sunrisesunset

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that Pipeworx 'picks the right tool, fills the arguments, and returns the result,' which provides useful context about the tool's automated behavior. However, it doesn't mention potential limitations like rate limits, authentication needs, or what happens with ambiguous questions, leaving some behavioral aspects unclear.

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

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

The description is perfectly front-loaded with the core functionality in the first sentence, followed by supporting details about how it works, and concludes with concrete examples. Every sentence earns its place by adding distinct 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?

For a tool with one parameter and 100% schema coverage but no annotations or output schema, the description is reasonably complete about what the tool does and how to use it. However, it lacks information about return values, error handling, or any constraints on question types, which would be helpful given the absence 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% description coverage, so the schema already documents the single 'question' parameter. The description adds value by emphasizing it should be 'in plain English' and 'natural language,' and provides concrete examples that illustrate appropriate parameter usage beyond what the schema states.

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 from data source'), and distinguishes from siblings by emphasizing natural language input without needing to browse tools or learn schemas. The examples further clarify the 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 provides clear context on when to use this tool: for asking questions in natural language when you don't want to browse tools or learn schemas. It doesn't explicitly state when NOT to use it or name specific alternatives among siblings, but the 'no need to browse tools' implies this is the preferred option for natural language queries over manual 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?

Description adds significant behavioral context beyond annotations: it resolves markets, classifies bets, fans out to data packs, and returns evidence with model comparison. 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?

Compact yet comprehensive; front-loaded with core purpose and actionable guidance, no redundant sentences.

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 clearly states return type (evidence packet + market-vs-model comparison) and explains internal logic (classification, fan-out), making it complete for an agent.

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

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

Adds value beyond 100% schema coverage by explaining depth enum ('quick = 2-3 sources, thorough = full fan-out') and giving concrete examples for the market parameter (slug, URL, question text).

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 specific verbs ('Research', 'pulling', 'resolves', 'classifies', 'fans out') and clearly distinguishes from siblings by focusing on Polymarket betting context, with examples of use cases.

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 ('should I bet on X?', 'what does the data say...') and implies differentiation from other tools like ask_pipeworx and validate_claim.

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; the description implies a read operation (returns data) and mentions data sources (SEC EDGAR, FDA), but does not explicitly state it is non-destructive, rate limits, or auth needs. It covers return format (paired data + URIs) but lacks explicit behavioral warnings.

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 with no waste. The purpose is front-loaded, and each sentence adds distinct value: function, type details, return format, efficiency advantage.

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 fully covers return content per type and mentions resource URIs. It is complete for a comparison tool, addressing both use cases and the number of entities.

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 meaning by detailing what data is returned for each entity type (e.g., 'revenue, net income, cash, long-term debt' for company). This goes beyond the enum values and array 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 compares 2–5 entities side by side, specifies two entity types with explicit data fields (revenue, net income, etc. for company; adverse-event count, FDA approvals, trials for drug), and distinguishes itself from sequential calls. It is specific verb+resource with strong context.

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

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

The description explains when to use the tool (for efficient side-by-side comparison, replacing 8–15 calls) and provides context per type. However, it does not explicitly mention when not to use or alternative tools for single-entity queries.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it performs a search based on natural language queries, returns relevant tools with names and descriptions, and has a specific use case (large tool catalogs). However, it doesn't mention potential limitations like rate limits, authentication requirements, or error conditions.

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

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

The description is perfectly concise with two sentences that each serve distinct purposes: the first explains what the tool does, the second provides usage guidance. There's no wasted language, and the most important information (the search functionality) is front-loaded.

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

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

Given the tool's moderate complexity (search functionality with 2 parameters) and no output schema, the description provides good context about what the tool does and when to use it. However, without annotations or output schema, it could benefit from more information about return format, error handling, or performance characteristics to be fully 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 fully documents both parameters (query and limit). The description mentions 'describing what you need' which aligns with the query parameter but doesn't add meaningful semantic information beyond what the schema provides. The baseline score of 3 reflects adequate but not enhanced parameter documentation.

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

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

The description clearly states the specific action ('Search the Pipeworx tool catalog'), the resource ('tool catalog'), and the method ('by describing what you need'). It distinguishes this tool from its siblings (get_times, get_times_date) by focusing on tool discovery rather than time-related 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 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.' This gives clear context about the appropriate scenario and distinguishes it from alternatives by positioning it as an initial discovery step.

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 burden. It implies a read-only operation (profile lookup) and describes the return format (pipeworx:// URIs), but does not explicitly state behavioral traits like auth requirements or rate limits. It is sufficiently transparent for a non-destructive query 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 concise (4 lines) and well-structured, starting with the main purpose and following with specifics in a readable list format. 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?

No output schema is provided, but the description mentions the return format (pipeworx:// URIs) and what data is covered. It sets expectations by stating it replaces 10–15 sequential calls. It could add more about output structure or limits, but it's adequate for 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?

Schema coverage is 100%, but the description adds significant meaning: it explains that type currently only supports 'company', value can be ticker or CIK, and names are not supported. It also directs users to resolve_entity for names, providing context 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: 'Full profile of an entity across every relevant Pipeworx pack in one call.' It lists specific data sources (SEC filings, XBRL, patents, news, LEI) and explains the use case for 'company' type, distinguishing it from a sibling tool (usa_recipient_profile).

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

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

The description provides explicit guidance: when to use this tool (for comprehensive entity profiles) and when not to (for federal contracts, use usa_recipient_profile). It also advises using resolve_entity for names, offering clear context for usage.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'Delete' implies a destructive mutation, the description doesn't specify whether deletion is permanent, reversible, requires specific permissions, or has side effects. It also doesn't describe what happens on success/failure or return values.

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

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

The description is a single, efficient sentence that communicates the core functionality without any wasted words. It's appropriately sized for a simple deletion tool and front-loads the essential information.

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

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

For a destructive mutation tool with no annotations and no output schema, the description is insufficient. It doesn't address critical behavioral aspects like permanence, authorization requirements, error handling, or return values. The description should provide more context given the tool's destructive nature and lack of structured metadata.

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 'key' already documented as 'Memory key to delete' in the schema. The description adds no additional parameter context beyond what the schema provides, meeting 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 action ('Delete') and the target resource ('a stored memory by key'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'recall' or 'remember', but the verb 'Delete' provides clear functional distinction.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'recall' (which likely retrieves memories) or 'remember' (which likely stores memories). There's no mention of prerequisites, error conditions, or appropriate contexts for deletion operations.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states what data is returned but doesn't mention potential limitations (e.g., accuracy, availability for extreme locations), error conditions, or response format. While it implies a read-only operation, it lacks details about rate limits, authentication needs, or data freshness.

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

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

The description is a single, efficient sentence that front-loads the core purpose. Every word earns its place by specifying the timeframe, exact data points returned, and required resource. No redundant or unnecessary information is included.

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

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

For a simple read-only tool with 2 fully documented parameters but no output schema, the description adequately covers what data is returned. However, it lacks details about the return format (e.g., structured object vs. text), units, or timezone handling, which would be helpful given the absence of output schema and annotations.

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 (lat, lng) with examples. The description adds no additional parameter information beyond what's in the schema, maintaining the baseline score for high schema coverage.

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

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

The description clearly states the tool's purpose: retrieving specific astronomical times (sunrise, sunset, dawn, dusk, solar noon, golden hour) for a location. It specifies 'today's' timeframe and the resource (location), but doesn't explicitly differentiate from its sibling 'get_times_date' beyond the implied temporal scope difference.

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 (getting today's times for a location) but doesn't explicitly state when to use this tool versus 'get_times_date' or provide any exclusion criteria. The temporal scope 'today's' hints at the distinction, but no direct comparison or alternative guidance is given.

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 describes what the tool returns but doesn't disclose behavioral traits like error conditions, rate limits, authentication needs, or whether it's a read-only operation. The description is functional but lacks 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 a single, efficient sentence that front-loads the key information (what it gets) and includes all necessary details (specific times, date, location). There's zero waste, and every word 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 tool's moderate complexity (3 required parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose well but lacks details on behavioral aspects and output format, which are important for an agent to use it correctly without annotations.

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 the three parameters (lat, lng, date). The description adds no additional parameter semantics beyond what's in the schema, such as format examples or constraints. Baseline 3 is appropriate when schema does the heavy lifting.

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

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

The description clearly states the specific action ('Get') and the exact resources returned (sunrise, sunset, dawn, dusk, solar noon, golden hour times) for a specific date and location. It distinguishes from the sibling tool 'get_times' by specifying it's for a particular date rather than a broader time range or other parameters.

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 specifying 'for a specific date at a location,' but doesn't explicitly state when to use this tool versus the sibling 'get_times' or provide any exclusions or prerequisites. The guidance is present but not comprehensive.

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 fully handles behavioral transparency. It discloses rate limiting (5 messages per identifier per day) and the 'free' nature. It does not mention side effects like ticket creation, but the tool is low-risk (sending feedback).

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 with no wasted words. It front-loads the purpose and immediately follows with usage examples and constraints. Every sentence serves a 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 feedback tool with 3 parameters (optional nested object) and no output schema, the description covers purpose, usage guidance, rate limits, and parameter tips. It lacks details on what happens after submission (e.g., acknowledgement), but that is not critical for a feedback 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% with detailed descriptions for each parameter, so baseline is 3. The description adds value by providing context on how to write the message (avoiding verbatim prompts, referencing tools/data) and reiterating the type categories implicitly, earning a 4.

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

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

The description clearly states the tool's purpose: sending feedback to the Pipeworx team. It lists specific use cases (bug reports, feature requests, missing data, praise), making it distinct from sibling tools like ask_pipeworx which is for queries.

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

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

The description provides clear when-to-use scenarios and a specific prohibition (do not include end-user prompt verbatim). It also mentions rate limiting. It does not explicitly contrast with alternatives, but the use cases are distinct 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?

Annotations already indicate readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds valuable behavioral context: the tool 'walks the child markets, extracts dates/thresholds from each question, sorts them, and reports any pair where the rule is violated'. It also describes the return format. 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 well-structured and appropriately sized. It front-loads the core purpose, explains the underlying concept, details how the tool works, and specifies the output format. Every sentence adds value without redundancy.

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

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

Given that there is no output schema, the description takes on the burden of specifying the return type (list of {market_a, market_b, gap_pp, suggested_trade}). It covers the tool's logic, input, and behavior completely. The annotations provide necessary safety context. The description is fully sufficient for an agent to decide when and how to invoke 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?

Schema coverage is 100% for the 'event' parameter. The description adds meaning beyond the schema by explaining that the 'event' is a Polymarket event slug or URL, gives an example ('when-will-bitcoin-hit-150k'), and clarifies that the tool uses it to retrieve child markets and extract dates/thresholds.

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 explicitly states that the tool finds arbitrage opportunities by checking monotonicity violations within a Polymarket event. It explains the concept with a concrete example (P('BTC hit $150k by May 31') ≤ P('BTC hit $150k by Jun 30')) and distinguishes itself from any potential sibling tools by focusing on this specific logic.

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 clearly indicates when to use this tool: when the same event has multiple 'by [date]' or 'by [threshold]' markets and you want to detect mispricing. However, it does not explicitly mention alternatives or when not to use it, though the context is clear enough for an agent.

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

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

Annotations already indicate readOnly and non-destructive, but description adds rich detail: uses lognormal model from FRED + live coinpaprika, groups by asset, fetches price history once, ranks by |edge|, and returns top N with suggested 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?

Six sentences, each providing meaningful information. Front-loaded with core action. Could be slightly more concise, but overall efficient for the 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?

No output schema provided, but description explains what the tool returns: top N ranked by edge magnitude with suggested trade direction. Covers model, source data, and use case adequately for its complexity.

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

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

Schema coverage is 100% with descriptions for all 3 parameters. Description does not add new semantic meaning beyond schema, but it is consistent. Baseline 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?

Description uses specific verb 'scan' and resource 'high-volume Polymarket markets' with clear action: return markets where Pipeworx data disagrees most. Distinguishes from siblings like polymarket_arbitrage and ask_pipeworx by focusing on edge detection for betting opportunities.

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 it is built for the 'what should I bet on today' question, indicating it discovers opportunities without manual browsing. Does not explicitly state when not to use, but context is clear and 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's dual behavior (retrieve by key or list all), mentions persistence across sessions, and clarifies the optional parameter behavior. However, it doesn't address potential edge cases like what happens with invalid keys or empty storage.

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

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

The description is perfectly concise with two sentences that each earn their place. The first sentence states the core functionality with parameter guidance, and the second provides context about when to use it. No wasted words, front-loaded with essential information.

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

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

For a single-parameter tool with good schema coverage but no output schema, the description provides strong context about functionality and usage. It could be more complete by describing the return format (e.g., what a 'memory' contains) or error conditions, but it adequately covers the core use cases given the tool's simplicity.

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

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

With 100% schema description coverage, the baseline is 3. The description adds significant value by explaining the semantic meaning of omitting the key parameter ('omit to list all keys') and connecting the parameter to the tool's dual functionality, going beyond what the schema alone provides.

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

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

The description clearly states the tool's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes this from sibling tools like 'remember' (which stores) and 'forget' (which removes), making the retrieval/list function explicit.

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 a clear alternative usage pattern ('omit key to list all keys'). It also implicitly distinguishes it from siblings by focusing on retrieval rather than storage or 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, the description discloses parallel fan-out behavior, accepted date formats, and return structure (structured changes, total_changes count, pipeworx:// URIs). It does not cover rate limits or authorization, but is sufficiently transparent for a read-like operation.

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 dense but well-organized: summary, behavior, parameter formats, return values, and usage notes. It is front-loaded and each sentence adds information, though it could be slightly more concise.

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 and lack of output schema, the description thoroughly covers all inputs, behavior, output details, and workflow suggestions. It is complete for an agent to understand and invoke the tool correctly.

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

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

Schema coverage is 100%, but the description adds value by explaining the 'since' parameter's relative formats and providing monitoring defaults. It also clarifies the 'type' parameter's constraints and the 'value' parameter's accepted forms.

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 retrieves 'what's new about an entity since a given point in time' and specifies the fan-out to SEC EDGAR, GDELT, and USPTO. It differentiates from siblings by outlining the specific data sources and use cases.

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 recommends usage for 'brief me on what happened with X' or change-monitoring workflows. It provides guidance on the 'since' parameter format but does not contrast with alternative sibling tools.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: it's a storage operation (implied mutation), specifies persistence differences ('Authenticated users get persistent memory; anonymous sessions last 24 hours'), and hints at session scope. However, it doesn't cover potential errors, rate limits, or exact response format, leaving minor 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 appropriately sized and front-loaded, with two sentences that efficiently convey purpose, usage, and behavioral details without waste. Each sentence adds distinct value: the first defines the tool's function, and the second clarifies persistence rules, 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 (2 parameters, no output schema, no annotations), the description is mostly complete. It covers purpose, usage context, and key behavioral aspects like persistence. However, it lacks details on return values or error handling, which would be helpful since there's no output schema, leaving a minor gap in full contextual understanding.

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

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

The schema description coverage is 100%, with both parameters ('key' and 'value') well-documented in the schema. The description adds minimal semantic value beyond the schema, only implying general use cases ('findings, addresses, preferences, notes') without specifying parameter constraints or interactions. This meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose with a specific verb ('store') and resource ('key-value pair in your session memory'), distinguishing it from sibling tools like 'forget' (which likely removes) and 'recall' (which likely retrieves). It explicitly mentions what gets stored ('intermediate findings, user preferences, or context across tool calls'), making the purpose unambiguous and distinct.

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

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

The description provides clear context 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. For example, it doesn't compare usage with 'recall' for retrieval or 'forget' for deletion, leaving some ambiguity in sibling tool 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?

Without annotations, the description carries full burden. It adequately discloses that the tool is a single call and returns ticker, CIK, company name, and canonical URIs. It does not mention any destructive behavior, authentication needs, or rate limits, but the operation appears to be a safe read, which is clear from 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 extremely concise, using a single sentence to convey purpose, supported inputs, and outputs. It front-loads the core action and avoids any filler, making it efficient for an AI agent to parse.

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

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

Given the tool has no output schema, the description compensates by listing the return values (ticker, CIK, company name, URIs). It covers the key aspects for a simple lookup tool, but lacks information about error handling or empty results, which would enhance completeness.

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

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

The input schema has 100% description coverage, and the description adds significant context: it explains the 'type' parameter supports 'company' (v1) and gives concrete examples for 'value' (ticker, CIK, name). This enriches the schema beyond the bare definitions.

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

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

The description clearly states the tool resolves entities to canonical IDs across Pipeworx data sources in a single call. It specifies the supported entity type (company) and provides concrete examples of input values (ticker, CIK, name). This distinctively differentiates it from sibling tools like ask_pipeworx or discover_tools.

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

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

The description explicitly notes that the tool replaces 2–3 lookup calls, implying it should be used for entity resolution instead of multiple separate queries. However, it does not provide explicit scenarios where other tools would be more appropriate or mention potential limitations for unsupported entity types.

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

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

With no annotations provided, the description carries the full burden. It discloses the verdict categories, extracted form, actual value with citation, and percent delta, as well as the underlying process (SEC EDGAR + XBRL). It does not mention side effects, but as a read-only query 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?

Three well-structured sentences front-load the main action, then add supported domain, output details, and value proposition. No unnecessary words. Every sentence is informative.

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

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

Given the tool's single parameter and lack of output schema, the description covers input, output, supported domains, and benefits. It does not address error handling or unsupported claims, but for a v1 tool with explicit domain scope, it is reasonably 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?

The schema already provides a good description for the 'claim' parameter. The description enhances it by adding examples and specifying the domain (company-financial claims), adding value 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 fact-checks natural-language claims against authoritative sources, specifies the supported domain (company-financial for US public companies), and distinguishes itself from siblings by noting it replaces 4-6 sequential agent calls, making it a composite tool.

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 supported claim types (company-financial) and that it replaces sequential calls, implying it's for financial fact-checking. However, it does not explicitly state when to use alternatives like 'compare_entities' or 'entity_profile', though the context is clear.

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