nutrition

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains key behaviors: Pipeworx picks the tool, fills arguments, and returns results, which is valuable context. However, it lacks details on limitations, error handling, or performance characteristics like response time or data source reliability.

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

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

The description is well-structured and front-loaded with the core functionality, followed by practical examples. Every sentence adds value: the first explains the tool's purpose and automation, the second provides usage guidance, and the third gives concrete examples without redundancy.

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

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

Given the tool's complexity (natural language processing with automated tool selection) and lack of annotations or output schema, the description does a good job explaining the core behavior and usage. However, it could be more complete by addressing potential limitations or the types of answers returned, as there's no output schema to clarify 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?

The schema description coverage is 100%, so the schema already documents the single 'question' parameter. The description adds meaningful context by specifying it should be 'in plain English' or 'natural language', providing examples that illustrate the expected format and scope, which enhances understanding beyond the basic schema.

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

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

The description clearly states the tool's purpose with specific verbs ('Ask a question', 'get an answer') and resources ('best available data source'), distinguishing it from sibling tools like 'search_products' or 'get_product' by emphasizing natural language processing and automated tool selection. It explicitly contrasts with manual tool browsing and schema learning.

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 guidance: use this tool for natural language questions when you don't want to browse tools or learn schemas, with clear examples illustrating appropriate use cases. It implicitly suggests alternatives (like sibling tools) for more specific or structured queries, though it doesn't explicitly name them.

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?

Describes internal behavior: resolves market, classifies bet type, fans out to relevant data packs, returns evidence packet and comparison. Aligns with annotations (readOnlyHint, openWorldHint) and adds concrete detail.

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?

Concise single paragraph with clear structure: purpose, inputs, operation, outcomes. Every sentence contributes essential information without redundancy.

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

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

Given no output schema, description adequately explains return values (evidence packet, comparison). Covers input variations and tool's role as a core demo product. Annotations provide safety context, making description 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?

Adds meaning beyond schema: explains 'quick' vs 'thorough' (2-3 vs full fan-out) and that 'market' can be slug, URL, or question. Schema already describes these but description reinforces and elaborates.

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 researches Polymarket bets by pulling relevant Pipeworx data. It specifies inputs (slug, URL, question), outputs (evidence packet, market-vs-model comparison), and distinguishes from general-purpose sibling tools like ask_pipeworx.

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 use cases: 'should I bet on X?', 'what does the data say?', 'is there edge?'. Implies when not to use by focusing on Polymarket bets, but does not explicitly exclude other uses or mention 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 mentions returns (paired data, URIs) and data sources, but does not disclose error handling, rate limits, or behavior on invalid inputs.

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 main purpose, every word adds value; no wasted text.

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

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

Given no output schema, the description explains return format and domain-specific data points. Missing error scenarios but sufficient for the tool's moderate complexity.

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

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

Schema coverage is 100% but description adds meaning by listing exact financial metrics and drug-related counts, and explains the efficiency gain (replaces 8-15 calls), enriching the schema's 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?

The description clearly states the tool compares 2-5 entities side by side, specifies two distinct types (company and drug) with detailed data points for each, and distinguishes itself from sibling tools that do not perform comparisons.

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

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

It explicitly says when to use (comparing 2-5 entities) and provides context for the two types, but does not mention when not to use or alternatives among sibling tools.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns 'the most relevant tools' and has a default/max limit behavior (implied by the schema), but lacks details on ranking algorithms, error handling, or performance characteristics. It adds some context (call first for large catalogs) but doesn't fully cover behavioral traits like rate limits or authentication needs.

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

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

The description is appropriately sized and front-loaded, with two concise sentences that each serve a clear purpose: the first defines the tool's function, and the second provides usage guidance. There is no wasted text, and it efficiently communicates essential information.

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

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

Given the tool's complexity (search functionality with 2 parameters) and the lack of annotations and output schema, the description is reasonably complete. It covers purpose, usage context, and hints at behavior, but could improve by addressing output format or error cases. However, it provides enough context for an agent to use the tool effectively in the given scenario.

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

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

Schema description coverage is 100%, so the schema already documents both parameters ('query' and 'limit') thoroughly. The description adds marginal value by emphasizing the natural language aspect ('describing what you need') and the tool discovery context, but doesn't provide additional syntax or format details beyond what the schema specifies.

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

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

The description clearly states the tool's purpose with specific verbs ('search the Pipeworx tool catalog') and resources ('returns the most relevant tools with names and descriptions'). It distinguishes from sibling tools like 'get_product' and 'search_products' by specifying it searches the tool catalog rather than products, making the scope explicit.

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

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

The description provides explicit usage guidelines: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This gives clear context on when to use it (large tool catalog, initial discovery) and implies alternatives (sibling tools) are for different purposes, though it doesn't explicitly name 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 carries the burden. It discloses the returned data types (citation URIs, specific sources) and the bundling behavior. It could mention potential limitations like rate limits or cost, but overall it provides good behavioral context for a read-only tool.

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

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

The description is four sentences, each providing essential information: purpose, details for type=company, return format, efficiency, and alternative. While a bit long, it is well-structured and avoids redundancy, earning a high score.

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

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

Given the tool's complexity (multiple data sources), the description covers the main points well. It explains what is returned and how it bundles calls. Without an output schema, it could be more explicit about the response structure, but it is sufficient for an agent to understand the tool's value.

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 the parameter values in context (type only 'company', value expects ticker or CIK, and names are not supported), which goes beyond the schema descriptions. This extra guidance improves understanding.

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

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

The description clearly states the tool provides a full profile of an entity across multiple data sources (SEC, XBRL, USPTO, GDELT, GLEIF). It differentiates from siblings by noting it replaces 10-15 sequential calls and specifies when to use an alternative (usa_recipient_profile for federal contracts). The verb 'profile' implies a comprehensive 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 explicitly states when to use this tool (for a comprehensive entity profile) and when not to (for federal contracts, use usa_recipient_profile). It also implies that names are not supported and suggests using resolve_entity first, providing clear usage 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Delete' implies a destructive mutation, it doesn't specify whether deletion is permanent, reversible, requires specific permissions, or has side effects (e.g., affecting related data). It also omits details on error handling or response format, leaving significant gaps.

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

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

The description is a single, efficient sentence with zero waste—'Delete a stored memory by key.' It's front-loaded with the core action and resource, making it immediately understandable without unnecessary elaboration.

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

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

For a destructive tool with no annotations and no output schema, the description is incomplete. It fails to address critical aspects like what happens post-deletion (e.g., confirmation, error messages), whether the operation is idempotent, or how it interacts with sibling tools. Given the complexity of a deletion operation, more context is needed for safe and effective use.

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

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

The input schema has 100% description coverage, with the single parameter 'key' documented as 'Memory key to delete'. The description adds minimal value beyond this, but since there's only one parameter and the schema is fully described, a baseline of 4 is appropriate—it doesn't detract but doesn't significantly enhance understanding either.

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

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

The description clearly states the specific action ('Delete') and resource ('a stored memory by key'), distinguishing it from sibling tools like 'remember' (store) and 'recall' (retrieve). It uses precise language that directly communicates the tool's function without ambiguity.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'recall' (which likely retrieves memories) or other deletion mechanisms. It lacks context about prerequisites, such as needing an existing memory key, or exclusions for when deletion might fail.

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 states this is a read operation ('Get'), but doesn't disclose behavioral traits like authentication requirements, rate limits, error conditions, or what happens with invalid barcodes. For a tool with no annotation coverage, this is a significant gap.

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 zero wasted words. It's front-loaded with the core purpose and includes all necessary information without redundancy.

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

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

For a simple lookup tool with one parameter and no output schema, the description covers the basic purpose adequately. However, without annotations or output schema, it lacks information about return format, error handling, and other behavioral aspects that 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?

Schema description coverage is 100%, so the schema already fully documents the single 'barcode' parameter with its format requirements. The description adds no additional parameter semantics beyond what's in the schema, meeting the baseline for high schema coverage.

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

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

The description clearly states the verb 'Get' and the resource 'full nutrition details for a food product', specifying the exact scope of data retrieved. It distinguishes from the sibling 'search_products' by focusing on lookup by barcode rather than search 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?

The description explicitly states when to use this tool: 'by its barcode (EAN/UPC)', providing clear context for usage. However, it doesn't explicitly mention when not to use it or name the alternative 'search_products' tool, though the distinction 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?

With no annotations provided, the description carries the full burden. It discloses the rate limit (5 per day per identifier), content restrictions, and the action of sending feedback to the team. This provides sufficient transparency for a feedback tool.

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

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

The description is concise at four sentences, front-loaded with the core purpose, and efficiently covers all necessary guidance 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?

For a feedback tool with no output schema and no annotations, the description covers purpose, usage, rate limits, and content rules. It is almost complete; one could wish for mention of confirmation or response, but it is not critical.

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

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

Schema description coverage is 100%, so the burden on the description is lower. The description adds context (e.g., 'Do not include the end-user's prompt verbatim') and rate limit info, but these are not parameter-specific. The parameter details are adequately covered in the schema itself.

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

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

The description clearly states the tool's purpose: 'Send feedback to the Pipeworx team.' It lists specific use cases (bug reports, feature requests, missing data, praise) and distinguishes it from sibling tools which are query-focused.

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 context on when to use the tool (for feedback types) and what not to include ('do not include the end-user's prompt verbatim'). It also mentions the rate limit, giving clear usage boundaries, though it does not explicitly compare 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?

Annotations (readOnlyHint=true, openWorldHint=true) are consistent with description. Description adds behavioral detail: it searches, groups, and checks monotonicity. No contradictory info. Provides internal logic good for agent understanding, though no mention of potential failures or rate limits.

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

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

Description is a single paragraph, efficiently structured with clear mode markers. It front-loads the purpose and uses examples. Slightly long but every sentence adds value. Minor improvement: could break into bullet points for 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?

Given no output schema, description mentions return format (ranked opportunities with direction+reasoning). Covers both modes and cross-event use case well. Could specify expected input format for topic (e.g., string length limits) but sufficient for typical use.

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 already describes both parameters (event, topic) with 100% coverage. Description adds value by explaining the two modes, linking each parameter to its mode, and giving concrete examples (e.g., 'when-will-bitcoin-hit-150k').

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket by checking monotonicity violations. It specifies two modes (event and topic) and distinguishes itself from siblings like polymarket_edges, making the purpose specific and unambiguous.

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 details two usage modes with examples (event slug for single-event, topic for cross-event). It explains when to use each mode, e.g., cross-event catches cases missed by single-event. Lacks explicit 'when not to use' guidance, but 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 declare readOnlyHint=true and destructiveHint=false, and the description aligns by stating it scans and returns data, fetches price history once, and computes model probability. It adds context about the lognormal model and data sources, 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 well-structured, starting with the main action, then detailing the model and algorithm, finally the output. Each sentence adds value, no fluff.

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

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

Given the tool's complexity (3 parameters, no output schema), the description covers the algorithm, data sources, caching, output format, and use case. It is complete for the intended purpose of discovering betting opportunities.

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

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

Schema coverage is 100% with detailed parameter descriptions. The tool description does not add significant meaning beyond the schema; it mentions returning top N ranked by edge magnitude, which is already in the limit parameter description. 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 scans high-volume Polymarket markets, identifies where Pipeworx data disagrees with market price, and returns top N ranked by edge magnitude. It distinguishes from sibling tools like 'polymarket_arbitrage' by focusing on edges for crypto-price bets.

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 mentions it is built for the 'what should I bet on today' question and helps discover opportunities without manual paging. It implies when to use but does not explicitly state when not to use or name 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?

With no annotations provided, the description carries full burden. It explains the dual behavior (retrieve by key or list all) and mentions persistence across sessions, which is valuable context. However, it doesn't disclose error handling, performance characteristics, or what happens when a non-existent key is provided.

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

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

The description is perfectly concise with two sentences that each earn their place. The first sentence explains the core functionality, and the second provides usage context. No wasted words or redundant information.

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

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

For a single-parameter tool with no output schema and no annotations, the description provides good coverage of purpose, usage, and parameter semantics. However, it doesn't describe the return format (what a 'memory' contains) or error conditions, leaving some gaps for the 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 schema description coverage is 100%, so the baseline is 3. The description adds meaningful context by explaining the semantic effect of omitting the key parameter ('omit to list all keys'), which clarifies the tool's dual functionality beyond what the schema alone provides.

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

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

The description clearly states the tool's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from siblings like 'remember' (store) and 'forget' (delete) by focusing on retrieval operations.

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

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

The description provides explicit guidance on when to use this tool: 'to retrieve context you saved earlier in the session or in previous sessions.' It also specifies when to omit the key parameter to list all memories, offering clear usage instructions.

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 parallel fan-out to multiple sources, date format flexibility, and return structure (changes, count, URIs). Missing details on error handling or rate limits, but generally 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 paragraph that efficiently covers purpose, behavior, and usage. It is front-loaded with the core purpose and adds details without waste. Minor room for improvement in 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?

No output schema exists, so description explains return values (structured changes, count, URIs). It covers the parallel fan-out and date formats. Lacks discussion of error cases or pagination, but sufficient for a read-only tool.

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

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

Schema has 100% description coverage, so baseline is 3. The description adds relative date examples for 'since' and clarifies ticker/CIK for 'value', but adds minimal value beyond 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 specifies the tool's function: 'What's new about an entity since a given point in time', with explicit entity type (company) and sources (SEC EDGAR, GDELT, USPTO). This distinguishes it from sibling tools like entity_profile or search_products.

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

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

The description provides explicit use cases: 'brief me on what happened with X' or change-monitoring workflows. It lacks explicit when-not-to-use or comparison to alternatives, but the usage 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 and adds valuable behavioral context beyond basic functionality. It discloses persistence traits ('Authenticated users get persistent memory; anonymous sessions last 24 hours'), which informs the agent about data retention and authentication requirements, though it doesn't cover rate limits or error handling.

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

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

The description is appropriately sized and front-loaded, with two sentences that efficiently convey purpose and behavioral traits without wasted words. Each sentence adds clear value: the first defines the tool's function, and the second provides critical context on memory persistence.

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

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

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is largely complete. It covers purpose, usage, and key behavioral aspects like persistence, but lacks details on return values or error cases, which could be helpful for a storage tool without 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?

The input schema has 100% description coverage, so the schema already documents both parameters ('key' and 'value') with examples. The description does not add any parameter-specific details beyond what the schema provides, such as constraints or usage tips, meeting the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('store a key-value pair') and resource ('in your session memory'), distinguishing it from siblings like 'forget' (remove) and 'recall' (retrieve). It explicitly mentions what can be stored ('intermediate findings, user preferences, or context across tool calls'), making the purpose unambiguous.

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

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

The description provides clear context on when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), but does not explicitly state when not to use it or name alternatives. It implies usage for persistence needs but lacks explicit exclusions or comparisons to other memory-related tools like 'forget' or 'recall'.

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 clearly explains the tool's behavior: it resolves an entity and returns canonical IDs (ticker, CIK, company name, URIs). While it does not explicitly state it is read-only, the nature of the operation suggests no side effects. A minor improvement would be stating it is safe to use.

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

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

The description is extremely concise: two sentences that capture purpose, inputs, outputs, and benefit. Every sentence adds value, and the structure is 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 no output schema, the description adequately explains return values (ticker, CIK, company name, URIs). It mentions replacing multiple calls, adding context. However, it lacks details on error handling or behavior when inputs are ambiguous, which would make it more complete.

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

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

The input schema already provides descriptions for both parameters (100% coverage). The description adds value by providing concrete examples (e.g., 'AAPL', '0000320193', 'Apple') and clarifying that 'type' is limited to 'company' in v1, enhancing semantic understanding beyond the schema.

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

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

The description clearly states the tool's purpose: resolving an entity to canonical IDs. It specifies the entity type (company) and inputs (ticker, CIK, name), distinguishing it from sibling tools like 'ask_pipeworx' or 'search_products' which have different scopes.

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 by noting it replaces 2-3 lookup calls, but does not explicitly state when to use this tool versus alternatives or provide exclusions. More guidance on when not to use it 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 provided, the description carries full burden for behavioral disclosure. It mentions what the tool returns but doesn't describe important behaviors like pagination, rate limits, authentication requirements, error conditions, or whether this is a read-only operation. The description provides basic functional information but lacks operational context.

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

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

The description is perfectly concise - a single sentence that efficiently communicates the tool's function and return values. Every word earns its place with no wasted text, and the information is front-loaded appropriately.

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 search tool with 2 parameters and no output schema, the description provides basic functional information but lacks important context. Without annotations or output schema, it should ideally describe the return format more thoroughly (structure of results, pagination behavior) and operational constraints. The description is adequate but has clear 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?

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description mentions searching 'by name, brand, or keyword' which aligns with the query parameter, but adds no additional semantic context beyond what's in the schema. This meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('search for food products') and resources ('food products'), and mentions what it returns. However, it doesn't explicitly distinguish this search tool from its sibling 'get_product' tool, which might be for retrieving specific products by ID rather than searching.

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 its sibling 'get_product'. It mentions what the tool does but doesn't specify use cases, prerequisites, or alternatives. This leaves the agent without clear direction on tool selection.

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

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

With no annotations, the description must bear the full burden of behavioral disclosure. It mentions authoritative sources and scope, but does not disclose side effects, authentication needs, rate limits, or error behavior (e.g., what happens with non-financial claims). The description adequately informs about the core function but lacks comprehensive behavioral 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 remarkably concise: two sentences with no wasted words. The first sentence states the purpose, the second adds specifics about scope and output. It is front-loaded and 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 has only one parameter and no output schema, the description explains the output types but not their exact structure. It mentions verdicts, structured form, actual value, citation, and delta, which is mostly complete. However, without an output schema, the description could be more precise about the return format to avoid ambiguity.

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

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

The schema already covers the parameter 'claim' with a description. The tool description adds further context on the type of claims allowed and provides examples, adding meaning beyond the schema. However, it could still include more constraints (e.g., language expectations, claim formatting).

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

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

The description clearly states the tool's purpose: fact-check a natural-language claim against authoritative sources (SEC EDGAR + XBRL). It specifies the scope ('company-financial claims') and the output (verdicts, structured form, actual value with citation, percent delta). It also distinguishes from siblings by noting it replaces 4-6 sequential agent calls.

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

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

The description provides clear context on when to use the tool (for fact-checking financial claims) and explains what it replaces (multi-step manual process). However, it does not explicitly state when not to use it (e.g., for non-financial claims) or list alternatives among sibling tools.

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