event slug/URL, topic seed question, and cross-event search behavior, providing concrete examples and context far beyond the schema alone.

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 it finds arbitrage opportunities by checking monotonicity violations, clearly distinguishes event vs topic modes, and contrasts with single-event vs cross-event detection, making the purpose highly specific and differentiated from siblings.

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?

Explains two modes with examples and when each is appropriate (e.g., cross-event for separate event cutoffs). While no explicit 'when not to use', the guidance is clear and actionable for selecting the correct mode.

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?

Adds details beyond annotations: explains algorithm (lognormal model, FRED, group by asset, fetch once), ranking by |edge|, and suggested direction. Does not contradict 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?

Concise, front-loaded with purpose, well-structured in 3-4 sentences, no waste.

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?

Explanation of return values (top N with edge magnitude and direction) and algorithm steps sufficient for agent. Lacks details on 'Pipeworx data' but acceptable given 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?

Schema covers all parameters with descriptions (100% coverage). Description adds context on edge meaning and defaults but not much extra parameter-specific meaning.

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 it scans Polymarket markets for edge opportunities, specifies scope (crypto-price bets, V1), and is built for discovery. Distinguishes from manual browsing.

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?

Implied use for 'what should I bet on today' and contrasts with manual paging, but no explicit when-not or comparison to sibling tools like polymarket_arbitrage.

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 behavior (retrieve by key vs list all) and persistence across sessions, which is valuable. However, it doesn't address potential limitations like memory size constraints, retrieval failures, or performance characteristics that would help an agent use it effectively.

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 usage context. 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?

For a tool with 1 parameter, 100% schema coverage, and no output schema, the description provides good contextual completeness. It explains the tool's purpose, usage, and parameter semantics adequately. The main gap is the lack of output format information, which would be helpful since there's 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?

The schema has 100% description coverage, so the baseline is 3. The description adds meaningful context by explaining the semantic effect of omitting the key parameter ('omit to list all keys'), which clarifies the tool's dual functionality beyond what the schema alone 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 tool's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory by key', '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 usage guidance: 'Use this to retrieve context you saved earlier in the session or in previous sessions.' It also explains when to omit the key parameter ('omit key to list all keys'), offering clear context for parameter usage decisions.

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 the full burden of behavioral transparency. It describes the parallel fan-out to multiple sources, the return format (structured changes, total_changes, URIs), and the accepted date formats. It does not disclose potential rate limits or latency, but the detail provided is substantial.

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, consisting of four sentences that are front-loaded with the main purpose. Every sentence contributes meaningful information without repetition or fluff. It efficiently conveys essential details.

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 the absence of an output schema, the description specifies the return format (structured changes, total_changes count, URIs). It also notes the current limitation of only supporting 'company' type. For a tool with three parameters, this provides all necessary context for usage.

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

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

Schema coverage is 100%, so the schema already describes all parameters. The description adds value by elaborating on 'since' (ISO or relative formats with examples), clarifying 'value' (ticker or CIK), and explaining the overall behavior. 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 the tool's purpose: retrieving changes about an entity since a point in time. It specifies the entity type 'company' and the sources it fans out to (SEC EDGAR, GDELT, USPTO), making it specific and distinct from siblings like 'entity_profile'.

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 guidance: 'Use for brief me on what happened with X or change-monitoring workflows.' It also explains the input format for 'since' and 'value' parameters. However, it does not explicitly mention when not to use this tool or suggest alternatives, though 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively adds context beyond basic functionality by detailing persistence behavior ('authenticated users get persistent memory; anonymous sessions last 24 hours'), which is crucial for understanding data retention. It does not cover other potential traits like error handling or rate limits, but the added persistence info is valuable.

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 two sentences that efficiently convey purpose and behavioral context without waste. Every sentence earns its place: the first defines the core action and usage, and the second adds critical persistence details, making it concise yet informative.

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, usage, and key behavioral traits (persistence). However, it lacks details on return values or error cases, which would be helpful since there's no output schema. It compensates well but has minor 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 schema description coverage is 100%, so the schema already fully documents both parameters ('key' and 'value'). The description does not add any additional meaning or syntax details beyond what the schema provides, such as examples of key usage or value constraints. Baseline 3 is appropriate as the schema handles 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 ('store a key-value pair') and resource ('in your session memory'), distinguishing it from sibling tools like 'recall' (retrieval) and 'forget' (deletion). It explicitly mentions what gets stored ('intermediate findings, user preferences, or context across tool calls'), making the purpose unambiguous and distinct.

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 helps differentiate it from alternatives like 'recall' for retrieval. However, it does not explicitly state when not to use it or mention specific alternatives by name, such as contrasting with 'forget' for deletion or 'discover_tools' for other purposes.

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; description fully handles transparency. It discloses expected outputs (ticker, CIK, name, URIs) and acceptable input formats (ticker, CIK, name). As a read-only lookup, no side effects are implied. Could mention error handling for unknown entities, but overall 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?

Every sentence is essential: purpose, version, input examples, output list, and value proposition. No redundancy, perfectly front-loaded.

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

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

Given two simple parameters and no output schema, the description adequately covers input and output. It includes version info and efficiency benefit. A minor gap is handling of unknown entities, but not critical.

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 value by explaining that 'value' can be a ticker, CIK, or company name with specific examples (AAPL, 0000320193, Apple). This enriches the schema 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 it resolves entities to canonical IDs, specifies v1 supports company type, and gives concrete examples of inputs (ticker, CIK, name) and outputs (ticker, CIK, company name, URIs). It distinguishes from sibling tools by emphasizing it replaces 2–3 lookup 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: when needing canonical IDs for entities, and that it replaces multiple calls. It does not explicitly state when not to use or list alternatives, but the context is clear enough for an agent.

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 the description carries full burden. It discloses the return values including verdict, structured form, actual value, citation, and delta, and explains the tool's purpose. It does not discuss side effects or error handling, but the disclosure is adequate for a read-only fact-check 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?

Two sentences, no redundant words, front-loaded with the main purpose. Every sentence adds essential 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?

Given one parameter, no output schema, and no annotations, the description provides a good overview of the tool's behavior, domain, and output. It could mention handling of out-of-domain claims, but the stated scope is clear.

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% coverage with a clear description for 'claim'. The description adds value by providing concrete examples (e.g., 'Apple's FY2024 revenue') and explaining the supported domain, going beyond the schema's default 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 fact-checks natural-language claims, specifies the domain (company-financial for US public companies), and lists the verdict types. It distinguishes itself from siblings by noting it replaces 4-6 sequential agent 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 limits usage to company-financial claims for public US companies and mentions the data sources (SEC EDGAR + XBRL). It implies when to use (instead of multiple calls) but does not explicitly state alternatives or exclusions.

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