Nws

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

Without annotations, the description carries full burden. It explains the tool routes queries across many sources and fills arguments, but it does not disclose error handling, response format, rate limits, or potential failure modes. This is adequate but not comprehensive.

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 (three sentences) and well-structured: purpose first, then usage context, then examples. No superfluous 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?

For a simple one-parameter tool with no output schema, the description is fairly complete, covering functionality, use cases, and examples. It lacks details on limitations or response structure, but overall is adequate.

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

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

With 100% schema coverage, the description adds value by providing example queries that illustrate the scope of the 'question' parameter, going beyond the schema's generic description.

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

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

The description clearly states the tool answers natural-language questions by automatically selecting the right data source, with a specific verb and resource. It distinguishes itself from sibling tools (e.g., compare_entities, discover_tools) by emphasizing it is a general-purpose query router.

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

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

The description explicitly lists when to use the tool (e.g., 'What is X?', 'Look up Y') and that it alleviates the need to pick a specific tool. However, it does not provide when-not-to-use guidance or alternatives, which would be beneficial.

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 behaviors. It does so by naming data sources (SEC EDGAR/XBRL, FAERS, FDA) and output format (paired data + citation URIs). It implies a read-only, batch operation. The description could be clearer about side-effects (none), but the read-only nature is evident from the wording.

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 efficient: a single paragraph of about 70 words. It front-loads the core purpose, provides usage triggers, breaks out the two modes with clear examples, and ends with efficiency benefits. Every sentence adds value.

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

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

Given the absence of an output schema, the description adequately explains return value structure ('paired data + citation URIs') and the data points for each entity type. It covers input validation (2-5 required) and data sources. A mention of error handling or edge cases would push it to 5, but it is complete enough for confident tool selection.

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 significant value by explaining how each 'type' parameter affects data sources and output fields (e.g., revenue, net income for companies; adverse events, approvals for drugs). It also gives example values like tickers, which are not in the schema description.

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

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

The description opens with a clear verb and resource: 'Compare 2-5 companies (or drugs) side by side in one call.' It specifies the action, the entity types, and the count. This immediately differentiates it from single-entity tools like entity_profile.

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

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

The description provides explicit trigger phrases ('compare X and Y', 'X vs Y', etc.) and use cases (tables/rankings of financial or drug data). It notes that this tool replaces 8-15 sequential calls, implicitly steering agents away from using multiple single-entity calls. However, it does not explicitly state when not to use it (e.g., for a single entity).

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

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

No annotations provided, so description must disclose behavioral traits. It only states it returns relevant tool names and descriptions, but does not mention safety (e.g., read-only, no side effects) or any constraints, leaving agents uncertain about potential impacts.

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 slightly long due to listing many domains, but each serves to clarify use cases. It is front-loaded and efficiently conveys purpose. Minor redundancy could be trimmed.

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 mentions return of names+descriptions. Missing details on pagination, sorting, or whether tools are from the current server only. Adequate for a simple list tool but not fully complete.

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

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

Schema coverage is 100% with descriptions for both parameters. Description adds no additional meaning beyond listing example queries; 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?

Description explicitly states it finds tools by describing data/task, lists many example domains (SEC filings, financials, etc.), and distinguishes from sibling tools by advising to call it FIRST when exploring options.

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

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

Description clearly says when to use (browse, search, discover tools) and provides specific examples. It advises calling it first, but does not explicitly exclude scenarios where it should not be used, missing some 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?

No annotations are provided, so the description carries the full burden. It discloses the return content (SEC filings, revenue, patents, news, LEI) and mentions citation URIs (pipeworx://). It does not mention side effects, rate limits, or authentication needs, but for a read-only aggregation tool, the disclosed information 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 a single paragraph that front-loads the core purpose immediately. It contains some redundancy in the usage examples, but overall it is efficient and well-structured for an AI 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 no output schema, the description does a good job explaining the returned data types and sources. It covers all key aspects a user would expect. Minor gaps include no mention of pagination or response size limits, but for a summary tool this is acceptable.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds significant value beyond the schema: it explains that type is currently limited to 'company', that value can be ticker or zero-padded CIK, and explicitly warns that names are not supported (pointing to resolve_entity).

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' and lists specific data types (SEC filings, fundamentals, patents, news, LEI). It distinguishes from sibling tools by noting it aggregates data that would require 10+ separate pack tools.

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

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

Explicitly provides example user queries ('tell me about X', 'give me a profile of Acme', etc.) and tells when not to use (names not supported, use resolve_entity first). This clearly guides the agent on when to invoke this tool versus 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 exist, so the description carries full burden. It only says 'delete' but does not disclose if the deletion is permanent, what happens if the key doesn't exist, or any side effects. This lack of detail for a destructive operation limits 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 two sentences with no fluff. The purpose is front-loaded and every word serves a clear function, making it concise and well-structured.

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

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

Given the tool's simplicity (one parameter, no output schema), the description covers the essential context: what it does and when to use it. It could mention that the key must exist, but overall it is sufficient for a minimal 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 one parameter 'key' described as 'Memory key to delete'. The description does not add any additional meaning beyond the schema, so it meets the baseline but does not enhance 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 verb 'delete' and the resource 'memory by key'. It distinguishes itself from sibling tools 'remember' and 'recall' by naming them, 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 explicit when-to-use scenarios: 'when context is stale, the task is done, or you want to clear sensitive data'. It also suggests pairing with 'remember' and 'recall', offering contextual guidance, though it doesn't explicitly state when not to use 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 carries full burden. It lists return fields but doesn't mention pagination, rate limits, or whether the data is read-only. It's adequate but not exhaustive.

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

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

Two sentences, no fluff. Front-loaded with purpose and immediately followed by filter details and return fields.

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 covers return fields. For a list tool with 7 optional parameters, it adequately explains filtering and output. Minor gap: doesn't mention default limit.

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

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

Schema coverage is 100%, so baseline is 3. The description adds context about filtering by state, point, severity, or status, but that's already in the schema. No additional parameter semantics beyond grouping.

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

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

The description clearly states it retrieves active NWS watches/warnings/advisories, with specific filter options. It distinguishes from sibling tools like get_forecast and get_observation.

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

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

The description implies when to use: for active alerts and with filters. It doesn't explicitly state when not to use or list alternatives, but the scope is well-defined.

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

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

No annotations provided, so description carries full burden. It discloses return structure (named periods, high/low, wind, text) and geographic restriction. Does not cover auth, rate limits, or error behavior, but for a read-only forecast tool this is sufficient.

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

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

Two sentences, no fluff. First sentence states purpose and key constraint, second sentence details return format. Every word adds value.

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

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

No output schema, so description must explain return. It adequately describes named periods, key data fields (high/low, wind, text), and time horizon (7-day). No obvious missing info for a weather forecast 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 brief descriptions ('US latitude', 'US longitude'). Description reinforces 'US lat/lon' but adds no new detail beyond schema. Baseline 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?

Description clearly states verb 'Get' and resource '7-day NWS forecast' for US lat/lon. Specifies return content (named periods with temp, wind, text). Distinct from siblings like get_hourly_forecast by mentioning '7-day' and 'named periods'.

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 constraint 'US locations only' but does not guide when to use this vs alternative siblings like get_hourly_forecast or get_observation. No explicit when-not or alternatives.

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

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

No annotations provided, so description must carry full burden. Mentions hourly nature and 168-hour window, but does not disclose traits like data freshness, error handling, or rate limits. Adequate for a simple read operation.

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

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

Two concise sentences: first states purpose, second gives use cases. No wasted words, front-loaded with key 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?

Completeness is adequate for a simple tool with well-documented schema. No output schema, so return format is not described, but the description gives enough context for an agent to infer the result is a list of hourly forecasts.

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%, baseline is 3. Description adds no additional meaning beyond what the schema already provides for latitude, longitude, and max_hours.

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

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

Clearly states 'Get the hourly NWS forecast for a US lat/lon (~168 hours)'. Specific verb, resource, and scope. Distinguishes from sibling 'get_forecast' implicitly by mentioning hourly.

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: 'short-term planning, severe-weather windows, or precipitation timing'. No guidance on when not to use or alternatives, but the use cases are 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, the description provides reasonable transparency by listing the return fields (temperature, humidity, etc.) and indicating a read operation, though it omits details on error handling or data freshness.

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

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

The description is a single, well-structured sentence that conveys 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?

Given no output schema, the description partially compensates by naming returned fields, but lacks details on data structure or units, which would improve completeness for an agent.

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

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

The description does not add meaning beyond the schema for the single parameter station_id, which already has a thorough description. Schema coverage is 100%, baseline 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 clearly states 'Latest observation from a specific NWS weather station' and lists the data returned, making it easy to distinguish from sibling tools like get_forecast or get_alerts.

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 for current weather conditions but lacks explicit when-to-use or when-not-to-use guidance, especially with siblings like get_forecast and get_hourly_forecast available.

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 the description fully covers behavioral traits: rate-limited to 5 per identifier per day, free, doesn't count against quota, and that team reads digests daily. 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?

Description is concise with no redundant information. Front-loaded with purpose, uses clear structure, every sentence adds value.

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

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

Given no output schema, the description provides complete context: purpose, usage guidance, behavioral traits, and parameter hints. Covers all necessary information for an agent to use the tool correctly.

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

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

Schema coverage is 100% with good descriptions, but the description adds useful guidance (e.g., 'Be specific', '1-2 sentences typical', '2000 chars max') that aids usage 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?

The description clearly states the tool is for providing feedback about bugs, missing features, data gaps, or praise. It lists specific types and explains each, distinguishing it from sibling tools like ask_pipeworx or discover_tools.

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

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

Explicitly states when to use the tool for each feedback type (bug, feature, data_gap, praise) and what not to do (don't paste end-user prompt, describe in terms of tools/packs). Includes rate limit and quota info.

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

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

No annotations provided, so description carries full burden. It discloses retrieval vs. listing, scoping by identifier, and pairing with remember/forget. Missing details on error behavior (e.g., missing key).

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 concise at 4 sentences, front-loaded with purpose. Each sentence adds value, though slight improvements could be made for tighter phrasing.

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

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

Given the simple input (one optional param) and no output schema, the description is mostly complete. It covers purpose, usage, scoping, and pairing. Lacks return format details.

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

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

Schema has 100% coverage (key parameter with description). Tool description adds value by explaining omit behavior (list all keys) and scoping, complementing 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 retrieves a value saved via 'remember' or lists all keys, distinguishing it from siblings 'remember' (save) and 'forget' (delete).

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

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

It provides clear context for when to use (look up stored context) and implies alternatives (remember/forget), but lacks explicit when-not-to-use or direct sibling 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 exist, so the description carries full burden. It discloses parallel fan-out to SEC, GDELT, USPTO and output structure (changes, count, URIs). However, it does not explicitly state the tool is readonly or mention error handling or rate limits.

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

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

The description is moderately sized (~130 words) and well-structured: opening purpose, usage examples, then internal behavior and return details. It is concise and front-loaded, though could be slightly trimmed.

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 values and mentions citation URIs. It covers the fan-out logic and parameter formats. However, it lacks details on result limits, pagination, or error cases, but these are not critical for basic usage.

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

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

Schema coverage is 100%, and the description adds significant value: for 'since', it explains both ISO and relative formats with examples; for 'value', it clarifies ticker vs. CIK; for 'type', it confirms only 'company' is supported, providing context beyond enums.

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

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

The description clearly states the tool's purpose: retrieving recent changes for a company. It provides multiple user query examples and distinguishes from siblings like entity_profile by focusing on temporal changes.

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

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

The description explicitly lists use cases and example queries (e.g., 'what's happening with X?', 'news on Apple this month'). However, it does not specify when not to use the tool or mention alternatives, but the context is clear enough.

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

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

No annotations provided; description reveals persistence details (authenticated vs anonymous, 24-hour retention) and scoping by identifier, which is adequate for 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?

Short, front-loaded paragraph with every sentence adding value; no wasted words. Purpose, usage, and details are logically ordered.

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

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

Given simple parameters, no output schema, and no annotations, the description covers purpose, usage, persistence, and pairing. Missing potential limits (size, number) but acceptable.

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 describes both parameters fully (100% coverage). The description adds example values and reinforces meaning but does not significantly extend 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?

The description clearly states the action ('save') and the resource ('data the agent will need to reuse later'), and distinguishes from siblings like 'recall' and 'forget' by explicitly mentioning them.

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 when-to-use guidance with examples ('discover something worth carrying forward'), and pairs with recall/forget, though no explicit 'when not to use' is given.

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

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

The description discloses the return values (IDs + citation URIs) and the ID systems covered. Without annotations, it carries the full burden; it is transparent about what the tool does but does not mention error conditions or rate limits.

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

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

The description is concise and front-loaded with the core purpose. Examples are helpful. A minor reduction in redundancy (e.g., 'Replaces 2–3 lookup calls') could improve conciseness, but overall it is well-structured.

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

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

Given no output schema, the description adequately explains the return format (IDs and URIs). It covers the main use case and context, though it lacks details on potential multiple matches or errors. For a lookup tool, this is sufficient.

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

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

Schema coverage is 100% with both parameters described. The description enhances understanding with examples and explains how to use the 'value' parameter for different types, adding significant value beyond the schema.

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

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

The description clearly states the tool's purpose: resolving entity names to canonical identifiers (CIK, ticker, RxCUI, LEI). It provides specific examples and distinguishes from potential similar tools by noting it replaces multiple 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 advises using this tool before others that need official identifiers, providing clear usage context. However, it does not explicitly state when not to use it or mention alternatives among siblings like entity_profile.

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 return format (verdict, extracted form, actual value with citation, percent delta) and domain limitations (v1 supports company-financial claims). No destructive behavior is implied, and the input-only nature is clear. Could mention safety or auth, but overall 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 a single paragraph of 5-6 sentences, front-loaded with purpose. It is concise but could benefit from scannable structure (e.g., bullet points for return values). No unnecessary 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 simple input schema (1 param, no output schema, no annotations), the description covers purpose, usage, domain, and return format adequately. Missing details like error handling are minor for this tool's 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?

Only one parameter 'claim' with 100% schema coverage. The description adds examples and clarifies claim scope beyond the schema's generic description, providing strong semantic guidance.

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 'Fact-check, verify, validate, or confirm/refute a natural-language factual claim', providing a specific verb and resource (authoritative sources). It gives example queries and distinguishes from siblings like ask_pipeworx by focusing on structured claim validation.

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?

Clearly says 'Use when an agent needs to check whether something a user said is true' with example phrasing. Does not explicitly list when not to use or alternatives, but the domain restriction (US company financial claims) implicitly guides usage. The note about replacing 4-6 calls adds efficiency context.

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