Pirate Weather

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

The description explains that the tool routes the question to 2,520 tools, fills arguments automatically, and returns structured answers with stable citation URIs. Annotations already indicate readOnly and openWorld, but the description adds valuable behavioral context beyond annotations, such as the routing mechanism and citation format, without contradiction.

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: it opens with a strong directive, lists supported domains, explains the mechanism, gives usage guidance, and provides examples. Every sentence adds value, and the length is justified by the richness of 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 the simple input schema (one required string), the description explains the output format (structured answer with pipeworx:// citations) and the tool's extensive capabilities. No output schema is present, but the description covers what the agent needs to know about the result. Additional context like number of sources and example queries makes it 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 only parameter 'question' is fully covered by the schema (100% coverage), and the description provides extensive examples of appropriate questions, adding significant semantic meaning beyond the schema's simple 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: answering factual questions about current or historical data from many authoritative sources, with examples and context that distinguish it from web search. It also lists numerous domains, making the purpose unmistakable.

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 says 'PREFER OVER WEB SEARCH' and provides detailed when-to-use guidance, including specific query examples and a list of question types. It even says to use it even if web search could answer, leaving no 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?

Annotations (readOnlyHint, openWorldHint, destructiveHint=false) are consistent with the description. The description adds significant behavioral context: how the tool resolves the market, classifies bets, and fans out to multiple data packs. No contradictions.

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

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

The description is a single paragraph of about 100 words, effectively front-loading the core purpose. It is relatively concise but could be slightly more terse or structured (e.g., bullet points) for quicker parsing. Every sentence adds value.

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

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

Given the tool's complexity (multiple input formats, data packs, classification, comparison), the description covers the main aspects comprehensively. It explains the fan-out process and the output type. It could be improved by explicitly stating the return format (e.g., 'returns a JSON object with evidence packets and comparison scores'), but the sibling tools and context signals compensate.

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

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

Schema description coverage is 100% (both 'market' and 'depth' have descriptions). The description adds meaning beyond schema by specifying acceptable formats for 'market' (slug, URL, question text) and explaining 'quick' vs 'thorough' for 'depth'. This helps agents use parameters correctly.

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

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

The description clearly states the tool's purpose: researching Polymarket bets by pulling Pipeworx data. It specifies inputs (slug, URL, question), actions (resolves market, classifies, fans out to packs), and outputs (evidence packet, market-vs-model comparison). The purpose is well-defined and distinct from sibling tools.

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

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

The description provides explicit usage examples ('should I bet on X?', 'what does the data say?', 'is there edge?') and positions it as the core demo product. However, it does not mention when not to use it or compare directly to siblings like 'forecast' or 'validate_claim', though context signals suggest these alternatives exist.

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, openWorldHint, and destructiveHint. The description adds specific data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs) and return format (paired data + citation URIs), providing useful context beyond annotations.

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

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

The description is concise yet comprehensive, front-loaded with the core purpose, followed by usage guidance, type-specific details, and a benefit statement. Every sentence adds value without redundancy.

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

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

Given the tool's complexity (two entity types, multiple data sources), the description covers purpose, usage, data sources, and return format. While it omits error handling or invalid input behavior, it provides sufficient context for an agent to understand and use 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 significantly enhances parameter meaning by explaining type enum (company vs drug), providing format examples (tickers/CIKs for companies, drug names), and detailing what data is returned for each type.

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: comparing 2–5 companies or drugs side by side. It uses specific verbs and resources (compare entities) and distinguishes from siblings like entity_profile by emphasizing multi-entity comparison and efficiency.

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

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

The description explicitly lists when to use the tool, including example user phrases ('compare X and Y', 'X vs Y', etc.) and contexts like tables/rankings. It also differentiates between company and drug types with clear data sources.

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 mark readOnlyHint=true and destructiveHint=false. Description adds that it returns 'top-N most relevant tools with names + descriptions', which clarifies behavior. A 5 would require more detail about how ranking works or performance, but 4 is appropriate given 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 moderately concise with a clear front-loaded purpose statement. The list of example data types adds length but aids agent understanding. Minor verbosity, but each sentence earns its place.

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

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

Given no output schema, the description explains the return format ('names + descriptions') and specifies a use-case strategy ('call this FIRST'). Together with annotations, it provides complete context for an agent to decide when and how to use this tool.

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

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

Schema coverage is 100% with descriptions for both parameters. Description further clarifies 'query' as natural language description and notes 'limit' default and max. Could be improved by explicitly stating that query is used for semantic search, but still adds value.

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

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

The description clearly states the tool finds tools by describing data or task, using verbs like 'find', 'browse', 'search', 'look up', 'discover'. It distinguishes from sibling tools by positioning itself as a first-call for exploring the available option set.

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

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

Explicitly says 'use when you need to browse, search, look up, or discover what tools exist', lists example domains, and instructs to 'call this FIRST when you have many tools available and want to see the option set (not just one answer)'. Also implies alternative (other tools) for specific answers.

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 declare readOnlyHint=true and destructiveHint=false. Description adds valuable context: returns pipeworx:// citation URIs, specific data categories (SEC filings, fundamentals, patents, news, LEI). No contradictions.

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 reasonably concise for the detail it provides. It is well-structured: first sentence states purpose, followed by usage guidance, return values, and parameter clarification. Could be slightly more concise, but no wasted 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?

Given no output schema, the description comprehensively covers return values (SEC filings, fundamentals, patents, news, LEI) and constraints (type only company, value format). No gaps for a profile 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 2 parameters. Description adds significant meaning beyond schema: explains that type only supports 'company', value can be ticker or zero-padded CIK, and explicitly says names are not supported.

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: 'Get everything about a company in one call.' It lists specific use cases and distinguishes from siblings by mentioning it replaces 10+ pack 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?

Provides explicit when-to-use examples ('tell me about X', 'research Microsoft') and when-not-to ('Names not supported — use resolve_entity first'). Clearly differentiates from alternatives like resolve_entity.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description adds no new behavioral context. It does not disclose any additional traits such as output format or data sources, beyond what annotations provide.

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 at 4 words, but this comes at the cost of informativeness. While structure is fine, the content is insufficient. It is not front-loaded with key details.

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

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

Given the tool has 7 parameters, no output schema, and a sibling (forecast_grid), the description is severely lacking. It does not explain what 'full forecast' entails, how parameters interact, or how it differs from forecast_grid.

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 low (43%), but the description does not explain any parameters. For example, it does not clarify that lat/lon are geographic coordinates. The description adds no value beyond the schema itself.

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

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

The description 'Full forecast at point' is vague and does not specify the type of forecast (e.g., weather). It also fails to distinguish from sibling tool forecast_grid, which likely provides similar functionality. A more specific verb or resource would improve clarity.

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

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

No guidance is provided on when to use this tool versus alternatives like forecast_grid. There is no mention of prerequisites, limitations, or context for optimal use.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds that it provides forecasts from specific models (GFS/ECMWF), which is useful beyond annotations but does not disclose other behaviors like data format or rate limits.

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 (one sentence), but it lacks necessary detail. It is front-loaded with the key terms, but brevity compromises informativeness.

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?

With 4 parameters, no output schema, and no parameter descriptions, the description is severely incomplete. It fails to explain what the tool returns, how to format inputs, or any expected behavior beyond the models.

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 0%, and the description provides no parameter details (e.g., lat/lon format, time format, units options). Agents cannot determine correct values without external knowledge.

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 'GFS/ECMWF grid forecast' specifies data sources (GFS, ECMWF) and type (grid forecast), but lacks an explicit verb (e.g., 'get', 'retrieve') and does not differentiate from the sibling tool 'forecast', which may offer point forecasts.

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

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

No guidance on when to use this tool vs alternatives. The sibling 'forecast' likely provides a different forecast type, but the description does not clarify scenarios (e.g., grid vs point, model preference).

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

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

Annotations already provide destructiveHint=true. The description adds context about clearing sensitive data but does not go significantly beyond what annotations infer.

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

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

Three sentences, front-loaded with action, then usage, then pairing. Every sentence adds value with no 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 simple tool with one parameter and no output schema, the description fully covers purpose, usage, and pairing, making it 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 a clear description for the 'key' parameter. The description adds 'by key' and pairing context but does not add new semantics 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 'Delete a previously stored memory by key', which is a specific verb and resource. It distinguishes from siblings by mentioning pairing with 'remember' and 'recall'.

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: 'when context is stale, the task is done, or you want to clear sensitive data'. This provides clear context, though it does not explicitly state when not to use.

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 non-destructive and non-read-only, and the description adds context: free, rate-limited (5 per identifier per day), not counted against tool-call quota, and team reads digests daily. 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 with every sentence earning its place. It opens with purpose, then usage conditions, typing guidance, constraints, and team info. It is appropriately sized without any fluff.

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

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

Given no output schema, the description explains what happens after submission (team reads, affects roadmap). It also covers rate limits and quota impact, making it fully complete 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%, and the description adds value beyond schema descriptions. For 'message', it advises specificity and length limits. For 'type', it reiterates enum meanings. It also provides guidance on describing issues in terms of Pipeworx tools/packs.

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: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It specifies the verb and resource, and distinguishes from siblings by listing the types of feedback (bug, feature, data_gap, praise).

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

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

The description provides explicit when-to-use scenarios (e.g., bug for wrong/stale data, feature for missing tool) and what not to do (don't paste end-user's prompt). It also explains team process and rate limits, giving full context for appropriate use.

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, so safety profile is clear. The description adds key behavioral details: it checks monotonicity violations across markets, returns ranked opportunities with suggested trade direction and reasoning. It does not mention output format or pagination, but given no output schema, the description provides substantial behavioral context. 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, using bold mode labels and parenthetical examples. It is moderately concise at about 120 words; each sentence provides unique value. Could be slightly shorter, but the structure aids readability and the key insight about Polymarket's event splitting is valuable.

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 (two modes, monotonicity logic), the description is complete. It explains the underlying problem (Polymarket listing cutoffs as separate events), the two operational modes, and the return format (ranked opportunities with reasoning). Annotations cover safety. No output schema exists, but the description implies enough for an agent to use the tool effectively.

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 both parameters having descriptions. The description adds beyond the schema by explaining that event can be a slug or URL, and topic is a seed question for cross-event search. It also ties parameters to the two modes, enriching semantics. Since baseline is 3 with full schema coverage, the added context justifies 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 finds arbitrage opportunities by checking monotonicity violations on Polymarket. It distinguishes two modes (event and topic) with examples, and explains why cross-event mode is needed, differentiating it from a single-event approach. The verb 'find' and resource 'arbitrage opportunities' are specific.

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 defines two modes and when to use each: 'event' for a single event slug/URL, 'topic' for cross-event searches. It provides concrete examples and explains the limitation of single-event mode, giving clear guidance on choosing between modes. This is comprehensive usage advice.

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

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

Annotations (readOnlyHint, openWorldHint, destructiveHint) indicate a safe, read-only operation. The description adds detailed behavioral context: V1 covers crypto-price bets using a lognormal model from FRED and live coinpaprika price, scans top markets, groups by asset, fetches price history once, and ranks by edge. No contradictions with annotations.

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

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

The description is concise yet comprehensive, front-loading the core purpose and then detailing the methodology. Every sentence adds value without repetition 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?

No output schema is provided, but the description specifies that the tool returns 'top N ranked by edge magnitude with suggested trade direction.' Given the complexity of the tool and its read-only nature, this is sufficient for an agent to understand what to expect.

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

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

Schema has 100% coverage with descriptions for all three parameters. The description adds value by explaining defaults (10, 1wk, 0.5), max limit (25), and the meaning of min_edge_pp as a filter, which supplements 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 action: scanning high-volume Polymarket markets to find where Pipeworx data disagrees with market price, computing edge, and returning top-ranked opportunities. It distinguishes itself from sibling tools like polymarket_arbitrage and forecast by focusing on edge-based discovery for daily betting.

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 frames the tool as answering 'what should I bet on today' for agents/users, indicating when to use it. It explains the process steps (scan, group, fetch, compute, rank) but doesn't directly contrast with alternatives or state when not to use it, though the context is clear enough.

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

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

Annotations already mark readOnlyHint=true, and the description adds context about scoping and pairing with remember/forget, enhancing transparency beyond annotations.

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

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

The description is concise with two sentences, front-loading the main purpose and efficiently covering usage, scope, and related tools.

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 tool with one optional parameter and no output schema, the description is complete, explaining scope and pairing with related tools.

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 covers 100% of parameters, and the description adds that omitting the key argument lists all keys, providing additional meaning 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 that the tool retrieves a value saved via remember or lists all keys, distinguishing it from siblings remember and 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?

It explicitly explains when to use the tool (to look up context stored earlier) and mentions the scope (anonymous IP, BYO key etc.), providing clear guidance on 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?

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: it fans out to three external sources (SEC EDGAR, GDELT, USPTO) in parallel and returns structured changes with citation URIs. This goes beyond the annotations.

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

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

The description is concise (5 sentences) with no wasted words. It front-loads the primary purpose, then adds usage examples, fan-out details, parameter notes, and return structure. 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?

While there is no output schema, the description outlines the return shape (structured changes, total_changes count, URIs). It covers the tool's key behavior (parallel fan-out). Some minor details like pagination or rate limits are absent, but overall it is complete for the task.

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

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

Schema coverage is 100%, but description adds meaning: it explains that 'since' accepts ISO dates or relative shorthand (with examples), 'value' can be ticker or CIK, and 'type' is currently limited to 'company'. This helps the agent form correct parameter values.

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

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

The description clearly states the tool's verb-resource pair: 'find recent changes' for a company. It provides concrete example queries and indicates the scope (last N days/months). It is distinct from siblings like entity_profile or compare_entities because it focuses on temporal changes.

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

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

The description explicitly lists user intents ('what's happening with X?', 'any updates on Y?') and provides example timeframes. It does not explicitly state when not to use, but the context is clear enough for the agent to differentiate.

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?

Discloses key behavioral traits: key-value pair scoped by identifier, persistence differences (authenticated vs anonymous, 24-hour limit). Annotations are basic (readOnlyHint false, destructiveHint false) and do not contradict.

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?

Front-loaded with purpose, followed by usage cues and storage details. Every sentence is informative and 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?

Tool is simple (store key-value), description covers purpose, usage, and persistence. No output schema, but return behavior is implied. Lacks explicit mention of success/failure response.

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

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

Schema covers both parameters with examples (100% coverage). Description adds usage context ('any text') but does not significantly extend beyond schema; therefore slightly above baseline 3.

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

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

The description clearly states the tool's action ('Save data') and resource ('data the agent will need to reuse later'), and distinguishes it from siblings 'recall' and '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?

Provides explicit when-to-use guidance ('when you discover something worth carrying forward') with concrete examples, and directs pairing with recall/forget.

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 (readOnly, openWorld, not destructive) are already clear. Description adds value by explaining it returns IDs plus 'pipeworx:// citation URIs' and mentions efficiency ('Replaces 2–3 lookup calls'). No contradictions.

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

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

Four sentences, each adding value. Front-loaded with main action, followed by examples and workflow guidance. No redundant or unclear phrasing.

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

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

Description covers purpose, usage, parameters, and output (IDs + URIs). Lacks explicit output schema or structure, but given the simple nature of the tool, it's nearly complete. Could mention that output is a JSON object.

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

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

Input schema covers both parameters (type, value) with descriptions. The description enhances meaning by giving examples of valid values (e.g., 'Apple' → AAPL) and explaining the identifier systems, which goes beyond the schema enum.

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

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

Description clearly states the tool's purpose: 'Look up the canonical/official identifier for a company or drug.' It lists specific identifier types (CIK, ticker, RxCUI, LEI) and gives concrete examples, effectively distinguishing it from siblings like entity_profile.

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

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

Explicitly instructs when to use: 'Use this BEFORE calling other tools that need official identifiers.' Provides examples of inputs and outputs, and notes it replaces multiple lookup calls. No ambiguity about its role in a workflow.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, and description adds valuable context: returns verdict types, extracted structured form, actual value with citation, and percent delta. 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?

Description is front-loaded with key phrase and each sentence adds value (purpose, usage, scope, output notes). Slightly long but no redundancy; could be marginally tighter.

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, description adequately covers return values (verdict, structured form, actual value, citation, delta) and the benefit of replacing multiple calls. Could mention constraints like 'US public companies only' more explicitly, but overall complete 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% with description for the only parameter 'claim'. The tool description adds meaning beyond schema by explaining acceptable claim formats (e.g., 'Apple's FY2024 revenue was $400 billion') and domain specificity, enhancing agent 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?

Description uses specific verb 'fact-check, verify, validate' with resource 'factual claim' and distinguishes from siblings by specializing in company-financial claims. It also notes it replaces 4-6 sequential calls, differentiating it as 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?

Explicitly states when to use: 'when an agent needs to check whether something a user said is true' with example prompts. Defines scope to company-financial claims (v1), but does not explicitly state when not to use or list alternative tools for other claim types.

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