Nhl

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

With no annotations provided, the description fully bears the burden of behavioral transparency. It clearly states that the tool picks the right tool, fills arguments, and returns results, which sets accurate expectations about its automated decision-making. No contradictions with annotations exist.

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

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

The description is concise, with four sentences that are front-loaded with the core purpose. Every sentence adds value: purpose, behavior, rationale, and examples. 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?

Given the tool's simplicity (one required parameter, no output schema), the description is complete. It explains input, behavior, and examples. The lack of output schema is mitigated because the description states it 'returns the result' without needing further detail.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining the parameter's purpose in natural language and providing examples, which clarifies the expected input beyond the schema's description of 'Your question or request in natural language'.

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 accepts natural language questions and returns answers from the best available data source. It distinguishes itself from siblings by acting as an intelligent dispatcher that abstracts away tool selection and argument filling, unlike specific tools like get_scores or get_schedule.

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 this tool: when you want to ask a question without manually picking tools or learning schemas. It provides examples of appropriate queries. However, it does not explicitly state when not to use it or mention alternative tools, though the examples imply a broad range of use cases.

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

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

Annotations already indicate read-only and open-world hints. The description adds valuable behavioral context: it resolves the market, classifies the bet, fans out to relevant packs, and returns an evidence packet with comparison. This goes beyond the annotations by explaining the internal process and output structure.

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 informative but slightly verbose with promotional language ('core demo product'). It is front-loaded with the core action and each sentence adds relevant detail, but could be tightened slightly without losing clarity.

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 adequately explains the return value: an evidence packet and market-vs-model comparison. All key aspects (input, process, output) are covered, making the tool understandable without needing an 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?

Schema coverage is 100% for both parameters. The description enriches the meaning: 'depth' explains quick vs thorough and default; 'market' clarifies it can be a slug, URL, or question text. The description adds significant semantic value beyond the schema definitions.

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

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

The description clearly states the tool's purpose: researching Polymarket bets by pulling Pipeworx data. It specifies the action ('Research'), the resource ('Polymarket bet'), and the method (pulling relevant data, resolving, classifying, fanning out). It distinguishes itself from siblings by noting it's the core demo product and that agents using it convert better than those discovering packs manually.

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

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

The description provides explicit use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It implies when to use this tool over others by stating it converts better. However, it does not explicitly state when not to use it or list alternative tools for different scenarios.

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

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

The description discloses the returned data for each type and mentions 'paired data + pipeworx:// resource URIs.' With no annotations, it adequately conveys the tool's output and behavior. It lacks details on authentication needs, rate limits, or side effects, but the core behavior is transparent.

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

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

Two sentences: first states core function, second details type-specific outputs and efficiency benefit. No wasted words, information is front-loaded and easy to parse.

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

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

The description covers purpose, input semantics, output (paired data + URIs), and efficiency. It lacks explanation of return format or error handling, but given the tool's complexity and lack of output schema, it is fairly complete. Minor gaps like sorting or limits prevent a 5.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining what 'values' should contain for each type (tickers/CIKs for company, drug names) and gives examples. This clarifies usage beyond the schema's descriptions.

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

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

Clearly states the tool compares 2–5 entities side by side in one call, and explicitly details the fields returned for each type (company: revenue, net income, cash, long-term debt; drug: adverse-event report count, FDA approval count, active trial count). This specificity and the mention that it replaces sequential calls distinguishes it from siblings.

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

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

The description notes it 'replaces 8–15 sequential agent calls,' implying efficiency when comparing multiple entities. However, it does not explicitly state when not to use it or provide alternatives (e.g., single-entity lookup via resolve_entity). The guidance is clear 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 carries the full burden. It discloses the tool returns 'the most relevant tools with names and descriptions' and mentions default/max limit. It does not mention whether the search is fuzzy or exact, but the behavioral intent is clear. 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?

Three concise sentences, each adding value. The first states the action, the second describes the return, the third gives usage context. No wasted words.

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

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

Given no output schema and simple parameters, the description sufficiently covers the tool's purpose and usage. It could mention the return format or that tools are returned with names and descriptions, but the existing text is adequate.

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. The description adds no additional meaning beyond the schema, so baseline 3 is appropriate.

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

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

The description clearly states the tool searches a tool catalog by natural language description and returns relevant tools. It explicitly differentiates from siblings by stating 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task', distinguishing it from other tools that perform different functions.

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 tells the agent to call this tool first when many tools are available, providing clear guidance on when to use it. It implies this is a discovery step before invoking specific tools, which is a strong usage directive.

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 what data is returned, that it returns citation URIs, and that it replaces many sequential calls. No annotations provided, but description handles most burden. Missing details on failure modes or performance.

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

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

Two sentences, front-loaded with purpose, no redundant information. 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?

Adequately explains output and limitations. No output schema, but description covers return content. Could mention error handling or empty results.

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

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

Schema coverage 100%, but description adds meaning: type only 'company', value examples, and limitation on names. Enhances schema with practical usage context.

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

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

Clearly states it returns a full profile of an entity across multiple packs in one call, enumerating data sources for company type. Distinguishes from sibling tools by naming alternatives and setting 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?

Explicitly says when not to use this tool (federal contracts -> usa_recipient_profile) and caveats about name input (use resolve_entity first). This provides clear decision support.

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 must carry the full burden. It states the tool deletes a memory, which implies a destructive operation, but does not clarify whether deletion is permanent or reversible, or if any confirmation is required.

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 concise sentence that front-loads the purpose. No unnecessary words, though it could be slightly expanded to include behavioral details without losing conciseness.

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 only one parameter, no output schema, and no annotations, the description adequately explains the core function. However, it lacks detail about permanence, scope, or error conditions, which would be helpful 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?

The input schema already has 100% coverage with a clear description for the 'key' parameter. The description adds no additional meaning beyond 'by key', so baseline 3 is appropriate.

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

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

The description uses a clear verb 'Delete' and a specific resource 'stored memory by key'. It clearly states what the tool does and distinguishes it from siblings like 'remember' (store) and 'recall' (retrieve).

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 when you need to delete a memory, but does not provide explicit guidance on when to use this tool vs alternatives, nor does it mention prerequisites or side effects.

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 are empty, so the description carries full burden. It discloses the tool returns profile and season stats, which is behavioral. However, it does not mention idempotency, error handling, or data freshness. Acceptable but not outstanding.

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 concise sentence that includes the purpose and the key parameter format. No unnecessary words, front-loaded with the main action.

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 only one parameter, no output schema, and simple structure, the description is complete for basic use. It covers what the tool returns. No mention of data freshness or pagination, but not necessary for a single-player endpoint.

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 playerId including an example. The tool description adds context about what the ID is for (NHL player) and the example value, which adds 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 the verb 'Get' and resource 'detailed profile and current season stats for an NHL player', distinguishing it from siblings like get_schedule and get_scores which cover different resources. The playerId parameter is exemplified.

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 when to use it (when needing player profile and stats) but provides no explicit guidance on when not to use it or alternatives among siblings. No exclusions are mentioned.

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

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

No annotations provided, so description bears full burden. It accurately describes the tool as returning weekly schedule with no side effects (read-only). Could mention that it uses current date context, but given no annotations, this is sufficient.

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

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

Two sentences, clear and front-loaded. Could be slightly more specific about the schedule scope (e.g., 'current NHL season' vs 'weekly'), but overall efficient.

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 (no params, no output schema), so description covers basics. However, lacks detail on return format (e.g., JSON structure) and whether it filters by team. Given no output schema, a bit more detail would be helpful.

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 no parameters, so description has no param info to add. Baseline 3 is appropriate as description doesn't need to explain parameters, but it could mention if any implicit parameters (like date context) affect results.

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 retrieves the current NHL weekly schedule, specifying the type of data (upcoming and recent games with teams, dates, venues). It distinguishes itself from siblings like get_player, get_scores, and get_standings, though it could explicitly contrast with get_scores.

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?

Description implies use when an agent needs schedule information, but no explicit guidance on when not to use or alternatives. With siblings like get_scores and get_standings, mentioning when to choose schedule over scores would improve clarity.

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 are empty, so description carries the burden. It discloses that the tool returns multiple states (live, final, scheduled) and specific stats, but does not mention any side effects, rate limits, or data freshness. Since no annotations exist, a 3 is appropriate as it adds some behavioral context but lacks depth.

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

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

Two sentences, no fluff. Every word adds value: specifies domain (NHL), temporal scope (today's), data types (scores, states, teams, shots, period). Perfectly 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 no output schema and no parameters, the description is sufficiently complete. It tells the agent what data to expect and the scope. Could add return format or limit, but for a simple getter, it's adequate.

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 no parameters (100% coverage), so description cannot add param semantics. However, the description clarifies what the tool does without parameters, effectively communicating that no input is needed. Baseline is 4 for zero parameters.

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

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

The description clearly states it retrieves today's NHL game scores and states, listing specific data returned (teams, scores, shots, period). The verb 'get' combined with 'scores' and context of NHL distinguishes it from siblings like get_schedule or get_standings.

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 it is for today's games only, but does not explicitly exclude future dates or mention when not to use it. No alternative tools are named, but the specificity (today's, NHL) provides clear context for when to use this tool.

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

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

No annotations provided, so description carries full burden. It mentions the tool returns standings data with specific fields, which is transparent. However, it doesn't state whether data is real-time or cached, or any 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?

Two concise sentences: first states purpose, second lists returned fields. No wasted words.

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

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

Given no parameters and no output schema, the description sufficiently explains the tool's purpose and output. Could mention if standings are for current season only, but not necessary.

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?

No parameters exist (0 params), so schema coverage is 100% and description need not add parameter details. The description compensates by explaining what data is returned.

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 gets current NHL standings for all teams, and specifies the data returned (wins, losses, etc.). It distinguishes itself from siblings like get_scores or get_schedule by focusing on standings data.

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

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

No explicit guidance on when to use this vs alternatives, but the purpose is clear. Sibling tools have different names (e.g., get_scores for scores, get_schedule for schedule), so usage context is implied.

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

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

No annotations provided, so description carries full burden. It discloses rate limiting and content policy. Does not mention storage or response behavior, but for a feedback 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 concise sentences, each serving a purpose: purpose, usage rule, constraint. No wasted words.

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

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

Given 3 parameters (one nested), no output schema, and feedback use case, description covers core purpose, guidelines, and rate limit. Lacks mention of confirmation or side effects, but acceptable for a simple 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?

Input schema provides detailed descriptions for all parameters (100% coverage). Description adds minor value (e.g., '1-2 sentences typical, 2000 chars max') but does not significantly extend schema explanations.

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 sends feedback to Pipeworx team and enumerates specific use cases: bug reports, feature requests, data gaps, praise. Distinguishes from sibling tools (e.g., ask_pipeworx) by focusing on feedback rather than 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?

Description provides explicit guidance on what to include (Pipeworx tools/data context) and what to exclude (end-user's prompt verbatim), plus rate limit (5 per day). However, it does not directly compare to alternatives among siblings.

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

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

Discloses full process: walks child markets, extracts dates/thresholds, sorts, checks ordering, reports pairs. Output format clearly described. Consistent with readOnlyHint and openWorldHint annotations.

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

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

Single dense paragraph of 5 sentences, front-loaded with purpose. Every sentence adds value. 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?

No output schema, but description defines return structure (list of {market_a, market_b, gap_pp, suggested_trade}). Explains logic, constraints, and example. Fully sufficient for an AI agent.

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

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

Schema coverage is 100%, baseline 3. Description adds context by explaining how the event parameter is used to walk child markets, enhancing meaning beyond the schema's 'slug or URL'.

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 verb 'Find', resource 'arbitrage opportunities within a Polymarket event', and mechanism 'monotonicity violations'. It distinguishes from siblings like polymarket_edges by focusing on price ordering violations.

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 context: applicable when event has multiple 'by [date]' or 'by [threshold]' markets. Includes example of violation. Lacks explicit when-not-to-use or alternative tools, but sufficient for guidance.

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 explains the algorithm: scans top markets, groups by asset, fetches price history once, computes model probability, ranks by edge. Mentions data sources (FRED, coinpaprika) and model (lognormal). Annotations indicate read-only and open-world, which description aligns with, adding value 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?

Description is a single paragraph of five sentences, front-loading purpose. Efficient but could be slightly more 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 complexity (multi-step algorithm), annotations (readOnly, openWorld), and no output schema, description covers what the tool does, how it works, and what it returns. Missing details on edge calculation formula but acceptable for use case.

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 default values already provided. Description does not add meaning beyond schema for parameters, fulfilling baseline expectations.

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 specifies verb 'scan' and resource 'highest-volume Polymarket markets', clearly stating it returns markets where Pipeworx data disagrees with market price, ranked by edge magnitude. Distinguishes from sibling tools like polymarket_arbitrage by focusing on single-market arbitrage 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 usage context: 'Built for the "what should I bet on today" question — agents/users discover opportunities without paging through hundreds of markets.' Provides clear context but lacks explicit when-not-to-use or alternatives.

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

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

No annotations provided, so description carries full burden. It indicates read operation (retrieve/list) and session persistence, but does not disclose limits on memory size, number of keys, or data retention policy.

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

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

Two sentences, front-loaded with action, zero wasted words. Efficient and complete.

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 simple parameter and no output schema, description adequately explains input behavior. Could mention return format (string? object?) but otherwise 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 clear description of 'key' parameter. Description adds that omitting key lists all, which enriches the semantic beyond schema.

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

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

Description uses specific verb 'retrieve' and resource 'memory by key' or 'list all stored memories', clearly distinguishing from sibling 'remember' (store) and 'forget' (delete).

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 to omit key to list all memories, implying use for retrieving context saved earlier. No direct alternative mentioned, but context clarifies when to use versus remember/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?

With no annotations, the description carries full burden. It discloses fan-out to multiple sources for 'company' type, explains 'since' formats, and describes return structure. Missing details on rate limits or permissions, but sufficient given typical usage.

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 redundancy. Front-loaded with core purpose, followed by mechanics and examples. Every sentence contributes 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 no output schema and 3 parameters, the description covers input, behavior, and output adequately. Does not address error handling or limits, but overall provides sufficient context for tool usage.

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 description adds significant context: explains 'type' enum limitation, gives 'since' format examples with recommended defaults, and clarifies 'value' can be ticker or CIK. Enhances understanding of how parameters interact.

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

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

Clearly states the purpose as finding what is new about an entity since a point in time, with specific examples for company type. Distinguishes itself by outlining fan-out behavior and giving usage scenarios like 'brief me on what happened with X'.

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 mentions use cases such as change-monitoring workflows, providing concrete guidance on when to use. Lacks explicit exclusion criteria or direct comparison with sibling tools, but 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.

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 persistence behavior (persistent vs. 24-hour expiry) which is crucial for an agent. It could mention if there are size limits or overwrite behavior, but the given context is good.

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

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

The description is three sentences, each adding value: purpose, use cases, and behavioral note. No redundancy. Front-loaded with core action.

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 simplicity of the tool (2 params, no output schema), the description covers the key aspects: what it does, when to use it, and persistence behavior. It could mention if there are overwrite semantics or limit on number of keys, but overall it is complete for a straightforward tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds context about what values can be stored ('findings, addresses, preferences, notes') but doesn't add meaning beyond the schema's parameter descriptions. It could provide more on format constraints or best practices.

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

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

The description uses a specific verb ('Store') and clearly identifies the resource ('key-value pair in your session memory'). It distinguishes itself from siblings like 'recall' and 'forget' by focusing on storage, which is evident from the description.

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

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

The description provides explicit use cases ('save intermediate findings, user preferences, or context across tool calls') and differentiates between authenticated and anonymous sessions. However, it does not mention when not to use this tool or compare it to alternatives.

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

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

No annotations are provided, so the description carries the full burden. It explains the accepted input variations and return fields. It does not explicitly state the operation is read-only or describe error handling, but the language ('resolve', 'returns') implies a safe lookup. More explicit disclosure of behavior would raise the score.

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 three sentences with no wasted words. It front-loads the core purpose, then provides specific details and comparison to alternatives. Every sentence adds necessary information.

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

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

Given the tool's simplicity (two parameters, no output schema needed), the description fully covers inputs, outputs, and usage context. It even mentions the benefit of replacing multiple calls. No additional information is required for an agent to use it 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 description coverage is 100%, so baseline is 3. The description adds value by providing concrete examples (AAPL, 0000320193, Apple) and clarifying the 'type' parameter is currently limited to 'company'. This helps the agent form correct inputs 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 resolves entities to canonical IDs in a single call, specifying the supported type (company) and input formats (ticker, CIK, name). It also lists the outputs (ticker, CIK, company name, resource URIs). This makes the purpose distinct from sibling tools which focus on sports, memory, or other domains.

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

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

The description indicates when to use the tool (instead of 2–3 lookup calls) and implies it for entity resolution. It does not explicitly state when not to use it or discuss alternatives, but sibling tools are unrelated, so confusion is unlikely. A brief mention of unsupported entity types would improve clarity.

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 must disclose behavior. It does so by describing the return values (verdict, structured form, actual value with citation, percent delta) and the internal process (NL parsing → entity resolution → data lookup → comparison). It does not cover failure modes or unsupported claims, but overall provides good transparency.

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

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

The description is three sentences, no wasted words. It starts with the primary purpose, then details outputs, then explains why it's useful (replacing multiple calls). Information is front-loaded and 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 single parameter and no output schema, the description is fairly complete. It covers input, output, supported domain, and version limitations. Missing details like error handling or language constraints are minor omissions, but overall adequate.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning by providing examples of valid claims (e.g., 'Apple's FY2024 revenue was $400 billion') and specifying the required format (natural-language factual claim). This helps the agent understand the parameter's role beyond the schema 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: fact-checking natural-language claims against authoritative sources, specifically company-financial claims. It lists the types of claims supported and the verdict categories returned, making it distinct from sibling tools like compare_entities or resolve_entity.

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

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

The description specifies the supported domain (company-financial claims for public US companies via SEC EDGAR + XBRL) and mentions it replaces multiple sequential agent calls, implying efficiency. It does not explicitly state when not to use or list alternatives, but the scope is well-defined.

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