nationalize

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it interprets natural language questions, selects appropriate data sources, and returns results. However, it doesn't mention limitations like rate limits, authentication needs, or potential errors, which would be helpful for a tool with no annotation coverage.

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

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

The description is well-structured and front-loaded with the core functionality. Every sentence adds value: the first explains the purpose, the second describes the mechanism, the third provides usage guidance, and the examples illustrate practical applications. There's no wasted text, and it's appropriately sized for a single-parameter 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 the tool's complexity (natural language processing with automatic tool selection) and no annotations or output schema, the description does a good job explaining what the tool does and how to use it. However, it doesn't describe the format or structure of returned answers, which would be important since there's no output schema. The examples help but don't fully compensate for this 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?

The schema has 100% description coverage, so the baseline is 3. The description adds value by explaining that the 'question' parameter should be in 'plain English' or 'natural language,' and provides concrete examples ('What is the US trade deficit with China?', etc.). This gives semantic context beyond the schema's generic 'Your question or request in natural language' 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'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'). It distinguishes itself from siblings by emphasizing natural language interaction rather than requiring tool-specific knowledge.

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). The examples further illustrate appropriate use cases, making it easy to distinguish from sibling tools like batch_predict or discover_tools.

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

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: the batch nature, request limit (10 names), and output format ('ranked nationality probabilities for each name'). However, it lacks details on error handling, rate limits, or authentication needs, leaving some gaps in behavioral context.

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

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

The description is front-loaded with the core purpose and efficiently conveys key details in two concise sentences. Every sentence earns its place by specifying the action, scope, limit, and output, with no wasted words or redundancy.

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

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

Given the tool's moderate complexity (batch prediction with a limit), no annotations, and no output schema, the description is largely complete. It covers purpose, usage, behavioral traits (batch, limit, output format), and parameters. However, it could improve by detailing error cases or response structure, slightly reducing 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%, with the schema fully documenting the 'names' parameter (array of strings, max 10 items). The description adds minimal value beyond the schema, mentioning 'multiple first names' and 'up to 10 names', but does not provide additional syntax or format details. Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the specific action ('Predict nationalities') and resource ('multiple first names'), distinguishing it from the sibling tool 'predict_nationality' by emphasizing batch processing ('multiple...in a single request') and the limit ('up to 10 names'). It avoids tautology by not merely repeating the tool name.

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

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

The description explicitly states when to use this tool: for processing 'multiple first names in a single request' with a 'maximum 10 names'. This directly contrasts with the sibling tool 'predict_nationality', which likely handles single names, providing clear alternative usage guidance without needing explicit 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?

Annotations already provide readOnly and openWorld hints. The description adds how the tool resolves, classifies, fans out to packs, and returns a comparison, going 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?

Four sentences, each adding value: purpose, process, use cases, and success metric. No wasted words.

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

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

No output schema, but description explains returns (evidence packet + comparison). It covers common use cases but omits error conditions 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 coverage is 100%; the description repeats the market parameter types but adds no new info. The depth parameter is already well-explained in 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 it researches Polymarket bets using Pipeworx data, specifies input formats (slug, URL, question), and distinguishes from sibling tools like ask_pipeworx and validate_claim by focusing on betting edge.

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?

Explicit use cases are given: 'should I bet on X?', 'what does the data say...', 'is there edge?'. No explicit alternatives mentioned, but the context implies this is the dedicated betting research tool.

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. Discloses return format (paired data + pipeworx:// URIs), data sources (SEC EDGAR for companies, FDA for drugs), and value range constraints. No destructive behavior 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?

Two sentences plus type-specific breakdown. Every sentence provides unique information. No fluff.

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

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

Tool has no output schema, but description sufficiently explains return values (paired data, resource URIs). Covers entity types, data fields, and the benefit of batching. No missing critical info.

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 descriptions, but the description adds meaning: explains that 'values' depend on 'type' (tickers/CIKs for companies, drug names for drugs). This goes 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?

States 'Compare 2–5 entities side by side in one call' with clear verb and resource. Differentiates from siblings by specifying entity types (company/drug) and data fields. No confusion with 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?

Explicitly explains when to use (comparing entities) and highlights efficiency ('Replaces 8–15 sequential agent calls'). Does not specify when not to use or name alternatives, but context is clear enough.

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 tool's function (searching and returning relevant tools) and some behavioral context (it should be called first in specific scenarios), but lacks details on rate limits, authentication needs, error handling, or what 'most relevant' means algorithmically. It provides basic operational context but misses deeper behavioral 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 sized and front-loaded, with two sentences that efficiently convey purpose and usage guidelines without any wasted words. Each sentence earns its place by providing critical information, making it highly concise and well-structured.

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

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

Given the tool's moderate complexity (a search function with 2 parameters) and the absence of annotations and output schema, the description does a good job of covering key aspects like purpose and usage. However, it lacks details on output format (e.g., structure of returned tools) and error cases, which would be helpful for an AI agent. It's mostly complete 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 input schema has 100% description coverage, so the schema already documents both parameters (query and limit) thoroughly. The description does not add any additional meaning or semantics beyond what the schema provides, such as examples of effective queries or implications of the limit parameter. It meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('Search the Pipeworx tool catalog') and resource ('tool catalog'), and explicitly distinguishes it from siblings by specifying it should be called FIRST when there are 500+ tools available. It provides a clear action and scope that differentiates it from batch_predict and predict_nationality.

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 guidelines: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This gives clear context for when to use this tool versus alternatives, including a specific threshold (500+ tools) and a directive on priority.

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

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

Describes the data returned and that it uses citation URIs. Notes performance benefit. Lacks explicit statement about idempotency or safety, but it's a read operation and no annotations provided.

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

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

Four sentences, front-loaded with purpose. 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?

Despite no output schema, the description fully explains what is returned, its benefits, and constraints. Covers all necessary aspects for an agent to decide and use the 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?

Adds crucial context beyond the schema: explains that type only supports 'company', value can be ticker or CIK, and that names are not supported, guiding the agent to use resolve_entity first.

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 returns a full profile of an entity across multiple data sources, listing specific types of data and distinguishing itself from sequential calls.

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

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

Explicitly says when to use (company profiles), what it replaces (10-15 sequential calls), and when to use an alternative (usa_recipient_profile for federal contracts). Also notes that names are not supported and suggests 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. It states 'Delete' implies a destructive mutation, but doesn't disclose if this is permanent, requires permissions, has side effects, or what happens on success/failure. For a mutation tool with zero annotation coverage, this is a significant gap in behavioral context.

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

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

The description is a single, efficient sentence with zero waste, clearly front-loading the core action. Every word earns its place, making it highly concise and well-structured.

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

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

Given this is a destructive mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, error handling, or return values, which are crucial for safe tool invocation in this 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 description coverage is 100%, with the parameter 'key' fully documented in the schema as 'Memory key to delete'. The description adds no additional meaning beyond this, such as key format or examples, so it meets the baseline score when the schema does the heavy lifting.

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

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

The description clearly states the verb ('Delete') and resource ('a stored memory by key'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'recall' or 'remember', which likely interact with the same memory system, so it misses the top 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 on when to use this tool versus alternatives. Given siblings like 'recall' (likely to retrieve memories) and 'remember' (likely to store memories), the description offers no context for choosing 'forget' over other operations or prerequisites for deletion.

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 the rate limit ('5 messages per identifier per day') and mentions the tool is free. This is good behavioral context for a feedback tool. However, it does not detail what happens after submission (e.g., if any confirmation is given), but the simplicity of the tool mitigates this.

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 extremely concise: three sentences that front-load the purpose, then provide usage guidelines and a behavioral note (rate limit). Every sentence adds necessary information without redundancy.

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

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

For a straightforward feedback tool with no output schema, the description covers purpose, usage instructions, and constraints. It does not explain return behavior (e.g., if a success message is sent), but the context is sufficient for an AI agent to use the tool correctly. The presence of well-documented parameters compensates for the lack of 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 input schema has 100% coverage with clear descriptions for each parameter, including enum values for 'type'. The description adds marginal value by instructing to be specific and limiting message length, but the schema already explains the parameters well. Thus a 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: 'Send feedback to the Pipeworx team.' It enumerates specific use cases (bug reports, feature requests, missing data, praise) which distinguishes it from sibling tools that perform other actions like prediction or data retrieval.

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

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

The description provides clear guidance on when to use this tool (for feedback) and gives instructions on what to include in the message: describe what you tried in terms of Pipeworx tools/data. It also explicitly states what not to include ('do not include the end-user's prompt verbatim'). It lacks explicit mention of when not to use it, but the context is sufficient for an AI 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?

Adds value beyond annotations: explains walking child markets, grouping, monotonicity checking, and returning ranked opportunities with suggested trade direction and reasoning. No contradiction with readOnlyHint or other 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 paragraph, clearly structured with explicit 'TWO MODES' labeling and parenthetical examples. Every sentence adds information, though the parenthetical could be slightly trimmed without loss.

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, but description explains return (ranked opportunities with trade direction + reasoning). Covers both modes' mechanisms and use cases adequately for an agent to assess suitability and expected results.

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 enriches by elaborating mode-specific behavior and example inputs (e.g., 'Strait of Hormuz traffic returns to normal'), adding context beyond the schema's short 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 finds arbitrage opportunities via monotonicity violations, specifies two distinct modes (event and topic), and distinguishes from siblings by highlighting cross-event mode's ability to catch cases missed by single-event 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?

Explicitly describes when to use each mode with concrete examples (event slug vs. topic seed), and notes the cross-event mode catches Polymarket's separate-event structure. Lacks explicit 'when not to use' but provides sufficient context for correct invocation.

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 significant context: the lognormal model from FRED and coinpaprika, grouping by asset, caching price history, ranking by |edge|, and returning suggested trade direction. 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 concise yet packed with essential details. It front-loads the purpose, explains the algorithm, data sources, and output format in a logical flow. Every sentence earns its place without redundancy.

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

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

Given the tool's complexity (model-based ranking, multiple parameters, no output schema), the description is remarkably complete. It covers the model, data sources, processing steps, and output. Minor omission: no mention of error handling or rate limits, but these are less critical for a read-only 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?

The input schema has 100% description coverage, so the description does not need to restate parameters. It mentions 'limit, window, min_edge_pp' in context but adds no new semantics beyond the schema descriptions. Baseline 3 is appropriate.

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

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

The description clearly states the verb 'scan', the resource 'Polymarket markets', and the specific output criterion 'where Pipeworx data disagrees most with the market price'. It distinguishes from siblings like 'polymarket_arbitrage' by focusing on opportunity discovery for a daily bet question.

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 positions the tool for the 'what should I bet on today' question, providing clear when-to-use guidance. It does not enumerate alternatives or exclusion criteria, but the sibling tools suggest different use cases (e.g., arbitrage, entity research), so the context is sufficient.

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

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: returns ranked results by probability, up to 5 country codes with scores. However, it doesn't mention accuracy limitations, data sources, rate limits, or error conditions that would be helpful for a prediction 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 perfectly concise: two sentences with zero wasted words. The first sentence states the purpose, the second specifies the output format. Every element 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 single-parameter prediction tool with no annotations and no output schema, the description is adequate but has gaps. It explains what the tool does and the output format, but doesn't address confidence thresholds, limitations, or what the country codes represent (ISO codes?).

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 the single 'name' parameter. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 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 ('predict'), resource ('nationalities for a given first name'), and scope ('most likely... ranked by probability'). It distinguishes from the sibling tool 'batch_predict' by specifying this is for a single name, not batch processing.

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: when you need nationality predictions for a single first name. It doesn't explicitly state when not to use it or mention the sibling 'batch_predict' as an alternative, but the single-name focus is implied.

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 describes the dual behavior (retrieve by key vs list all) and mentions persistence across sessions, but doesn't disclose important behavioral traits like error handling (what happens if key doesn't exist), response format, or whether listing returns just keys or full memories. The description adds some context but leaves gaps.

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

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

Two sentences that are front-loaded with core functionality. Every phrase earns its place: first sentence states the dual operations, second provides usage context. No redundant or unnecessary 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 no annotations and no output schema, the description should do more. While it covers purpose and usage well, it doesn't describe the return format (what a 'memory' looks like when retrieved, what format the list returns), error conditions, or persistence details. For a memory retrieval tool with zero structured metadata, this leaves significant gaps.

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

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

Schema description coverage is 100%, so the schema already documents the single parameter. The description adds valuable semantic context by explaining the conditional logic: 'omit key to list all keys' clarifies that the parameter is optional and its absence triggers a different behavior. This goes beyond what the schema provides about the parameter being optional.

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 sibling tools like 'remember' (store) and 'forget' (delete) by focusing on retrieval operations.

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

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

The description provides explicit guidance on when to use this tool ('retrieve context you saved earlier') and includes clear conditional logic ('omit key to list all keys'). It also distinguishes usage scenarios between retrieving specific memories versus listing all available ones.

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 core behavior: parallel fan-out to three sources, accepted 'since' formats, and return structure. However, it does not mention failure modes (e.g., missing entity, source unavailability), rate limits, or side effects, leaving some transparency gaps.

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

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

The description is very concise: 4 sentences that are front-loaded with purpose and progressively detail behavior. Every sentence adds unique information with zero fluff. It is well-structured 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 no output schema, the description explains the return values (structured changes, total_changes count, URIs). It covers supported entity types and parameter formats. However, it does not mention result limits, pagination, or how results from multiple sources are combined, which could be relevant for complex queries.

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 significant value beyond the schema by explaining the 'since' parameter format with concrete examples ('7d', '30d', '1y') and clarifying the fan-out behavior for type='company'. It also describes the return format, which is absent from 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 purpose: 'What's new about an entity since a given point in time.' It details the behavior for type='company', listing the three data sources (SEC EDGAR, GDELT, USPTO) and the return structure. It also gives explicit use case examples ('brief me on what happened with X'), making the purpose highly specific and actionable.

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

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

The description provides clear usage context with examples like 'brief me on what happened with X' and change-monitoring workflows. However, it lacks explicit guidance on when NOT to use this tool versus alternatives (e.g., entity_profile or compare_entities), and no mention of prerequisites or exclusions.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool performs a write operation (implied by 'Store'), specifies persistence differences ('Authenticated users get persistent memory; anonymous sessions last 24 hours'), and hints at session scope. However, it does not mention potential limitations like storage size, rate limits, 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 appropriately sized and front-loaded, with two sentences that efficiently convey purpose, usage, and behavioral details. Every sentence earns its place by adding distinct value: the first explains the core function and use cases, while the second clarifies persistence behavior without redundancy.

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

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

Given the tool's moderate complexity (write operation with session memory), no annotations, and no output schema, the description is largely complete. It covers purpose, usage, and key behavioral aspects like persistence. However, it lacks details on return values (e.g., success confirmation) and potential errors, which would enhance completeness for a mutation tool.

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

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

Schema description coverage is 100%, so the schema already fully documents both parameters ('key' and 'value'). The description adds minimal value beyond the schema by implying the parameters are used for storage but does not 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 specific action ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from siblings like 'forget' (delete) and 'recall' (retrieve). It provides concrete examples of what to store ('intermediate findings, user preferences, or context across tool calls'), making the purpose unambiguous.

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 offers clear context on when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), but does not explicitly state when not to use it or name alternatives. It implies usage for persistence needs but lacks explicit exclusions or comparisons to sibling tools like 'recall'.

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. Discloses version (v1), supported type (company), input formats, and return fields including resource URIs. Does not discuss error handling, auth, or rate limits, but covers core behavior adequately.

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

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

Three sentences, front-loaded with purpose, no filler. Efficiently conveys key information without redundancy.

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

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

Given moderate complexity (2 params, no output schema), description sufficiently explains inputs, outputs, and benefit. Lacks mention of error states or limitations beyond version 1, but is largely complete for 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?

Schema coverage is 100% (both parameters described). Description adds value beyond schema by clarifying accepted input formats (ticker, CIK, name) with concrete examples like 'AAPL' and 'Apple', which aids agent in properly invoking the tool.

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

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

Clearly states the tool resolves an entity to canonical IDs across Pipeworx data sources. Specifies it handles company type with ticker, CIK, or name, and returns ticker, CIK, name, and resource URIs. 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?

Describes when to use (single call for entity resolution) and provides examples of accepted inputs. Lacks explicit when-not-to-use or alternative tools, but the sibling context shows no overlap in functionality.

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 sources, return types (verdict, structured form, actual value with citation, percent delta), and the fact that it replaces multiple agent calls. Does not mention auth or rate limits, but for a read-heavy tool this is acceptable.

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

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

Two sentences front-load the purpose. Second sentence is slightly long but contains necessary details. No wasted words.

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

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

Given the single parameter and no output schema, the description adequately explains what the tool does and what it returns. Could detail error handling but is sufficient for typical 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?

Schema description coverage is 100% with an example in the schema. The description provides additional context by giving examples and clarifying the financial domain, but does not add meaningfully beyond the schema for the parameter itself.

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

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

Description clearly states the tool fact-checks natural-language claims, specifically company-financial claims for US public companies via SEC EDGAR + XBRL. It lists the verdict types and distinguishes itself from siblings by claiming to replace 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?

Specifies domain (company-financial, US public companies) and implicitly suggests when to use (fact-checking financial claims). Does not explicitly mention alternatives or when not to use, but the domain restriction is clear.

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