Usda Fdc

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

No annotations provided, so description carries full burden. It states it picks the right tool and fills arguments, but lacks details on failure modes, rate limits, or authentication.

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 purpose, includes examples, but could be slightly shorter. Still informative without excessive verbosity.

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 (300+ sources, routing), description covers main use and examples. Lacks details on error handling or output format, but no output schema expected.

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

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

Schema coverage is 100% for the single parameter 'question'. Description adds value beyond basic schema by explaining expected format and providing concrete examples.

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 answers natural-language questions by routing across 300+ sources. It explicitly contrasts with sibling tools like compare_entities or discover_tools, which 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?

It gives explicit when-to-use scenarios ('when a user asks What is X?') and examples. It doesn't cover when not to use, but context implies it's a general fallback.

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=true, openWorldHint=true, destructiveHint=false) are consistent. The description adds detailed behavioral context: how it resolves the market, classifies the bet, fans out to packs, and returns evidence and comparison. No 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 a single paragraph that is information-dense yet clear. It front-loads the purpose and then details behavior. Every sentence adds value, though slightly more concise wording could improve readability.

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

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

Despite lacking an output schema, the description fully explains what the tool returns (evidence packet plus market-vs-model comparison). Covers input formats, internal fan-out logic, and output structure, making it complete for a complex 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 each parameter described. The description adds meaning beyond the schema: explains the 'depth' enum values ('quick = 2-3 evidence sources, thorough = full fan-out') and clarifies that 'market' can be a slug, URL, or question text.

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

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

The description explicitly states the verb ('Research'), the resource ('Polymarket bet'), and the method ('pulling relevant Pipeworx data'). It distinguishes itself by labeling it as the 'core demo product' and noting 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?

Explicitly states use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. Clearly communicates when to use, though does not explicitly list alternatives or 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?

With no annotations, the description fully bears the transparency burden. It discloses data sources (SEC EDGAR/XBRL, FAERS) and specific fields pulled, and mentions efficiency gains. However, it omits potential limitations like geographic scope or data recency.

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, front-loads the purpose, and every sentence adds essential information. 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, the description provides a brief but incomplete picture of the return format ('paired data + citation URIs'). More detail on the output structure would improve completeness.

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

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

Schema coverage is 100%, and description adds significant meaning: explains the meaning of 'values' for each type, provides examples, and clarifies the 2-5 item constraint.

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

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

The description clearly states the tool compares 2-5 companies or drugs side by side, with specific use cases and examples. It distinguishes itself from sibling tools like entity_profile by offering multi-entity comparison.

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 trigger phrases like 'compare X and Y', 'X vs Y', and explains when to use each type. Gives clear context for both company and drug comparisons.

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 explains the tool returns 'top-N most relevant tools with names + descriptions' and is a meta-tool for exploration. Does not mention safety (read-only) but sufficient for the task.

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 efficient sentences, no fluff. Purpose, examples, return format, and usage advice are front-loaded and 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, description adequately explains return format. All essential aspects (purpose, usage, outcome) are covered for a simple discovery 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%, baseline 3. Description adds example query values but no additional parameter details beyond schema. Meets expectations without extra depth.

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

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

Description explicitly states 'Find tools by describing the data or task' with concrete examples (SEC filings, FDA drugs, etc.), clearly differentiating from sibling tools by advising to 'Call this FIRST'.

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 clear usage context: 'Use when you need to browse, search, look up, or discover what tools exist' and instructs to call first. Lacks explicit when-not or alternative tool names but context is strong.

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 cover behavior. It clearly indicates this is a read operation that returns aggregated data with citation URIs. It also discloses the limitation that only 'company' type is supported. However, it does not mention rate limits or auth requirements.

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

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

The description is fairly concise and well-structured, starting with purpose, then usage guidelines, then output details. Every sentence adds value, though some phrasing like '10+ pack tools' is slightly verbose but not distracting.

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 (returns multiple data types) and no output schema, the description gives a solid overview of what is returned. It could be more detailed about the structure of the response, but it covers the key elements sufficiently for an agent to decide whether to use the tool.

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

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

Schema coverage is 100%, but the description adds value by explaining the enum constraint for type, giving examples for value (ticker or CIK), and explicitly stating that names are not supported and to use resolve_entity instead. This goes beyond the schema descriptions.

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

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

The description clearly states 'Get everything about a company in one call', specifying the action and resource. It lists the included data types (SEC filings, fundamentals, patents, news, LEI) and distinguishes from siblings like resolve_entity by explaining when to use this tool instead.

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 and when-not-to-use guidance. It advises using resolve_entity first if only a name is available, and suggests this tool is for broad company overviews, thus differentiating from other tools on the server.

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. States deletion but lacks details like permanence or confirmation. Adequate but minimal.

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 action, second gives usage guidance. No extraneous information, highly 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?

Given the tool's simplicity (1 required param, no output schema), the description is complete enough. Could mention return value or absence thereof, 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?

Schema coverage is 100% with a clear description of the 'key' parameter. Description does not add further semantics beyond what schema 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?

Description uses specific verb 'Delete' and resource 'memory by key', clearly stating the action. It distinguishes from siblings by referencing '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?

Provides explicit when-to-use scenarios: stale context, task done, clear sensitive data. Does not include when-not-to-use or alternatives, but context implies it.

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 behavioral traits. The phrase 'Full food record' suggests a read-only operation, but no explicit statement about side effects, permissions, or rate limits is given. The minimal description fails to compensate for absent 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 a single sentence with no unnecessary words. However, it is arguably too brief, lacking details that could improve clarity without becoming verbose.

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 three parameters, no output schema, and no annotations, the description is incomplete. It does not explain the purpose of the 'format' and 'nutrients' parameters nor what the returned full food record contains.

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 provides descriptions for all three parameters (100% coverage), so the description adds no additional meaning. It simply restates retrieval by FDC id, offering no extra context beyond the schema.

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

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

The description 'Full food record by FDC id' clearly indicates retrieval of a single food record using its FDC id, distinguishing it from sibling tools like search_foods or list_foods which operate on multiple items. However, it does not explicitly state it's for a specific ID-based lookup, which would be ideal.

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 such as search_foods or nutrients_for_food. The description lacks any context about prerequisites or typical 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?

With no annotations, the description bears full responsibility for behavioral disclosure, but it only states the tool is a 'reference'. It does not explain what is returned (e.g., list of objects, IDs, names) or any side effects, making it insufficiently 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?

The description is a single, efficient sentence with no wasted words. It is appropriately concise for a parameterless tool, though it could be more informative 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 no parameters, no output schema, and no annotations, the description should provide a clearer picture of the return format or structure. Calling it a 'reference' is vague; an agent would benefit from knowing it returns a list of categories with identifiers.

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, so schema coverage is 100%. The description adds no parameter information, which is acceptable given no parameters exist. The baseline score of 3 applies.

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 'USDA food group reference (categories)' clearly states the tool returns a reference list of food categories. It uses a specific resource (food groups) and implies the verb 'list', distinguishing it from siblings like 'list_foods' which return individual foods.

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

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

The description provides no guidance on when to use this tool versus alternatives (e.g., 'search_foods' or 'list_foods'). It does not mention context or exclusions, leaving the agent to infer usage from the name alone.

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 compensate. It only states 'paginated browse', implying read-only, but does not disclose rate limits, data freshness, or default behavior. The description adds minimal behavioral insight beyond the schema.

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?

Extremely short (2 words), but this is under-specification rather than conciseness. The description lacks essential details, making it insufficient for an agent.

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 a simple browse tool with no output schema, the description should clarify what 'FDC foods' are, the response format, and scope. It fails to provide sufficient context for correct 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?

Only 60% of parameters have schema descriptions, but the description adds no elaboration on parameters like sort_by or sort_order. It does not compensate for the incomplete schema coverage.

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

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

The description 'Paginated browse of FDC foods' clearly identifies the verb (browse) and resource (FDC foods), distinguishing it from siblings like get_food (single item) and search_foods (keyword search). However, it could be more precise, e.g., 'list all foods'.

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 versus alternatives like search_foods or list_food_groups. The description does not mention context or exclusions.

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

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

No annotations are provided, so the description must disclose behavioral traits. It only says 'nutrient values only for a food'—no mention of read-only nature, required permissions, return format, or any side effects. This is insufficient.

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 short but lacks substantive information. Conciseness is undercut by vagueness; it does not efficiently communicate key details. A single sentence with low information density.

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 minimal parameters, the description should provide more context about return values, usage hints, or data sources. It only gives a vague statement, leaving the agent with significant uncertainty.

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 50% (fdc_id undocumented, nutrient_numbers described). The description does not add meaning beyond the schema; it does not explain what fdc_id represents or provide context for nutrient_numbers beyond what the schema already says.

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 states 'nutrient values only for a food,' implying it retrieves nutrient info for a given food. However, it lacks a clear action verb and does not explicitly distinguish from sibling tools like get_food, which likely returns full food details.

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 versus alternatives such as get_food or search_foods. The description implies it's for nutrient-only needs but does not explicitly state usage context or exclusions.

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

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

With no annotations, the description fully discloses behavioral traits: it is free, does not consume tool-call quota, rate-limited to 5 per identifier per day, and the feedback is read daily by the team. 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 a single paragraph that efficiently covers purpose, usage, and constraints. It is front-loaded with the verb 'Tell' and structured logically, though it could be slightly more organized with bullet points.

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 lack of output schema, the description does not explain return values, but it sufficiently covers input parameters, usage context, rate limits, and audience. It is complete enough 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%, but the description adds meaningful context beyond the schema: it explains the enum values ('bug', 'feature', etc.) and provides formatting guidance for the message field (be specific, 1-2 sentences, 2000 chars).

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: to send feedback (bug, feature, data_gap, praise) to the Pipeworx team. It distinguishes itself from sibling tools (e.g., ask_pipeworx, discover_tools) by focusing on feedback rather than queries or data retrieval.

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

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

The description provides explicit guidance on when to use each feedback type (bug, feature, etc.) and what not to include (end-user prompt). It also mentions rate limits and quota impact, helping the agent decide when to invoke 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?

Annotations already declare readOnlyHint=true, so the description's addition of 'walks child markets, extracts dates/thresholds, sorts them, and reports violations' provides valuable behavioral context beyond the annotations. 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 efficiently front-loaded with the purpose and provides detailed logic in subsequent sentences. Every sentence adds value, though it could be slightly more concise.

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

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

Given the tool's simplicity (one parameter, no output schema) and the annotations covering read-only and open-world hints, the description adequately explains the input, logic, and output format (list of entries). It is complete for its complexity.

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

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

Schema description coverage is 100%, with the parameter 'event' already well-described. The tool description adds minimal additional semantics beyond the schema, so a baseline score of 3 is appropriate.

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

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

The description clearly states it finds arbitrage opportunities by checking monotonicity violations, with a specific verb (find) and resource (arbitrage opportunities). It distinguishes itself from sibling tools like polymarket_edges by focusing on event-level arbitrage, not edges.

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

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

The description explains when to use the tool (for events with multiple 'by date' or 'by threshold' markets) and how to pass the event slug or URL. It does not explicitly state when not to use or provide alternative 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?

Annotations (readOnlyHint=true, openWorldHint=true, destructiveHint=false) already indicate safety. The description strongly complements them by detailing the internal process: scanning top markets, grouping by asset, fetching price history once, computing model probability, and ranking by |edge|. This adds significant behavioral context 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 at 4 sentences, front-loaded with purpose, then methodology, output format, and use case. No filler; every sentence contributes meaningful 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 complexity (multi-step processing, no output schema), the description fully covers inputs, process, output (top N ranked by edge magnitude with suggested trade direction), and intended use case. It provides a complete mental model for an agent to invoke 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 covers 100% of parameters with descriptions. The description adds extra value by stating default values (limit=10, window=1wk, min_edge_pp=0.5) and the maximum for limit (25). This helps agents quickly understand expected inputs without inferring from schema alone.

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: scan high-volume Polymarket markets and return those with the largest disagreement between Pipeworx data and market price, specifically for crypto-price bets using a lognormal model. It distinctively differentiates from siblings by focusing on edge detection for a 'what should I bet on today' use case, unlike polymarket_arbitrage which likely targets arbitrage.

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

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

The description implies the tool is for discovering betting opportunities without manually browsing, but it lacks explicit guidance on when to use it versus alternatives like polymarket_arbitrage or bet_research. No when-not-to-use or context-dependent exclusions are provided.

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?

Since no annotations are provided, the description carries full burden. It describes the scoping (anonymous IP, BYO key hash, account ID) and the read nature of the operation. Lacks detail on data persistence or error behavior, but sufficient for a retrieval tool.

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

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

Three sentences, each adding distinct value: main action, use case examples, and scope/pairing. No wasted words; front-loaded with the core functionality.

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

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

Without an output schema, the description explains what the tool returns (value or list of keys). Provides context on storage scope and pairing. Could mention error handling for missing keys, but overall sufficient for a simple tool.

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

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

Schema already covers the key parameter with 100% description coverage. The description adds value by giving concrete examples of stored data (ticker, address, notes) and clarifying the dual behavior, which goes 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 two modes: retrieve a specific value by key, or list all keys by omitting the argument. It distinguishes from siblings by explicitly mentioning pairing with '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?

Provides clear context on when to use (look up stored context without re-deriving) and usage notes (omit key to list all). Missing explicit when-not guidance, but the pairing with remember/forget implies 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, but the description discloses the tool fans out to SEC EDGAR, GDELT, and USPTO in parallel, and returns structured changes with counts and URIs. This gives clear behavioral insight without 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?

Single, well-structured paragraph. Front-loaded with purpose, then details on usage, input format, and output. Every sentence adds necessary information without waste.

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

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

No output schema, but description states return format (structured changes, count, URIs). Covers all input parameters and behavior. Lacks error handling details, but sufficient for typical 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?

Input schema has 100% parameter descriptions. The description adds value by explaining 'since' accepts ISO dates or relative shorthand, and 'value' can be ticker or CIK. Examples enhance understanding 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 clearly states the tool retrieves recent changes for a company, with specific examples of use cases and question phrasings. It distinguishes from sibling tools like entity_profile and compare_entities.

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

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

Provides explicit example queries and context for when to use (e.g., 'what's happening with X?'). Does not specify when not to use or recommend alternatives, but the guidance is sufficient for typical 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?

No annotations are provided, so the description carries the full burden. It discloses storage as key-value pairs scoped by identifier, persistent memory for authenticated users, and 24-hour retention for anonymous sessions. It does not specify behavior on key overwrite, but overall adds substantial value.

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 and front-loaded with the main purpose. It could be slightly trimmed but is efficient and well-structured.

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

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

For a simple key-value store with full schema coverage and no output schema, the description covers usage guidelines, behavioral details, and pairing with siblings. It is complete and leaves no major gaps.

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

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

Input schema has 2 parameters with full coverage (100%). The description adds examples (e.g., 'subject_property', 'target_ticker') but these are similar to the schema's description of key. The schema already provides clear semantics, so the description does not significantly enhance parameter understanding.

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

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

The description states a specific verb and resource (save data for later reuse) and distinguishes itself from sibling tools like recall and forget, which are explicitly mentioned for retrieval and deletion.

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 when to use ('when you discover something worth carrying forward') and provides alternatives ('Pair with recall to retrieve later, forget to delete'), along with scoping and persistence details.

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 discloses that it returns IDs plus pipeworx:// citation URIs, and explains the types of IDs for each entity type. Adequately covers expected behavior but omits details on rate limits or permissions.

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 contributing meaningful information. Purpose is front-loaded, examples are concise, and no redundant or filler content.

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 absence of an output schema, the description covers the main aspects: purpose, inputs, outputs (IDs and URIs), and usage order. It could be slightly more explicit about the return format structure, but the examples suffice.

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 adds value by providing concrete examples (e.g., 'Ozempic' → RxCUI 1991306) and extending the value parameter to include brand/generic drug names beyond the schema's mention.

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 it looks up canonical/official identifiers (CIK, ticker, RxCUI, LEI) for companies or drugs, with specific examples like 'Apple' → AAPL. It distinguishes from siblings by noting it replaces 2–3 lookup calls.

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

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

Explicitly says 'Use when a user mentions a name and you need the [identifiers]' and 'Use this BEFORE calling other tools that need official identifiers.' Provides strong usage context but lacks explicit when-not-to-use scenarios.

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

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

No annotations are provided, and the description does not disclose any behavioral traits such as rate limits, authentication needs, or handling of no results. The burden of transparency is unmet.

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. It is efficient, though it could include more detail without becoming verbose.

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 complexity (7 parameters, no output schema) and sibling tools, the description is too brief. It lacks details on return format, pagination, and differentiation from similar 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?

Input schema has 100% description coverage for all 7 parameters. The description adds no extra meaning beyond what the schema already provides, meeting the baseline of 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 searches USDA FDC food items, with a specific verb and resource. However, it does not distinguish from sibling 'list_foods', which could overlap in functionality.

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 versus alternatives like 'list_foods' or 'get_food'. The description lacks context on appropriate scenarios or exclusions.

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

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

With no annotations, the description details the return type (verdict, actual value with citation, percent delta) and notes it consolidates several steps. It does not mention destructive actions, authorization needs, or rate limits, but for a read-only verification tool the transparency is adequate.

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

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

The description is three sentences that front-load the action, then provide usage context, scope, and return value summary. No unnecessary words, highly 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?

For a single-parameter tool with no output schema or annotations, the description covers purpose, usage, domain scope, and output format. It could mention limitations (e.g., only US companies) more explicitly, but overall it is sufficiently 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% and the schema already describes the 'claim' parameter well. The description adds examples and clarifies the type of claims (natural-language factual), but this adds only marginal value 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 is for fact-checking and verification of natural-language claims, specifically supporting company-financial claims. It includes usage examples and distinguishes it from sibling tools like ask_pipeworx or compare_entities.

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

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

The description provides explicit when-to-use guidance with example queries like 'Is it true that…?' and notes it replaces multiple sequential calls. However, it does not explicitly state when not to use it or mention alternatives.

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