Medlineplus

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

No annotations provided, so the description covers behavioral traits. It explains that the tool routes across 300+ sources, picks the right tool, and fills arguments. This gives good insight into its behavior beyond basic functionality, though it could mention potential latency or 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, with a clear first sentence stating the purpose, followed by usage guidance and examples. Every sentence adds value without redundancy.

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

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

Given the simple single-parameter schema and no output schema, the description is fairly complete. It explains the tool's routing behavior and gives examples. However, it could be slightly more explicit about the output format or any limitations.

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 single 'question' parameter with a clear description. The tool description adds value by providing example questions and clarifying the scope of what can be asked, which goes beyond the schema's minimal 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 picking the right data source, effectively distinguishing it from sibling tools that are more specific. The verb 'answer' and resource 'natural-language question' are 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 explicitly lists example use cases ('What is X?', 'Look up Y', etc.) and indicates when to use this tool (when the user doesn't want to figure out which specific tool to call). However, it does not explicitly state when not to use it or mention alternative 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?

With no annotations, the description covers data sources (SEC EDGAR, FAERS), return type (paired data + citation URIs), and efficiency (replaces 8-15 calls). It does not mention side effects or read-only status, but the description implies safe read-like behavior.

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

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

The description is well-structured with front-loaded purpose, usage cues, then details per type, then output note. It could be slightly shorter, but each 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?

No output schema exists, so the description should fully explain return format. While it mentions 'paired data + citation URIs', it is vague. A richer description of the comparison output (e.g., table 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%, baseline 3. The description adds value by explaining the difference between company and drug types, giving ticker/drug name examples, and detailing what data each type pulls.

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 compares 2-5 companies or drugs side by side, listing specific data sources and use cases. It differentiates from siblings like entity_profile (single entity) and search (general query).

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 explicit trigger phrases like 'compare X and Y' and 'X vs Y', and specifies entity types with examples. It does not mention when not to use, but the intent 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 are empty, so description should disclose behavioral traits. It does not mention if the operation is read-only, idempotent, has side effects, or requires special permissions. The description only states the mapping function.

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 directly conveys the purpose with no fluff. It is appropriately front-loaded and efficient, though could benefit from a brief usage scope.

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, yet the description omits what is returned (e.g., topic titles, IDs, multiple matches). Does not mention error behavior, performance, or handling of invalid codes. Incomplete for a mapping 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 description coverage is 100%, so the description adds minimal value beyond the schema. The description reiterates the mapping purpose but does not provide additional parameter-specific context beyond the schema's descriptions.

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

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

The description clearly states the tool maps clinical codes to MedlinePlus topics, listing supported code systems with a specific verb 'Map' and resource 'MedlinePlus consumer-health topics'. It is distinct from sibling tools like 'resolve_entity' and 'search'.

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 (e.g., 'resolve_entity' or 'search'). Lacks conditions, prerequisites, or exclusions. Merely states what it does.

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 discloses that the tool returns 'top-N most relevant tools with names + descriptions,' indicating a read-only, non-destructive behavior. However, it could further clarify that it does not execute any actions or modify state, and mention rate limits or auth requirements if any exist.

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

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

The description is moderately concise (three sentences), with the purpose stated upfront. Every sentence serves a purpose: purpose, usage context, and behavior. Slightly longer than necessary due to the domain list, but the list adds significant value for a discovery tool.

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

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

For a simple tool with 2 parameters and no output schema, the description covers purpose, usage, and return type. It mentions 'top-N most relevant tools' but does not specify how relevance is determined or guarantee completeness. Still, it is sufficient for an agent to select and invoke 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 clear parameter descriptions. The description adds value by providing context for the query parameter with concrete examples ('analyze housing market trends') and explaining default/max for limit. It goes beyond the schema by listing example domains, which aids 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 clearly states the tool's purpose: 'Find tools by describing the data or task.' It specifies the verb (discover/find) and resource (tools), and distinguishes itself from siblings by positioning itself as a first-step discovery tool ('Call this FIRST when you have many tools available').

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

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

Explicitly says 'Use when you need to browse, search, look up, or discover what tools exist for...' followed by a comprehensive list of domains. Provides clear alternative guidance: 'Call this FIRST... want to see the option set (not just one answer).' This leaves no ambiguity about when to use the 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?

With no annotations, the description carries full burden. It discloses the tool is a read operation (returns data) and lists returned items (SEC filings, fundamentals, patents, news, LEI). It does not explicitly state read-only, but the context implies no side effects. It also mentions limitations (only 'company' type). Slightly more explicit about non-destructive nature could elevate score.

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

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

The description is a single paragraph that front-loads the main purpose. It contains useful detail but could be slightly more structured (e.g., bullet points for returned items). However, it is efficient and avoids fluff.

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

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

Given no output schema, the description explains return values comprehensively: recent SEC filings, fundamentals, patents, news, and LEI. It also addresses how to handle unsupported input (names) by directing to resolve_entity. This is complete for the 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?

Schema has 100% coverage, but the description adds value by providing examples ('AAPL', '0000320193') and explaining that CIK must be zero-padded. It also clarifies that names are not supported. This adds meaning beyond the schema.

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

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

The description clearly states the tool's purpose: 'Get everything about a company in one call.' It uses specific verbs ('Get', 'Returns') and identifies the resource ('company'). It distinguishes from sibling tools by noting it replaces calling 10+ pack tools across various domains.

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

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

Explicit usage scenarios are given: when a user asks 'tell me about X', 'give me a profile', etc. It advises using resolve_entity for names and specifies that only ticker or CIK are accepted. This provides clear when-to-use and when-not-to-use 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?

The description clearly states the tool is destructive ('delete') and mentions clearing sensitive data. However, it does not specify what happens if the key does not exist or if the operation is reversible. With no annotations, it partially fulfills the burden.

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 with no redundancy. Every clause adds value: action, usage guidelines, sibling pairing.

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 1-parameter delete tool, the description covers purpose, usage context, and relationships. No output schema needed; the behavior is straightforward. Complete for the complexity level.

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 the single parameter ('key') with a basic description. The tool description adds no new semantic information beyond the schema, so 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 clearly states the action ('Delete a previously stored memory by key') and differentiates from siblings by mentioning 'Pair with remember and recall.' The core purpose is 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?

Explicitly gives three distinct when-to-use scenarios ('context is stale, the task is done, or you want to clear sensitive data') and pairs with siblings, implying alternative tools for other 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?

No annotations exist, so description carries full burden. It discloses free cost, no quota impact, daily team review, and roadmap influence. Slightly lacks explicit mention of outcome after submission, but 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?

All sentences earn their place; purpose first, then usage, then constraints. 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?

Covers purpose, usage, behavioral traits, parameter guidance, and constraints. No output schema expected; description is self-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%, so baseline is 3. Description adds value by explaining how to format messages and use context, and reinforces enum meanings without redundancy.

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: sending feedback about bugs, features, data gaps, or praise. It distinguishes itself from sibling tools (which are query/memory tools) by being a submission channel.

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 (bug, feature, praise) and when-not-to (don't paste user prompts). Mentions rate limits and non-quota behavior, giving clear boundaries.

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, description discloses key behaviors: retrieving/listings, scoped to identifier, pairs with remember/forget. No mention of permissions or rate limits, but adequate for simple retrieval.

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

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

Three concise sentences, front-loaded with core action, no wasted words. 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?

For a simple one-param tool with no output schema, description covers retrieval behavior, scoping, and pairing. Complete for agent selection and invocation.

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 that omitting key lists all saved keys, gives examples. Schema coverage is 100% and description enriches it.

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 verb (retrieve/list) and resource (saved memory). Distinguishes from siblings (remember, forget) and specifies listing all keys when key omitted.

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?

Describes when to use: look up context stored earlier, with examples. Implies not for saving/deleting. Mentions scoping but no explicit when-not or alternatives beyond pairing.

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, so description carries full burden. It discloses fan-out to three sources (SEC, GDELT, USPTO) and return structure (changes, count, URIs). No destructive behavior implied.

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 paragraph but well-organized: purpose, usage, behavior, parameters. Could be broken into sections, but 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?

No output schema, but description explains return fields (structured changes, total_changes, URIs). Covers fan-out and parameter formats. Fully adequate for agent invocation.

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 examples for 'since' format (ISO date, relative) and 'value' (ticker/CIK), enhancing 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 it returns recent changes for a company, with example queries. Distinguishes from siblings like entity_profile or search by focusing on temporal monitoring.

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 user query examples (e.g., 'what's happening with X?') and suggests default time windows. Does not explicitly mention when not to use, 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?

Discloses that data is scoped by identifier, persistent for authenticated users, and 24hr TTL for anonymous sessions. Does not explicitly mention overwrite behavior for existing keys, but overall 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?

Three sentences: first states purpose, second gives usage context, third provides persistence details. Front-loaded and no unnecessary words.

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

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

Given no output schema and simple 2-param input, the description fully covers purpose, usage, scope, and pairing with siblings. No 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 covers 100% of params with descriptions. Description adds context on naming conventions and allowed values, but schema already handles semantics adequately. 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?

Specifies verb 'save data' and resource 'key-value pair', explains it is for reuse across conversations/sessions. Distinguishes from siblings like 'recall' and 'forget'.

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

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

Explicitly states when to use: 'when you discover something worth carrying forward'. Mentions persistence differences for authenticated vs anonymous sessions, and suggests pairing with 'recall' and 'forget'.

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

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

No annotations provided, so description carries full burden. It discloses that the tool returns IDs plus pipeworx:// citation URIs and provides examples of outputs. Does not mention authentication, rate limits, or side effects, but for a lookup tool this is fairly transparent.

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

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

Two concise sentences plus an example line. Every sentence adds value: purpose, usage context, examples, output format, and relation to other tools. No superfluous 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?

Despite no output schema, the description explains what is returned (IDs plus pipeworx:// URIs) and gives concrete examples. It covers purpose, usage, input parameters, output format, and workflow positioning, making it fully complete for a moderately 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 descriptions for both parameters. The description adds value by providing specific examples (e.g., 'Apple' → AAPL) and clarifying acceptable input formats (ticker, CIK, name for companies; brand/generic for drugs) 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 it looks up canonical IDs for companies/drugs, lists specific ID systems (CIK, ticker, RxCUI, LEI), and distinguishes itself from siblings by indicating it should be used before other tools that need identifiers.

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 CIK...' and 'Use this BEFORE calling other tools that need official identifiers.' Provides clear context but does not explicitly mention when not to use it or alternatives besides referencing 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?

With no annotations provided, the description carries full responsibility for behavioral transparency. It only states the tool performs free-text search but does not disclose behavioral traits such as authentication requirements, rate limits, result ordering, or any side effects. This is insufficient for a tool with no 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, efficient sentence with no wasted words. It is concise and front-loaded, clearly stating the tool's purpose. While minimal, it earns its length.

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 5 parameters, no output schema, and no annotations, the description provides adequate but not complete context. It does not explain return values, scope of 'related NLM databases,' or pagination behavior. It is sufficient for basic understanding but leaves 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 high (80%), so the schema already documents most parameters. The description adds no additional meaning about parameters—it only describes the tool's overall function. Baseline 3 is appropriate; no extra value added.

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 performs 'Free-text search across MedlinePlus topics (or related NLM databases),' specifying the verb 'search' and the resource. It effectively distinguishes this tool from siblings, which are all different functions (e.g., ask_pipeworx, 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 implies usage for general search across health topics and databases, but it does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. The context is clear but lacks directiveness.

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 the burden. It discloses return verdict types and cites caching/speed benefit, but does not discuss rate limits, authentication needs, or behavior for unsupported claim types. Adequate but not thorough.

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 but well-structured: purpose, usage guidance, domain/output detail, and efficiency note. Every sentence adds value with no 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?

Given one parameter, no output schema, and no annotations, the description is remarkably complete: it explains purpose, when to use, output structure, and domain limitation.

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 description already. The tool description adds value by specifying the domain of claims (company-financial) and providing concrete examples, enhancing 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 fact-checks/validates natural-language factual claims against authoritative sources, specifically for company-financial claims for US public companies. It distinguishes itself from sibling tools like ask_pipeworx or entity_profile by its unique function.

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 an agent needs to check whether something a user said is true' with examples of query forms. It sets domain limitations ('v1 supports company-financial claims') but does not provide explicit when-not-to-use guidance for out-of-domain claims.

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