Nuget

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

No annotations are provided, so the description must fully disclose behavior. It does so by stating the tool 'picks the right tool, fills arguments, returns the result' and routes across many sources. It doesn't mention limitations (e.g., accuracy, latency) but for a query tool, this 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 well-structured with a clear purpose statement, usage guidance, examples, and scope. It is slightly long but each sentence contributes meaning, making it efficient without being verbose.

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

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

Given the tool's complexity (routing across 300+ sources) and the absence of an output schema, the description is mostly complete about inputs and behavior but lacks information about the return format or content type. It only says 'returns the result', which is vague and could be more helpful.

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

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

The input schema has one parameter with a simple description. The description adds significant value by providing examples of natural-language questions (e.g., 'Adverse events for ozempic') that illustrate expected usage beyond the schema's minimal note.

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, with specific examples and a list of over 300 sources. It effectively distinguishes from siblings like 'search' by emphasizing that the agent doesn't need to choose which specific Pipeworx tool to call.

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

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

The description explicitly tells when to use the tool ('when a user asks 'What is X?'... and you don't want to figure out which Pipeworx pack/tool to call'). While it doesn't provide a 'when not to use' or list alternatives, the context of sibling tools and the meta-routing nature makes the usage 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?

Despite no annotations, the description details data sources for each type (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs) and specifies returned fields (revenue, net income, etc.). It also mentions the output format ('paired data + pipeworx:// citation URIs'). Missing are error handling and authentication details, but it 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?

The description is concise (about 100 words), front-loaded with the main purpose, and organized logically. Every sentence serves a purpose: use cases, per-type details, and efficiency benefit. No redundant or vague statements.

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 describes return fields for both types (e.g., revenue, net income for companies; adverse-event counts for drugs). It does not detail the exact JSON structure or error handling, but it provides enough context for an agent to understand the tool's behavior.

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 describes both parameters with 100% coverage, so the baseline is 3. The description adds meaning beyond the schema by specifying data sources and exact financial metrics per entity type, enriching the agent's understanding of what data will be retrieved.

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's first sentence, 'Compare 2–5 companies (or drugs) side by side in one call,' immediately states the specific verb and resource. It implicitly distinguishes from the sibling 'entity_profile' by emphasizing batch comparison and efficiency (replacing 8–15 sequential 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 explicit usage patterns: 'Use when a user says "compare X and Y", "X vs Y",... or wants tables/rankings of revenue / net income / cash / debt across companies — or adverse events / approvals / trials across drugs.' It does not include when-not conditions or explicit alternative tool names, but the context is clear.

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

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

With no annotations, the description carries full burden. It states the tool is for discovery, returns top-N results with names and descriptions, and implies a read-only search. It lacks details on caching or error handling, but overall is transparent about its 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 paragraph that front-loads the purpose, then lists examples, then output, then usage guidance. It is efficient but the list of topics is somewhat lengthy; however, it adds clarity. Could be slightly more concise but well-structured.

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

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

For a discovery tool with no output schema and no annotations, the description covers what it does, when to use it, and the output format. It does not explain relevance ranking or sorting, but is sufficient for an agent to select and invoke it correctly.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds value by providing natural language examples for the 'query' parameter (e.g., 'analyze housing market trends'), which aids understanding beyond the schema's description. The 'limit' parameter is not further explained, but the schema already covers defaults and max.

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 'Find' and resource 'tools', and distinguishes it from siblings by emphasizing discovery and browsing over specific tasks. It lists concrete examples of data domains, 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?

Explicitly says 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear when-to-use and implies not for narrow, single-tool queries.

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 all returned data categories (SEC filings, fundamentals, patents, news, LEI) and mentions citation URIs, but lacks details on response format, pagination, or any side effects. Still transparent enough for safe invocation.

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 detailed and front-loaded with purpose, but every sentence earns its place. Could trim slightly without losing information, but overall efficient for the complexity.

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 is provided; the description lists the major categories of returned data but lacks structure details (e.g., field names, nesting). For a data-dense tool, more structure would aid reliable use. Still, the description covers key types.

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 both params fully; description adds value by explaining type enum limitation ('only company supported'), value formats (ticker or CIK), and the name resolution constraint, which the schema alone does not convey.

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

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

The description explicitly states the tool aggregates comprehensive company data in one call, with clear verb ('Get everything about a company') and specific use-case examples ('tell me about X', 'research Microsoft'), distinguishing it from the many individual pack tools listed as siblings.

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

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

Provides explicit when-to-use guidance with natural language examples and notes that names require resolve_entity first, implicitly excluding cases where raw identifiers aren't 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 description bears full burden. Clearly indicates destructive action (delete) and purpose of clearing sensitive data. Lacks explicit mention of irreversibility, but adequate for a simple delete 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, front-loaded with action, no wasted words. Efficiently conveys 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?

No output schema, but tool is simple delete. Description covers purpose, usage, and sibling context. Does not specify return values or error behavior, but sufficient for low 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%, so baseline is 3. Description adds no new meaning beyond schema's 'Memory key to delete'. No compensation needed, but no extra value.

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

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

Description uses specific verb 'Delete' and resource 'previously stored memory by key'. Clearly distinguishes from siblings by mentioning pairing with 'remember' and 'recall'.

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

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

Explicitly states when to use: when context is stale, task is done, or to clear sensitive data. Also recommends pairing with 'remember' and 'recall', providing 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 must fully disclose behavior. It only lists output types without stating read-only nature, error handling, 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 a single, clear sentence with no wasted words. It efficiently conveys the core purpose.

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

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

The description covers basic output (versions, tags, dependencies, dates) but lacks details on format, pagination, or limitations. With no output schema, more completeness would be beneficial.

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 baseline is 3. The description adds no extra meaning to the parameters beyond what the schema provides.

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

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

The description states 'Registration metadata for a package — versions, tags, dependencies, dates,' clearly indicating the tool retrieves package metadata. However, it does not explicitly differentiate from siblings like 'latest_version' or 'entity_profile', missing a chance to clarify its specific scope.

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

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

No guidance is provided on when to use this tool versus alternatives. The description does not mention any conditions, prerequisites, or exclusions.

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

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

With no annotations, the description should disclose behaviors like side effects, permissions, or error handling. It only states the function, omitting any such 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 a single, efficient sentence with no wasted words. It earns its place by stating the core function.

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

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

Given the simplicity (2 params, no output schema, no annotations), the description is too minimal. It fails to specify the return value format, error cases, or usage context.

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 'id' and 'prerelease'. The description adds no extra meaning beyond the schema, meeting the baseline.

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 the 'most recent released version of a package,' which is a specific verb-resource combination. It distinguishes itself from sibling 'list_versions' by focusing on the latest version.

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

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

No guidance is provided on when to use this tool versus alternatives like 'list_versions' or 'get_package.' There are no context notes or exclusions.

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

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

With no annotations, the description must disclose behavior. It only states it returns versions but omits details like read-only nature, authentication needs, or pagination.

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?

One sentence with no waste, but it is under-specified for the necessary details. It achieves conciseness at the cost of completeness.

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 low complexity (one parameter, no output schema), the description fails to explain what the output contains (e.g., list of version strings or objects). No context on return format.

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 sole parameter 'id' is documented in the schema with 'Package id'. The description adds no extra meaning beyond the schema, but schema coverage is 100%, so baseline 3 is appropriate.

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

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

The description is a noun phrase ('All published versions of a package') without a verb. It does not state the action the tool performs (e.g., list or retrieve), making its purpose unclear.

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 siblings like 'get_package' or 'latest_version'. No exclusions or alternative suggestions are provided.

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

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

With no annotations, the description carries the full burden and does well by disclosing rate limits (5 per identifier per day), that it's free and doesn't count against tool-call quota, and that feedback is read in daily digests and affects the roadmap. It doesn't mention the response format (likely no output), but the behavioral details are valuable.

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

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

The description is fairly detailed but structured: it starts with the main purpose, followed by when to use, behavior, and parameter hints. Every sentence adds value. It could be slightly more concise, but overall it is well-organized and front-loaded.

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 (no output schema, no annotations), the description covers purpose, usage scenarios, behavioral constraints, and parameter semantics. It provides enough context for an agent to correctly invoke the tool. The missing output format is not critical for a feedback tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the enum values for 'type' (bug, feature, data_gap, praise, other) and providing guidance for the 'message' field (be specific, 1-2 sentences, 2000 chars max). It also explains the optional 'context' object fields (pack, tool, vertical). This goes beyond the schema descriptions.

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

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

The description clearly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It specifies the verb ('tell') and the resource (feedback to the Pipeworx team). It distinguishes itself from sibling tools like ask_pipeworx (which is for asking questions) and discover_tools (for exploration) by focusing on reporting issues or suggestions.

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: for bugs (wrong/stale data), feature requests, data gaps, praise, or other. It also advises what not to do ('don't paste the end-user's prompt'). However, it doesn't provide direct comparisons with siblings to help the agent decide when not to use this tool.

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

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

No annotations provided, so description carries full burden. It discloses scoping (anonymous IP, BYO key hash, account ID) and behavior (omit key to list). Does not mention any destructive actions 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?

Three concise sentences, front-loaded with core purpose, then usage and scope. No 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?

Given 1 parameter and no output schema, the description adequately covers input behavior and context. Could mention return format but not essential.

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

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

Input schema has 100% coverage with description for 'key'. The tool description reaffirms the parameter meaning but adds minimal extra 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?

Description states 'Retrieve a value' and 'list all saved keys', clearly defining verb and resource. It distinguishes from siblings like remember and forget.

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

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

Explicitly describes use cases: 'look up context stored earlier'. Mentions pairing with remember and forget, but lacks explicit when-not-to-use or alternatives.

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

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

With no annotations, the description bears full burden. It discloses the parallel fan-out to SEC EDGAR, GDELT, USPTO and the return structure (changes, count, URIs). Could mention potential limits or latency, 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?

The description is concise with two information-dense sentences. It front-loads the purpose and usage examples, with no redundant or vague statements.

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

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

Given the tool's complexity (multi-source, no output schema), the description covers purpose, usage, parameter formats, and return structure. It could mention error handling or max window, but is largely 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%, but the description adds value by providing concrete examples for 'since' (e.g., '7d', '30d') and explaining that 'value' accepts ticker or CIK. This enhances 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: retrieving recent changes for a company. It uses specific phrases like 'what's new' and provides example queries, distinguishing it from siblings like entity_profile or 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?

The description gives explicit usage examples (e.g., 'what's happening with X?') and explains the tool fans out to multiple sources. It lacks explicit when-not-to-use or alternative suggestions, but the context is clear.

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

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

No annotations provided, so description carries full burden. Discloses scoping by identifier, persistence differences for authenticated vs anonymous users, and 24-hour retention for anonymous sessions. Could mention size limits or concurrency details, but covers key traits.

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

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

Four sentences, no waste. First sentence states purpose, second explains when to use, third details scoping and retention, fourth references sibling tools. 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?

No output schema, but description adequately covers what happens: data saved, scoped, with retention info. Also references retrieval and deletion tools, completing the lifecycle. Sufficient for a straightforward save operation.

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

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

Schema coverage is 100%, and description adds meaningful conventions: key examples (subject_property, target_ticker) and value explanation (any text – findings, addresses, etc.). This goes beyond the schema's type-only descriptions.

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

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

Clearly states verb 'save', resource 'data the agent will need to reuse later', and scope 'across conversations or sessions'. Distinguishes from siblings by mentioning 'pair with recall to retrieve later, forget to delete'.

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

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

Explicitly says when to use: 'when you discover something worth carrying forward'. Provides specific examples (resolved ticker, target address, etc.) and mentions pairing with recall and forget for retrieval and deletion.

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 IDs plus citation URIs and performance benefit. Could be more explicit about read-only nature, but sufficient for a lookup 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?

Four sentences, each adds value. Front-loaded with verb and outcome. 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?

No output schema, but description adequately describes return values (IDs + URIs). Covers both entity types and use case. Complete for a simple lookup tool with 2 parameters.

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

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

Input schema has 100% coverage with descriptions. Description adds concrete examples (e.g., 'Apple' → AAPL, CIK) and clarifies acceptable formats for value parameter, going 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?

Clearly states it looks up canonical IDs (CIK, ticker, RxCUI, LEI) for companies and drugs. Includes concrete examples and distinguishes from siblings 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 tells when to use: when a user mentions a name and needs official identifiers for other tools. Also specifies sequencing: use BEFORE other tools that need these IDs.

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

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

No annotations are provided, so the description must disclose all behavioral traits. It only says 'Full-text search' without mentioning return format, pagination behavior, rate limits, or any side effects. For a search tool, core behaviors like result limits and ordering are omitted.

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 gets the point across efficiently. It is front-loaded with the key action and resource. However, it is too brief to fully cover the tool's purpose, missing any follow-up details.

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 4 parameters, no output schema, and no annotations, the description is insufficient. It does not explain return values, pagination via skip/take, or the role of prerelease. The description does not provide enough context for an agent to use the tool effectively without relying entirely on the 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?

All four parameters have descriptions in the input schema (100% coverage), so the schema already defines their semantics. The description adds no extra meaning beyond what the schema provides, earning the baseline score of 3.

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

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

Clearly states 'Full-text search across NuGet packages.' The verb 'search' and resource 'NuGet packages' make purpose explicit. Although sibling tools like 'get_package' and 'list_versions' exist, the description differentiates by being a general full-text search over the entire package collection.

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

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

No guidance on when to use this tool versus alternatives like 'get_package' or 'list_versions'. The description only states what it does, without any when-to-use or when-not-to-use context. An agent would have no help deciding between search and other package-related 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 carries full burden. It discloses that v1 only supports company-financial claims via SEC EDGAR+XBRL and lists possible verdicts. It does not mention side effects or rate limits, but the tool appears read-only and safe.

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, front-loaded with the main action, then provides usage guidance, limitations, and return information. No redundant words; 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?

Given one parameter, no output schema, and no annotations, the description thoroughly covers purpose, when to use, supported domains, and return values. It is complete enough for an agent to correctly select and invoke the tool.

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

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

The schema covers 100% of the single parameter with a description. The tool description adds meaning by specifying the domain (company-financial) and providing examples of valid claims, which enhances 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 it fact-checks or validates natural-language claims against authoritative sources, using verbs like 'verify' and 'confirm/refute'. It distinguishes itself by noting it replaces multiple sequential calls, which sets it apart from siblings like 'search' or '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 guidance on when to use (checking truth of user statements) with examples, and notes the supported domain (company-financial). However, it lacks explicit exclusions for unsupported claim types, which would improve clarity.

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