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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: the tool automatically selects data sources and fills arguments, and it handles natural language questions. However, it lacks details on limitations (e.g., response time, data source availability, error handling) or output format, which are important for a tool with no output schema. The description doesn't contradict any annotations, but it could be more comprehensive.

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 examples. Every sentence adds value: explaining the automation benefit, contrasting with manual methods, and providing concrete use cases. It's efficiently structured without redundancy or 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?

Given the tool's complexity (automated source selection) and lack of annotations/output schema, the description does well by explaining the high-level behavior and providing examples. However, it could be more complete by mentioning potential limitations or the types of data sources covered. For a tool with no structured output information, additional context on result format would be helpful, but the examples partially compensate.

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

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

The input schema has 100% description coverage, with the 'question' parameter well-documented. The description adds semantic context by emphasizing 'plain English' and 'natural language,' and provides examples that illustrate the expected format and scope of questions. This enhances understanding beyond the schema's technical definition, though it doesn't detail constraints like length or supported topics.

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 search_drug_events or discover_tools by emphasizing natural language input without manual tool selection.

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

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

The description explicitly states when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It provides clear alternatives by implication (use other tools if you want to browse or learn schemas) and includes examples ('What is the US trade deficit with China?') to illustrate appropriate use cases, making it highly actionable.

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?

The description adds value beyond the annotations (readOnlyHint, openWorldHint, destructiveHint) by detailing the fan-out behavior: it resolves the market, classifies the bet, and fans out to the right data packs based on the classification. It also describes the output structure, providing transparency about what happens internally.

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 paragraph but contains all essential information without redundancy. Every sentence adds value, and the information is well-organized, starting with the core purpose and then providing details on inputs, behavior, and usage examples.

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 having no output schema, the description adequately covers the return values (evidence packet and market-vs-model comparison). It explains the fan-out logic based on bet classification, making the tool's behavior predictable. For a tool with only 2 parameters, all necessary context is provided.

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, but the description adds meaning beyond the schema. For 'market', it lists acceptable forms (slug, URL, question text). For 'depth', it explains the difference between 'quick' (2-3 evidence sources) and 'thorough' (full fan-out), which is not in the schema's enum 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: researching a Polymarket bet by pulling Pipeworx data. It specifies input types (slug, URL, question text) and output (evidence packet + market-vs-model comparison). It also distinguishes itself from sibling tools by claiming it is the core demo product and that agents using it convert better than those using other tools.

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 use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It implies when to use it over alternatives by stating that agents using this tool convert better, guiding the agent to prefer this tool for bet research.

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?

Despite no annotations, the description explains what data is returned (paired data, pipeworx URIs) and sources (SEC EDGAR, FDA), providing good behavioral context for a read-only 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 concise sentences that front-load the main purpose and provide key details without unnecessary words.

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

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

Covers both entity types, data sources, and output format (paired data, URIs). Lacks error handling or pagination info but is adequate for the tool's 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?

Description significantly enriches schema by detailing the fields returned for each 'type' and providing concrete examples for 'values' (tickers/CIKs, drug names), going well beyond the schema's enum and array definitions.

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 states 'Compare 2–5 entities side by side in one call' and specifies different data fields for 'company' vs 'drug' types, distinguishing it from sibling tools like 'search_drug_events' which focus on 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?

Clearly states the use case for comparing multiple entities efficiently, though does not explicitly mention when not to use it or alternatives.

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

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: the tool returns 'most relevant tools' (implying ranking/ relevance scoring) and has a default/max limit context (implied by the input schema's limit parameter description). However, it doesn't cover aspects like rate limits, authentication needs, or error handling, leaving gaps for a search tool.

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

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

The description is appropriately sized and front-loaded: the first sentence states the core purpose, and the second provides crucial usage guidance. Every sentence earns its place with no wasted words, making it efficient and easy to parse.

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 fairly complete. It covers purpose, usage context, and behavioral hints (like 'most relevant'), but lacks details on output format (beyond 'names and descriptions') and error cases, which could be improved for full 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 baseline is 3. The description adds minimal value beyond the schema: it mentions 'by describing what you need' which aligns with the query parameter, but doesn't provide additional semantics like query formatting tips or limit usage context beyond what's in the schema descriptions.

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

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

The description clearly states the tool's purpose with specific verbs ('Search the Pipeworx tool catalog') and resources ('by describing what you need'), and distinguishes it from sibling tools by emphasizing its role in discovering tools rather than searching specific domains like drugs or food recalls. It explicitly mentions the catalog context and the return format ('most relevant tools with names and descriptions').

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: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This clearly indicates when to use it (for initial discovery in large tool sets) and implies alternatives (sibling tools like search_drug_events for domain-specific searches), though it doesn't explicitly name 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, the description reveals that the tool returns 'pipeworx:// citation URIs', and that bundling is not suitable for federal contracts. However, it does not disclose error handling (e.g., missing entity) 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?

The description is a single paragraph of about 60 words with no fluff. It is front-loaded with the main purpose, then details included data, mentions citation URIs, and provides alternative guidance. Every sentence adds value.

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

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

Given the lack of output schema and annotations, the description provides a solid overview of inputs and outputs. It could be improved by hinting at response structure or error cases, but it is sufficiently complete for a two-parameter tool.

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

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

Schema coverage is 100%, and the description adds context beyond the schema: 'type' only supports 'company' now, 'value' accepts ticker or CIK, and names require resolve_entity first. This supplements the schema descriptions effectively.

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 every relevant Pipeworx pack' and lists specific data sources for company type. It distinguishes itself from calling multiple sequential tools and mentions an alternative for federal contracts.

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 this tool ('replaces 10–15 sequential agent calls') and when not to ('For federal contracts call usa_recipient_profile directly'). Also, the schema description for 'value' mentions that names are not supported and advises using resolve_entity first.

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. 'Delete' implies a destructive mutation, but the description doesn't disclose whether deletion is permanent, requires specific permissions, affects related data, or provides confirmation. For a destructive tool with zero annotation coverage, this is inadequate behavioral disclosure.

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 states the core functionality without any wasted words. It's appropriately sized for a simple tool and front-loaded with the essential action and resource.

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

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

For a destructive tool with no annotations and no output schema, the description is incomplete. It doesn't explain what constitutes a 'stored memory', how keys are formatted, what happens on success/failure, or return values. Given the complexity of deletion operations, more context is needed for 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 single parameter 'key' fully documented in the schema as 'Memory key to delete'. The description adds no additional parameter semantics beyond what the schema already provides, so it meets the baseline score when 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 action ('Delete') and resource ('a stored memory by key'), making the purpose immediately understandable. However, it doesn't differentiate this tool from potential siblings like 'recall' or 'remember' that might also interact with memories, so it doesn't reach the highest score.

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 prerequisites, when deletion is appropriate, or what happens after deletion. With siblings like 'recall' and 'remember' that likely read or create memories, the lack of comparative guidance is a significant gap.

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 must cover behavioral traits. It discloses rate limiting (5 per day) and that it's 'Free.' However, it does not mention what happens after sending (e.g., asynchronous, stored, no guarantee of reply). For a feedback tool, this is adequate but could be more transparent about processing.

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: purpose, usage guidelines, and rate limit. Front-loaded with the primary verb and resource. No redundant or filler content. 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?

For a simple feedback tool with no output schema and fully described parameters, the description covers all necessary aspects: purpose, when to use, how to format feedback, and constraints. It is complete and leaves no major gaps for the 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 coverage is 100%, so each parameter is described. The description adds value by explaining the form of 'message' (be specific, 1-2 sentences, 2000 chars) and the 'context' object. It also includes the rate limit which is not in schema. This goes beyond structural definitions.

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: 'Send feedback to the Pipeworx team.' It lists specific use cases (bug reports, feature requests, missing data, praise) which distinguishes it from sibling tools like ask_pipeworx. This provides unambiguous purpose and scope.

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

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

The description gives explicit usage guidance: what feedback types are appropriate and to describe attempts in Pipeworx tools/data without including end-user prompts. It also mentions rate limiting (5 per day). Missing are explicit when-not-to-use or alternative tools, but the context is clear enough given 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?

Annotations already indicate read-only and non-destructive behavior. The description adds rich behavioral context: it explains the two algorithmic modes (walking child markets vs. cross-event search) and the logic of monotonicity checking, which goes well beyond the 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 a single paragraph that front-loads the main purpose. It is mostly concise, though slightly longer due to detailed mode explanations. Every sentence contributes meaning, so it is well-structured overall.

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 that the tool returns 'ranked opportunities with suggested trade direction + reasoning,' which covers the return value. It also explains the two modes and their logic, making the tool fully understandable without additional context.

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

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

Schema coverage is 100% and the description in the schema already explains both parameters well. The description repeats this information but does not add new semantic depth beyond what the schema provides. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: finding arbitrage opportunities via monotonicity violations. It specifies two distinct modes ('event' and 'topic') with examples, making it easy to understand what the tool does and differentiating it from siblings like 'polymarket_edges'.

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 each mode, including an example showing why cross-event mode is necessary when events are split. It clearly distinguishes the use cases, leaving no ambiguity about which mode to choose.

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 indicate readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds significant behavioral context: it scans top markets, groups by asset, fetches price history once, computes model probability, ranks by edge, and returns suggested trade direction. No contradictions 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 a single paragraph of moderate length, front-loaded with the main action. It is efficient but could be more structured (e.g., bullet points) for easier scanning.

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 lacking an output schema, the description explains return values ('top N ranked by edge magnitude with suggested trade direction') and the overall process. For a tool with 3 parameters and no output schema, this is sufficiently complete.

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

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

Schema description coverage is 100%, with each parameter having a clear description. The tool description adds minimal new parameter-specific information beyond defaults and context, 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 that the tool scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price, specifying the scope (crypto-price bets) and model. However, it does not explicitly differentiate from sibling tools like 'polymarket_arbitrage', which may have overlapping purpose.

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

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

The description provides a clear use case: 'Built for the 'what should I bet on today' question — agents/users discover opportunities without paging through hundreds of markets by hand.' It implies when to use but does not list exclusions or alternatives.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the dual functionality (retrieve by key vs list all) and persistence across sessions, which is valuable. However, it doesn't mention potential limitations like memory size constraints, retrieval speed, or error conditions for invalid keys.

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. No wasted words, and information is front-loaded appropriately.

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

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

For a single-parameter tool with no output schema and no annotations, the description provides good coverage of purpose, usage, and parameter semantics. It could be more complete by mentioning what format the memories are returned in or any constraints, but it adequately covers the essential context 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?

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 behavior 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', '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 specifies when to omit the key parameter to list all memories, giving clear operational 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 key behaviors: parallel fan-out across SEC EDGAR, GDELT, USPTO; supported date formats (ISO and relative); return structure (structured changes, total_changes count, pipeworx:// URIs). It does not contradict any annotations.

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

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

The description is concise with four sentences. It is front-loaded with the core purpose, then details behaviors, date formats, and return structure. Every sentence adds value without redundancy.

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

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

Given the absence of an output schema, the description adequately explains return values. It covers all parameters with added semantics. It could mention potential prerequisites (e.g., internet access) or response size, but overall sufficient for agent understanding.

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

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

Input schema has 100% coverage with descriptions for all three parameters. The description adds value by giving specific examples for 'since' (e.g., '7d', '30d', '3m', '1y') and recommending '30d' or '1m' for typical monitoring. It also clarifies that 'type' is only 'company' and 'value' can be ticker or CIK.

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

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

The description clearly states the tool's purpose: 'What's new about an entity since a given point in time.' It specifies the supported type ('company') and the three data sources fanned out in parallel. The phrase 'Use for...' reinforces the intended use case.

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 suggests when to use: 'Use for 'brief me on what happened with X' or change-monitoring workflows.' However, it does not mention when not to use it or compare with sibling tools like entity_profile or compare_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 key behavioral traits: the persistence model (authenticated users get persistent memory, anonymous sessions last 24 hours) and the tool's purpose (storing data across tool calls). However, it doesn't mention potential limitations like storage capacity, key 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, followed by usage context and behavioral details. Every sentence earns its place with no wasted words, making it highly efficient and readable.

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 2-parameter tool with no annotations and no output schema, the description provides good coverage of purpose, usage context, and persistence behavior. However, it doesn't describe what happens on success/failure, return values, or potential error conditions that would be helpful for an agent invoking this tool.

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

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

With 100% schema description coverage, the input schema already documents both parameters thoroughly. The description doesn't add significant meaning beyond what the schema provides - it mentions 'key-value pair' but doesn't elaborate on parameter usage, constraints, or examples beyond what's in the schema descriptions.

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

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

The description clearly states the specific action ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from siblings like 'forget' (remove) and 'recall' (retrieve). It explicitly defines the tool's function as persistent storage rather than search or deletion 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 clear context for when to use the tool ('to save intermediate findings, user preferences, or context across tool calls'), but doesn't explicitly state when NOT to use it or mention specific alternatives among the sibling tools (like 'forget' for removal or 'recall' for retrieval).

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 full burden. It discloses that it is a single call, returns canonical IDs and URIs, and is currently limited to company type. It does not mention side effects or permissions, but the read-only nature is implied. The version limitation is transparent.

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

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

The description is two sentences that efficiently convey purpose, input format, output, and value proposition. No extraneous information; 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 enumerates return fields. It covers input constraints, output, and version limitations. Missing edge case handling (e.g., not found) but acceptable for a simple tool.

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

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

Schema coverage is 100%, but the description adds significant value by providing concrete examples (AAPL, 0000320193, Apple), explaining that v1 only supports 'company', and detailing the output format. This exceeds the baseline 3 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 resolves entities to canonical IDs, specifying inputs (ticker, CIK, name) and outputs (ticker, CIK, name, URIs). It distinctly differentiates from sibling tools by addressing a specific need for entity resolution.

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 the tool (resolving entities) and replaces multiple lookups, but does not explicitly state when not to use or mention alternative tools. However, given sibling tools are in different domains, this is adequate.

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 but lacks behavioral details. It mentions the return content but doesn't cover rate limits, authentication needs, pagination, or error handling. The description doesn't contradict annotations, but it's insufficient for a tool with no annotation support.

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 and front-loaded, stating the purpose and return content in two clear sentences without unnecessary details. It could be slightly improved by integrating usage context.

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 annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, output format, and usage guidelines relative to siblings, making it inadequate for a search tool with two parameters.

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 fully documents both parameters (query and limit). The description adds no parameter-specific semantics beyond what's in the schema, maintaining the baseline score 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 tool searches FDA adverse drug event reports and specifies what information is returned (patient reactions, drug details, outcomes). It distinguishes from sibling tools by focusing on drug events rather than labels or food recalls, though it doesn't explicitly contrast 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?

No guidance is provided on when to use this tool versus the sibling tools (search_drug_labels, search_food_recalls). The description implies usage for adverse event searches but doesn't specify scenarios or exclusions.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return content (label sections) but lacks critical details: whether this is a read-only operation, potential rate limits, authentication needs, error conditions, or pagination behavior. For a search tool with zero annotation coverage, this leaves significant gaps in understanding its operational traits.

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 concise with two clear sentences. The first states the action and resource, and the second specifies return content. There's no wasted verbiage, and information is front-loaded, though it could be slightly more structured by explicitly separating purpose from output 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 with two parameters), lack of annotations, and no output schema, the description is minimally adequate. It covers the core function and return sections but omits behavioral context, error handling, and output format details. It meets basic needs but leaves the agent under-informed about operational aspects.

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 minimal parameter semantics beyond the schema. It implies the 'query' parameter can include drug names or ingredients, but the schema already describes this with 100% coverage. No additional details about parameter interactions, defaults beyond 'limit', or search logic are provided, meeting the baseline for high schema coverage without adding significant 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 tool's purpose: searching FDA drug labeling (package inserts) and returning specific label sections. It specifies the verb 'search' and resource 'FDA drug labeling', making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'search_drug_events' or 'search_food_recalls' beyond the resource focus.

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 its siblings. It doesn't mention alternatives, prerequisites, or exclusions. While the resource focus (drug labeling) implies some context, there's no explicit comparison to 'search_drug_events' (likely adverse events) or 'search_food_recalls', leaving the agent to infer 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 are provided, so the description carries the full burden of behavioral disclosure. It mentions what data is returned but lacks critical behavioral details such as whether this is a read-only operation, potential rate limits, authentication requirements, error handling, or pagination behavior. For a search 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 appropriately sized and front-loaded, consisting of two clear sentences that state the tool's purpose and what it returns. There is no wasted text or redundancy, making it efficient. However, it could be slightly more structured by explicitly separating purpose from output 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 with two parameters), no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and return data but misses behavioral context and usage guidelines. For a search tool without annotations or output schema, it should do more to explain how results are structured or limitations.

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 input schema already fully documents the two parameters (limit and query). The description adds no additional meaning beyond what the schema provides—it doesn't explain parameter interactions, default behaviors beyond the schema's 'default 10', or search syntax nuances. Baseline 3 is appropriate when the schema does all 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: searching FDA food enforcement/recall records and returning specific information like product recalls, reasons, distribution patterns, and status. It uses specific verbs ('search', 'returns') and identifies the resource (FDA food enforcement/recall records). However, it doesn't explicitly differentiate from sibling tools like search_drug_events or search_drug_labels, which appear to search different FDA data domains.

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 sibling tools or other search options, nor does it specify prerequisites, exclusions, or optimal use cases. The only implied usage is for searching FDA food recall data, but this is redundant with the purpose statement.

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

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

With no annotations, the description bears full responsibility for behavioral disclosure. It mentions the data source (SEC EDGAR + XBRL), the return types (verdict, extracted form, actual value with citation, percent delta), and efficiency (replaces 4–6 agent calls). It does not mention non-obvious traits like latency or error handling, but the description is largely transparent.

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

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

The description is four sentences, front-loaded with the core purpose, followed by scope, output, and efficiency. Every sentence adds distinct value with no redundancy. It is perfectly concise for the complexity of the tool.

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

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

Given no output schema, the description covers the return values (verdict, extracted structured form, actual value with citation, percent delta). It does not specify exact object structure, but the tool is simple with one parameter, and the listed outputs are sufficient for an agent to understand what to expect.

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 specifying the claim type (natural-language, company-financial), providing examples, and explaining that the tool extracts a structured form. This goes beyond the schema description which only gives a generic example.

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 verb and resource: 'Fact-check a natural-language claim against authoritative sources.' It specifies the supported domain (company-financial claims for public US companies) and lists the verdict categories, making it distinct from sibling tools.

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 the tool to 'company-financial claims (revenue / net income / cash for public US companies),' implying when to use. However, it does not provide alternatives for other claim types or explicitly state when not to use, which would warrant a 5.

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