genderize

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: the tool automatically selects data sources and fills arguments, handles natural language input, and returns results. However, it doesn't mention potential limitations like response time, data freshness, or error handling, leaving some behavioral aspects unspecified.

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

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

The description is efficiently structured: a clear purpose statement, explanation of the automated mechanism, usage guidance, and three concrete examples. Every sentence adds value without redundancy. It's appropriately sized and front-loaded with the core functionality.

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 tool selection and execution) and lack of annotations/output schema, the description does well by explaining the tool's behavior and providing examples. However, without output schema, it doesn't describe what the return format looks like (structured data, text, etc.), leaving some uncertainty about 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 description coverage is 100%, with the single parameter 'question' well-documented in the schema. The description adds minimal parameter semantics beyond the schema, mainly through the examples that show what types of questions are appropriate. This meets the baseline expectation when schema coverage is high.

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 discover_tools or predict_gender. The examples further clarify its 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 explicitly states when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' This provides clear guidance to use ask_pipeworx for natural language queries instead of manually selecting tools like discover_tools or predict_gender. The examples reinforce appropriate use cases.

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

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

Annotations already indicate readOnlyHint=true, destructiveHint=false, and openWorldHint=true. The description adds specific behaviors: it resolves the market, classifies the bet type, fans out to relevant data packs, and returns an evidence packet with a market-vs-model comparison. It does not mention potential latency or rate limits, but overall transparency is good.

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 four sentences, front-loaded with the core action. Each sentence adds value: input specification, process, use cases, and strategic context. It is appropriately sized but could be slightly more compact without losing information.

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

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

Given the tool's moderate complexity (2 parameters, 1 required, no output schema), the description covers purpose, inputs, internal steps, and outputs. It explains the fan-out logic and the returned evidence packet and comparison. No output schema exists, but the output concept is clearly described.

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

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

Schema description coverage is 100%, so baseline is 3. The description does not add new meaning for the 'market' or 'depth' parameters beyond what the schema already provides (examples and enumeration details). The additional context about internal fan-out is behavioral, not parameter-specific.

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 action: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the input types and distinguishes from siblings like ask_pipeworx and compare_entities by focusing specifically on polymorph market analysis.

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 use cases: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?"' It also explains why this tool is preferable to generic discovery: 'agents that get bet-relevant context here convert better than ones that have to discover the packs themselves.'

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

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

Without annotations, the description discloses data sources (SEC EDGAR, FDA), return types (paired data + resource URIs), and efficiency gain. It implies a read-only operation but does not explicitly state safety or authorization 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 very concise (5 sentences), each sentence adds value without redundancy. It front-loads the core purpose and efficiently covers type differences, return format, and benefits.

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

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

The description covers inputs, outputs, and data sources. It lacks explicit error handling details or exact output structure, but given the tool's simplicity this is adequate. It meets the needs for a comparison 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%, so baseline is 3. The description adds meaning by explaining the type-specific return fields (revenue for company, trial count for drug) and providing input format examples, which goes beyond the schema descriptions.

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

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

The description clearly states the action ('compare 2–5 entities side by side'), specifies the two entity types with distinct data sources, and distinguishes itself from siblings by noting it replaces 8–15 sequential calls.

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

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

The description provides clear context on when to use (side-by-side comparison of companies or drugs) and includes examples of valid input values. However, it does not explicitly mention when not to use or suggest alternative tools from the sibling list.

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 that the tool returns 'the most relevant tools with names and descriptions', which adds behavioral context beyond basic search functionality. However, it lacks details on performance aspects like rate limits, error handling, or authentication needs, leaving gaps 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 front-loaded with the core purpose in the first sentence, followed by usage guidance, with no wasted words. Both sentences earn their place by providing essential information efficiently, 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 (search functionality with 2 parameters), no annotations, and no output schema, the description is mostly complete. It covers purpose, usage context, and behavioral output, but lacks details on return format specifics or error cases, which slightly reduces completeness for a tool without structured output documentation.

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. The description does not add any parameter-specific details beyond what the schema provides (e.g., it doesn't explain query formatting or limit implications further), resulting in a baseline score of 3 as the schema handles the heavy lifting.

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

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

The description clearly states the tool's purpose with specific verbs ('Search the Pipeworx tool catalog') and resources ('tool catalog'), and explicitly distinguishes it from siblings by emphasizing its role in discovering tools among 500+ available options, rather than performing predictions like the 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 provides explicit guidance on when to use this tool ('Call this FIRST when you have 500+ tools available and need to find the right ones for your task'), including a clear condition (500+ tools) and alternative context (vs. not using it when tools are limited or known).

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

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

No annotations provided, so the description must convey behavioral traits. It discloses that it returns pipeworx:// citation URIs and bundles multiple data sources. It does not mention auth or rate limits, but for a read-only aggregate 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?

Four sentences, front-loaded with purpose, no fluff. Every sentence provides essential information.

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

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

Despite lacking an output schema, the description outlines the types of data returned (filings, financials, patents, news, LEI) and the URI format. It is sufficient for an agent to understand the output scope.

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

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

Schema coverage is 100%, but the description adds significant value: it explains that value accepts ticker or CIK (not names) and directs the user to resolve_entity for names. For type, it confirms only company is supported currently.

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 returns a full profile of an entity across multiple relevant packs, specifies type='company', and lists the exact data sources (SEC, XBRL, USPTO, GDELT, GLEIF). It distinguishes itself from the sibling tool usa_recipient_profile 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 ('Replaces 10–15 sequential agent calls') and when not to ('For federal contracts call usa_recipient_profile directly'). Provides an alternative tool name and rationale.

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 for behavioral disclosure. It states 'Delete', implying a destructive mutation, but doesn't clarify permanence (e.g., irreversible), side effects, error handling (e.g., what happens if the key doesn't exist), or authentication needs. For a destructive tool with zero annotation coverage, this leaves critical gaps in understanding behavior.

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

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

The description is a single, clear sentence with zero wasted words. It's front-loaded with the core action ('Delete'), making it immediately scannable. Every word earns its place by conveying essential information without redundancy or fluff, achieving optimal conciseness for a simple 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 destructive nature, lack of annotations, and no output schema, the description is incomplete. It doesn't address critical context like what 'delete' entails (e.g., permanent removal), success/failure responses, or how it integrates with sibling tools. For a mutation tool with minimal structured data, more behavioral and usage details are needed to be fully informative.

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' documented as 'Memory key to delete'. The description adds minimal value beyond this, only reinforcing that deletion is by key. Since the schema already provides full parameter documentation, the baseline score of 3 is appropriate, as the description doesn't significantly enhance parameter understanding.

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

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

The description clearly states the action ('Delete') and the resource ('a stored memory by key'), making the purpose immediately understandable. It distinguishes from sibling tools like 'recall' (likely retrieving) and 'remember' (likely storing), though it doesn't explicitly compare them. The description avoids tautology by not just restating the tool name 'forget'.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing memory to delete), exclusions, or relationships with sibling tools like 'recall' or 'remember'. The agent must infer usage from the purpose alone, which is insufficient for optimal tool selection.

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

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

With no annotations, the description carries the full burden. It discloses rate limits and cost, but does not detail what happens after sending (e.g., confirmation, response time), or whether the operation is idempotent. The behavior is generally clear for a feedback tool but lacks depth.

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 concise sentences, each adding essential information: purpose, use cases, content guidance, and constraints. No wasted words, and critical details are front-loaded.

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

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

Given the tool's simplicity and lack of output schema, the description covers purpose, parameter semantics, usage constraints, and cost. It could mention that feedback is a one-way message with no guaranteed response, but this is a minor omission.

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 3. The description adds value by mapping enum values to use cases and explaining the 'context' and 'message' parameters with practical guidance (e.g., 'Be specific', '1-2 sentences typical'). This exceeds the baseline.

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

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

The description clearly states the tool's purpose: 'Send feedback to the Pipeworx team.' It then lists specific use cases (bug reports, feature requests, missing data, praise) and provides guidance on content, making the purpose highly specific and distinct from siblings.

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

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

The description explicitly states when to use (for various feedback types) and what not to include (end-user's prompt verbatim). It also mentions rate limiting and cost ('Free'). However, it does not explicitly contrast with sibling tools like 'ask_pipeworx', leaving some room for ambiguity.

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?

Annotated as read-only and non-destructive; description adds rich behavioral details: walks child markets, extracts dates/thresholds, sorts, and reports violations, fully disclosing internal logic.

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 with no wasted words: first states purpose and rule, second explains input and process; front-loaded with key 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?

Despite no output schema, description specifies return structure (list of {market_a, market_b, gap_pp, suggested_trade}) and covers all aspects: input, logic, and output; fully complete for a simple 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?

Schema already describes the 'event' parameter fully; description reinforces usage with a concrete example (slug format), adding marginal value beyond 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?

Description explicitly states verb 'Find', resource 'arbitrage opportunities within a Polymarket event', and method 'monotonicity violations', clearly distinguishing the tool's purpose 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?

Provides explicit instructions to pass event slug or URL and explains the arbitrage condition, but does not mention when to avoid using the tool or compare with alternatives.

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

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

Annotations indicate read-only and non-destructive behavior. The description adds valuable behavioral details: it fetches price history once (caching), computes model probability, ranks by edge magnitude, and suggests trade direction. This goes well beyond the annotations, though not exhaustive.

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 core purpose. It is relatively concise with no filler, though it could benefit from tighter organization (e.g., bullet points for parameters). 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?

No output schema is provided, but the description explains the return format (top N ranked by edge magnitude with suggested direction). It covers the model, data sources, and caching behavior. For a tool with 3 parameters and no output schema, this is adequate and fairly complete.

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

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

Schema coverage is 100% with parameter descriptions. The description reinforces defaults and constraints (e.g., 'Default 10, max 25') but adds limited new meaning beyond the schema. Baseline 3 is appropriate as schema already documents parameters.

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 function: scanning high-volume Polymarket markets and returning those with greatest disagreement between Pipeworx data and market price. It specifies the verb 'scan' and 'return', the resource 'Polymarket markets', and the unique comparative approach, distinguishing it from sibling tools like 'polymarket_arbitrage'.

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 directly addresses the user question 'what should I bet on today' and frames the tool as an opportunity discovery mechanism. It mentions coverage scope (crypto-price bets) and data sources, providing clear context. However, it lacks explicit 'when not to use' or alternative tool references.

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: the data source ('genderize.io'), return values (gender, probability, sample size), and probability range (0–1). However, it lacks details on error handling, rate limits, or accuracy limitations, which are important 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 a single, well-structured sentence that efficiently covers purpose, data source, and return values without unnecessary words. It is front-loaded with the core function and provides essential details concisely.

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

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

Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is reasonably complete. It explains what the tool does, the data source, and the return format. However, it could benefit from mentioning potential limitations or error cases to fully guide an agent.

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

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

Schema description coverage is 100%, with the parameter 'name' clearly documented as 'First name to predict gender for.' The description adds no additional parameter semantics beyond what the schema provides, such as format constraints or examples. Baseline 3 is appropriate since 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'), resource ('gender of a person'), and scope ('based on their first name, using global data from genderize.io'). It distinguishes from the sibling tool 'predict_gender_country' by specifying 'global data' rather than country-specific data.

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

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

The description implies usage context by mentioning 'first name' and 'global data', which suggests this tool is for general predictions without country filtering. However, it does not explicitly state when to use this vs. the sibling 'predict_gender_country' or provide exclusion criteria, leaving some ambiguity.

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. While it states the prediction is 'most likely' (implying probabilistic output) and 'calibrated to a specific country', it doesn't disclose important behavioral aspects like accuracy rates, confidence scores, data sources, limitations (e.g., handling of unisex names), or what happens with invalid inputs. For a prediction tool with zero annotation coverage, this represents significant gaps.

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

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

The description is a single, well-constructed sentence that efficiently communicates the core functionality. Every word earns its place - 'predict', 'most likely gender', 'person', 'first name', 'calibrated', 'specific country' - with no redundant information. It's front-loaded with the main purpose and appropriately sized for the tool's complexity.

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

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

Given the tool's moderate complexity (2 parameters, prediction functionality) and the absence of both annotations and an output schema, the description is minimally adequate. It covers the basic purpose and differentiator but lacks important context about behavioral characteristics, output format, and limitations. The description should do more to compensate for the missing structured information.

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

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

The schema description coverage is 100%, so the input schema already fully documents both parameters. The description adds marginal value by mentioning 'first name' and 'specific country' which aligns with the schema, but doesn't provide additional semantic context beyond what's already in the parameter descriptions. This meets the baseline expectation when schema coverage is complete.

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 the most likely gender'), target resource ('person based on their first name'), and key differentiator ('calibrated to a specific country') that distinguishes it from the sibling tool 'predict_gender'. It uses precise language that leaves no ambiguity about what the tool does.

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

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

The description implicitly provides usage context by specifying 'calibrated to a specific country', suggesting this tool should be used when country-specific gender prediction is needed. However, it doesn't explicitly state when to use this tool versus the sibling 'predict_gender' tool, nor does it provide any exclusion criteria or alternative 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 full burden. It describes the dual behavior (retrieve by key vs list all) and persistence across sessions, which is valuable. However, it doesn't disclose error behavior (what happens if key doesn't exist), format of returned data, or any limitations like memory size constraints.

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, zero waste. First sentence states the dual functionality clearly. Second sentence provides usage context. Every word earns its place with no 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?

For a retrieval tool with no annotations and no output schema, the description covers basic purpose and usage well. However, it lacks information about return format (what a 'memory' contains), error handling, and any system limitations. Given the simplicity of the tool (1 optional parameter), it's adequate but could be more 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%, so the schema already documents the optional 'key' parameter. The description adds meaningful context: explains the semantic effect of omitting the parameter (lists all keys) and connects the parameter to 'memory key to retrieve' concept. This goes beyond the schema's technical 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 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?

Explicit guidance is provided: use to retrieve context saved earlier in current or previous sessions. Clear conditional logic: 'omit key to list all keys' tells when to use each mode. No explicit alternatives mentioned, but the sibling tools have different purposes (store, delete, predict).

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 parallel fanning out to three sources, returns structured changes with count and URIs, and explains parameter formats. It does not mention authentication or rate limits, but covers core behavior well.

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

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

The description is concise, well-structured, and front-loaded with the core purpose. Every sentence adds necessary information without redundancy. It uses clear formatting with examples and 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 there is no output schema, the description adequately covers return values (structured changes, count, URIs). It explains the parallel data sources and parameter constraints. For a three-parameter tool with no nested objects, it is thoroughly complete.

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

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

Schema coverage is 100%, yet the description adds significant value: explains 'type' is limited to company, 'since' accepts ISO dates and relative strings with examples, and 'value' accepts tickers or CIK. It also recommends typical monitoring values ('30d' or '1m').

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 retrieves recent changes for an entity ('What's new about an entity since a given point in time'), specifies the supported type ('company'), and outlines data sources (SEC EDGAR, GDELT, USPTO). This distinguishes it from siblings like entity_profile or compare_entities.

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

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

The description provides explicit use cases ('brief me on what happened with X' or change-monitoring workflows') and explains parameter usage (since format). However, it does not explicitly state when not to use this tool or mention alternatives, though the context implies its purpose.

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 and does well by disclosing key behavioral traits: it explains persistence differences (authenticated users get persistent memory, anonymous sessions last 24 hours) and the cross-tool context capability. It doesn't cover rate limits or error conditions, but provides substantial operational 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 perfectly concise with two sentences that each earn their place: the first states the core functionality, the second adds crucial behavioral context about persistence. No wasted words, front-loaded with the main purpose.

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

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

For a 2-parameter tool with no annotations and no output schema, the description provides good context about the tool's behavior and usage. It could benefit from mentioning what happens on duplicate keys or the format of return values, but covers the essential operational aspects well given the 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?

The schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema properties. This meets the baseline expectation when schema coverage is complete.

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

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

The description clearly states the tool's purpose with specific verbs ('store a key-value pair') and resource ('in your session memory'), and distinguishes it from siblings by specifying it's for saving data across tool calls. It goes beyond the name 'remember' by explaining the storage mechanism.

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 on when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), but doesn't explicitly mention when not to use it or name alternatives among siblings like 'forget' or 'recall'. The guidance is helpful but lacks sibling differentiation.

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 bears full burden. It discloses input formats (ticker, CIK, name), return fields (ticker, CIK, company name, pipeworx:// URIs), and version limitation (v1 only supports 'company'). It does not cover error handling or rate limits, but for a simple resolution tool, this is adequate.

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

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

The description is extremely concise: two sentences with no redundant content. The first sentence front-loads the core purpose, and the second adds details on input and output. Every sentence earns its place.

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

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

Given the simple tool complexity (2 params, no output schema), the description sufficiently explains the purpose, input formats, and return elements. It lacks only an explicit statement of read-only behavior or potential side effects, but overall it is complete enough for correct selection and invocation.

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% (2 parameters, both described). The description adds value by providing concrete examples (e.g., 'AAPL', '0000320193', 'Apple') and specifying the input format, but it does not significantly extend beyond the schema's basic descriptions.

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

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

The description uses a specific verb ('resolve') and resource ('entity to canonical IDs'), and explicitly states it replaces 2–3 lookup calls, distinguishing it from sibling tools. It clearly conveys the tool's function 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 states this tool replaces multiple lookup calls, implying it is the preferred single-call option. However, it does not explicitly mention when not to use it or list alternative tools (e.g., if an entity type other than 'company' is needed).

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 fully discloses the return format (verdict, extracted form, actual value with citation, percent delta) and the domain limitations. It does not mention side effects or error handling, but it covers the key behavioral aspects.

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: the first defines purpose and domain, the second enumerates outputs and advantages. Each sentence adds essential information; zero waste.

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

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

Given the tool's complexity (NLP, financial domain, structured output), the description covers the core functionality, output structure, and domain constraints. Lacks error cases or usage restrictions, but is sufficiently complete for an agent to decide to use it.

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 single parameter 'claim' has a schema description with an example, but the tool description adds value by explaining it should be a natural-language factual claim and providing examples. This helps agents understand the input format beyond the schema.

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

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

The description clearly states the tool's purpose: fact-check a natural-language claim, specifically for company-financial claims via SEC EDGAR + XBRL. It lists the verdict types and output components, distinguishing it from sibling tools like ask_pipeworx or compare_entities.

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

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

The description specifies the supported domain (company-financial claims for US public companies) and notes that it replaces multiple agent calls, implying efficiency. It does not explicitly state when not to use or provide alternatives, but the context is clear enough for an agent.

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