Huggingface

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

No annotations are provided, so the description carries the full burden. It states that the tool routes across multiple sources and returns results, but does not disclose potential failure modes, authentication needs, rate limits, or how ambiguous questions are handled. The behavior is high-level but lacks detail on edge cases.

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 provides usage guidance and examples. It is concise without unnecessary words, though slightly more structured formatting could improve readability.

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

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

There is no output schema, so the description should ideally explain the return format. It only says 'returns the result'. For a tool that queries 300+ sources, more detail on what the result looks like (e.g., text, table, structured data) would be helpful. Coverage of inputs and purpose is good, but output is vague.

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 'question'. The description adds value beyond the schema by providing example questions and clarifying the type of natural language input expected. This helps the agent understand the scope and format of valid queries.

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 appropriate data source. It uses specific verbs like 'answer', 'pick', and 'routes', and lists many data sources, distinguishing it from sibling tools like search_datasets or get_dataset that require manual selection.

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 ('Use when a user asks...') and provides examples of query types. It also explains the benefit ('you don't want to figure out which Pipeworx pack/tool to call'). However, it does not include explicit exclusions or when-not-to-use advice.

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 fully covers behavioral traits: data sources (SEC EDGAR/XBRL for companies, FAERS/FDA/clinical trials for drugs), return type (paired data + citation URIs), and performance hint (replaces 8-15 calls).

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, front-loaded with main purpose, and every sentence adds essential information without redundancy.

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

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

Covers both entity types, return format, and data sources. No output schema, but mentions citation URIs. Lacks error handling details, but adequate for typical comparison use.

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

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

Schema coverage is 100%. Description adds value by explaining the distinction between company and drug types, providing examples for values, and clarifying expected input formats.

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 compares 2-5 companies or drugs side-by-side, with specific use cases and data sources. It distinguishes from sibling 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?

Explicit when-to-use triggers given (e.g., 'compare X and Y', 'X vs Y'). No explicit when-not-to-use, but 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 are provided, so the description carries the full burden. It states the output format (top-N tools with names and descriptions) but does not disclose behavioral traits such as rate limits, authentication requirements, or behavior with ambiguous queries. While it adequately describes the core behavior, it could be more transparent about operational details.

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 front-loaded with the core purpose: 'Find tools by describing the data or task.' It then provides detailed examples and usage guidance. While it is slightly verbose, each sentence adds value. It is well-structured and easy to parse.

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 and the presence of a comprehensive input schema, the description adequately covers the output (top-N tools with names and descriptions) and usage context. It does not have an output schema, but the description compensates by explaining what the tool returns. The information is sufficient 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?

The input schema has 100% coverage with descriptions for both parameters (query and limit). The description adds value by providing example queries (e.g., 'analyze housing market trends') and specifying the default and max for limit. This enriches the schema's documentation.

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 provides specific examples of data types (SEC filings, financials, etc.) and explains the output: 'Returns the top-N most relevant tools with names + descriptions.' This distinguishes it from sibling tools like search_datasets, which search for datasets specifically.

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: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear usage context and implies that other tools are for specific tasks. It also lists alternative tools implicitly by mentioning the types of data, guiding the agent to use this tool for discovery.

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 explains what data is returned (SEC filings, fundamentals, patents, news, LEI) and mentions citation URIs. However, it does not disclose data freshness, potential failure cases, or any rate limits, which would make it a 5.

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 dense paragraph but front-loads purpose and efficiently packs usage guidelines, content list, and parameter nuances. Every sentence adds value, though it could be split for readability.

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

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

Given the tool's complexity (aggregating multiple data sources), the description comprehensively covers what to expect (SEC filings, fundamentals, patents, news, LEI) and references sibling resolve_entity. No output schema is provided, but the description details return contents adequately.

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%, providing baseline 3. The description adds significant value by clarifying that type only supports 'company' and that value accepts ticker or zero-padded CIK (not names), with a fallback to resolve_entity. This goes beyond schema descriptions.

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

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

The description clearly states the verb 'get' and resource 'company profile', and explicitly lists the data domains covered (SEC filings, fundamentals, patents, news, LEI). It distinguishes from sibling tools like resolve_entity by specifying when not to use this tool.

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 scenarios (e.g., 'tell me about X', 'give me a profile') and instructs to use resolve_entity when only a name is available, citing the alternative. It clearly states when not to use the tool (for non-company entities, for names).

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 bears full burden. It clearly states the destructive action ('delete') but lacks details on behavior for missing keys or idempotency. However, for a simple delete 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 two concise sentences, front-loaded with the action and purpose, plus usage guidance. Every sentence adds value with no redundancy.

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

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

Given the tool's simplicity (single parameter, no output schema), the description covers what it does, when to use it, and related tools. It is sufficiently complete for an agent to use 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% and the parameter description 'Memory key to delete' matches the schema. The description adds no new meaning beyond the schema, so a baseline of 3 is appropriate.

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

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

The description explicitly states 'Delete a previously stored memory by key,' using a specific verb and resource. It distinguishes from siblings 'remember' and 'recall' by implying the delete action.

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 conditions: 'when context is stale, the task is done, or you want to clear sensitive data.' It also suggests pairing with siblings, offering clear context and 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 carries full responsibility for behavioral disclosure. It merely repeats the tool name without stating that the operation is read-only, whether authentication is needed, or what side effects exist. The word 'info' implies a read action but not explicitly.

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?

While the description is short, it is under-specified to the point of uselessness. It does not front-load critical information; it simply restates the tool name in a three-word phrase. True conciseness would preserve meaning, which is absent here.

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 tool with 2 parameters and no output schema, the description is grossly inadequate. It fails to explain what information is returned, how parameters affect the output, or any constraints. The agent cannot correctly invoke this tool based solely on the description.

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 0%, and the description adds no explanation for the parameters 'repo_id' and 'revision'. The agent receives no clues about their format, constraints, or usage, making the tool effectively opaque.

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 'Detailed dataset info' is vague and does not clearly specify the action or resource. It reads as a noun phrase rather than a verb-driven statement, and it fails to distinguish from siblings like 'get_model' or 'get_space' which likely retrieve similar info for other entities.

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

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

No guidance is provided on when to use this tool versus alternatives such as 'list_dataset_files' or 'search_datasets'. The agent receives no context about prerequisites or preferred scenarios.

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

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

With no annotations, the description must disclose key behaviors. It only lists returned data types but omits whether the operation is read-only, requires authentication, or error handling. This is insufficient for an agent to understand side effects or prerequisites.

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

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

The description is extremely concise—one line with no unnecessary words. While very short, it is not misleading and front-loads key information, though it could benefit from slightly more detail without losing conciseness.

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

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

For a simple tool with 2 parameters and no output schema, the description provides minimal but functional context. It fails to mention return format, common errors, or usage constraints, leaving gaps 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 input schema covers both parameters with descriptions, achieving 100% coverage. The description adds no extra semantic value beyond what the schema provides, resulting in a 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?

The description 'Detailed model info — config, tags, downloads, files at root' clearly identifies the tool's purpose of retrieving comprehensive model metadata. It distinguishes from siblings like search_models and list_model_files, but could be more precise about the 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 such as search_models or get_dataset. The description lacks context for appropriate usage scenarios.

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

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

With no annotations, the description carries full burden but only states 'Detailed Space info', omitting behavioral details such as error handling, permission needs, or what constitutes 'detailed'.

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

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

The description is extremely short (3 words) but sacrifices informativeness; it is under-specified rather than efficiently communicative.

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 2 parameters, no output schema, and no annotations, the description is grossly incomplete—missing return value info, error conditions, and relationship to sibling tools.

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

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

Schema coverage is 0%, meaning the description adds no meaning to the parameters. It does not explain repo_id or revision, leaving the agent without guidance on required inputs.

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 'Detailed Space info.' is vague; it does not specify what a 'Space' is (likely a Hugging Face Space) or differentiate from sibling tools like get_dataset or get_model.

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 usage guidelines are provided; the description does not indicate when to use this tool versus alternatives, nor any 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?

Annotations are empty, so the description must disclose behavioral traits. It only states a read operation ('List'), but lacks details on pagination, error behavior, permissions, or response format. This is minimal but not misleading.

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

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

The description is a single sentence with no wasted words. It is concise, though it could be structured with more detail for clarity.

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 3 parameters, no output schema, and no annotations, the description is too sparse. It doesn't explain how parameters like 'path' or 'revision' affect the listing, nor what the return looks like.

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 0% and the description adds no explanation for parameters like 'repo_id', 'path', or 'revision'. It offers no additional meaning beyond the parameter names.

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 ('List') and the resource ('files in a dataset repo'). This distinguishes it from sibling tools like 'list_model_files' (for models) and 'get_dataset' (for dataset metadata).

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

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

The description provides no guidance on when to use this tool versus alternatives like 'get_dataset' or 'list_model_files'. No when-not-to-use or contextual hints are 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?

No annotations provided; description misleadingly says 'at the root' while schema allows subdirectory filtering. Does not disclose pagination, error behavior, or authentication requirements.

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

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

Single short sentence, no wasted words, but could benefit from additional context while remaining concise.

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

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

Description does not cover the path parameter's ability to override root, and no output schema exists. Simple tool but incomplete for an AI agent to use 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 67% (2 of 3 parameters have descriptions). Description adds no parameter details beyond schema; missing repo_id description in both schema and 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?

Clearly states it lists files at the root of a model repo, but doesn't mention the optional 'path' parameter that allows listing subdirectories.

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 list_dataset_files or search_models. No when-not-to-use or alternative suggestions.

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 effectively communicates behavioral traits: it is a free, rate-limited (5 per identifier per day) feedback submission that does not count against tool-call quota. While it does not explicitly state 'no side effects' or 'safe', the context strongly implies it's a read-only submission. The description adds sufficient context for an agent to understand the tool's 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 concise and well-structured. It front-loads the core purpose, then covers usage guidelines, parameter hints, and constraints in a logical flow. Every sentence earns its place without unnecessary fluff, making it easy for an agent to parse quickly.

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, few parameters), the description is complete. It covers what the tool does, when to use it, how to fill in parameters, behavioral constraints (rate limit, free), and what happens to the feedback (team reads daily, affects roadmap). No gaps remain.

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

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

The input schema has 100% coverage, so baseline is 3. The description adds value by elaborating on the 'type' enum options with concrete examples (e.g., 'bug = something broke or returned wrong data') and gives guidance on the 'message' content (be specific, 1-2 sentences, 2000 chars max). 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 explicitly states the tool's purpose: to provide feedback to the Pipeworx team about bugs, missing features, data gaps, or praise. It clearly distinguishes itself from sibling tools like 'ask_pipeworx' or 'discover_tools' by specifying the use case of 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 provides explicit guidance on when to use the tool: when a tool returns wrong/stale data (bug), when a desired tool is missing (feature/data_gap), or when something works well (praise). It also advises on what not to include (end-user's prompt) and mentions rate limits and free usage, helping the agent decide appropriately.

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 full burden. It discloses the core behavior (retrieve, list, scope) and pairing, but lacks details on error handling (e.g., key not found) and response format, which are relevant for a retrieval tool.

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

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

The description is concise (three sentences), front-loaded with the main action, and every sentence adds value. There is no wasted text.

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

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

Given the simple one-parameter schema and no output schema, the description covers the main use cases, scoping, and tool relationships. It could mention response format or error handling for full completeness, but it is still effective.

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 only repeats what the schema already states (omit key to list). The description adds no additional meaning beyond the schema, so baseline 3 is appropriate.

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

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

The description uses a specific verb 'retrieve' and clearly identifies the resource (saved values) and the dual functionality: retrieving a specific key or listing all keys. It also distinguishes itself from sibling tools 'remember' and 'forget' by mentioning pairing.

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

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

The description explicitly states when to use the tool ('look up context stored earlier... without re-deriving it from scratch') and provides context on scoping and pairing with remember/forget. However, it does not explicitly state when not to use it or compare to unrelated siblings.

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 must disclose all behavioral traits. It explains the fan-out to three sources and the return structure (structured changes, total_changes count, URIs). However, it omits details like authentication requirements, rate limits, or whether the operation is read-only, which would be important 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 a single, moderately long paragraph that packs in purpose, usage, parameters, and return structure. It is efficient and front-loaded with purpose. It could be slightly better structured with bullet points or lists, but it avoids redundancy.

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

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

Given the tool is relatively simple (3 parameters, no output schema), the description adequately covers the return information (structured changes, total_changes count, URIs). However, it does not describe whether the response is paginated or the exact shape of 'structured changes'. For a critical monitoring tool, more precision could 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?

The input schema has 100% coverage, but the description adds value by specifying accepted formats for 'since' (ISO date or relative shorthand like '7d', '30d', '3m', '1y') and examples for 'value' (ticker or CIK). It also clarifies that 'type' only supports 'company'. This goes beyond the schema's definitions.

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's purpose: 'What's new with a company in the last N days/months?' and provides concrete example queries. It lists the specific data sources (SEC EDGAR, GDELT, USPTO), which distinguishes it from sibling tools that focus on single entities or comparisons.

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

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

The description clearly indicates when to use the tool by listing user queries ('what's happening with X?', 'any updates on Y?', etc.) and provides usage context for the 'since' parameter with examples. However, it does not explicitly state when not to use it or compare it to other tools in the sibling list, which would raise the score to 5.

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 key behavioral traits: persistence varies by authentication (persistent for authenticated, 24-hour retention for anonymous), scoping by identifier, and storage as key-value pairs. No annotations present, description covers all.

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

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

Concise multi-sentence description front-loads the core purpose. Every sentence adds value (use cases, scoping, retention, companion tools). No filler.

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

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

For a simple key-value store, the description covers purpose, usage, behavioral notes, and integration with siblings. No output schema needed; the tool's effect is straightforward.

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%; description adds example values for keys (e.g., 'subject_property') but does not significantly extend semantic meaning 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 explicitly states it saves data for reuse, with specific use cases like resolved tickers, addresses, etc. It clearly distinguishes from siblings 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?

Provides explicit when-to-use guidance ('when you discover something worth carrying forward') and mentions companion tools (recall, forget) for complementary actions.

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 clearly states the tool returns IDs plus pipeworx:// citation URIs and gives examples. It implies a read-only lookup, though it could explicitly state it does not modify data. Overall, it provides sufficient behavioral 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 concise (4 sentences) and front-loaded with the main purpose. Every sentence adds value, no redundancy. 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 (2 parameters, no nested objects, no output schema), the description is complete. It covers purpose, when to use, parameters with examples, and return format (IDs plus URIs). No gaps identified.

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 valuable context beyond the schema, such as explaining the `value` parameter format for both entity types and providing concrete examples like 'Apple' → AAPL. This extra detail improves understanding.

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

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

The description uses specific verbs ('Look up the canonical/official identifier') and clearly states it resolves entities to identifiers like CIK, ticker, RxCUI, LEI. It distinguishes from siblings by indicating it should be used before other tools needing official 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?

The description explicitly says when to use ('when a user mentions a name and you need the ... ID systems that other tools require as input') and provides examples. It also states 'Use this BEFORE calling other tools that need official identifiers' and implies it replaces multiple lookups, though it does not explicitly name alternatives.

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

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

With no annotations provided, the description must carry the full burden of behavioral disclosure. It only implies a read operation via 'browse/search' but does not explicitly state read-only behavior, rate limits, pagination, or any side effects. This is insufficient for an eight-parameter tool.

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

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

The description is very concise (one phrase). However, conciseness here equates to underspecification. The structure is front-loaded with a verb, but it does not earn its place by providing essential information.

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

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

Given the complexity of eight parameters, no output schema, and no annotations, the description is severely incomplete. It provides no information on return values, filtering behavior, sorting options, or how parameters interact. The agent would have no context to 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 description coverage is only 13% (only task_categories has a brief description). The tool description does not add any meaning to the parameters beyond their names. For a tool with low schema coverage, the description should compensate but fails to do so.

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 as browsing or searching datasets on the Hub. It identifies the resource (datasets) but does not differentiate from sibling search tools like search_models or search_spaces, which also have similar phrasing.

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 usage guidelines are provided. The description lacks any context on when to use this tool versus alternatives such as search_models or get_dataset. It does not mention exclusions or prerequisites.

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 fully disclose behavior. Only states 'Browse / search' without mentioning return format, pagination, or that it is read-only.

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?

Very short (4 words) and front-loaded. However, it lacks substance; could be more informative without losing conciseness.

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

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

Despite 10 well-documented parameters and no output schema, the description fails to summarize return behavior or use cases. Incomplete for a complex search 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?

Input schema has 100% description coverage, so baseline is 3. Description adds no additional 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?

Description clearly states 'Browse / search models on the Hub.' Uses specific verb+resource and distinguishes from sibling tools like search_datasets and search_spaces which target different resources.

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. Does not mention that it is for models only or compare with search_datasets/search_spaces.

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

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

No annotations are provided, and the description lacks any disclosure of behavioral traits such as pagination, sorting behavior, or the effect of the 'full' parameter. The description adds minimal value beyond the tool name.

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

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

The description is very concise but under-specified. It is not wasteful but fails to provide enough information, making it minimally viable. A sentence explaining key parameters would improve it without losing conciseness.

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

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

Given the tool has 7 parameters, no output schema, and no annotations, the description is incomplete. It does not explain return values, pagination, or how to effectively search. Sibling tools are not differentiated.

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 only 14%, with only the 'sdk' parameter having a description. The description does not explain other parameters like 'full', 'sort', 'limit', 'author', 'search', 'direction', leaving their semantics unclear despite the tool's purpose being browsing/searching.

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 'Browse / search Spaces (demo apps)' clearly states the action (browse/search) and the resource (Spaces as demo apps). This distinguishes it from sibling tools like search_datasets and search_models.

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

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

The description provides no guidance on when to use this tool versus alternatives like search_datasets, search_models, or get_space. No context on filtering or prerequisites.

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 the description must entirely disclose behavioral traits. It fails to mention that the operation is read-only, how 'trending' is defined, or any other side effects. The minimal description provides no insight into tool behavior beyond a vague result type.

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

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

The description is very concise at a single phrase, but it sacrifices necessary detail. It is not verbose, but being too concise reduces its utility. There is no structure or front-loading of 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?

Given the tool's simplicity (one param, no output schema, no annotations), the description should at least explain what 'trending' means. It does not, leaving ambiguity about the data source or criteria. The description is incomplete for its 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 description coverage is 100% for the single parameter 'limit' (1-100, default 20). The description adds no extra meaning beyond the schema, which is adequate for a simple parameter 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 'Currently-trending datasets' indicates the tool returns trending datasets, but it lacks a verb and does not specify an action like 'list' or 'retrieve'. It is not a tautology, but it does not clearly distinguish itself from siblings like 'search_datasets' or 'get_dataset' in terms of purpose.

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

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

No guidance is provided on when to use this tool versus alternatives such as 'search_datasets' or 'trending_models'. The description does not mention any context, exclusions, or prerequisites for usage.

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

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

No annotations provided, so description bears full burden. It only states 'currently-trending models' without defining 'trending', output structure, or read-only nature. Important behavioral context missing.

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 sentence, no wasted words. Could be slightly improved by front-loading key action, but efficient overall.

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?

Simple tool with one parameter and no output schema. Description provides basic purpose but lacks details like trend definition and result format. Adequate but not comprehensive.

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 for 'limit'. The description adds no additional parameter meaning beyond schema. Baseline 3 is appropriate.

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

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

The description clearly states it returns 'currently-trending models on the Hub.' It uses specific verb+resource and distinguishes from siblings like 'trending_datasets' and 'get_model'.

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?

Usage is implied but not explicit. No guidance on when to use this vs alternatives like search_models or trending_datasets. No exclusions or when-not-to-use.

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

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

No annotations are provided, so the description carries full burden. It discloses domain constraints (company-financial, public US companies), verdict types, return format (structured form, actual value with citation, percent delta), and that it replaces multiple steps. This is thorough, though it could mention potential false negatives from domain mismatch.

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 efficiently cover purpose, when to use, and return details. Every sentence adds value with no repetition or filler. Front-loaded with 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?

Despite no output schema, the description fully explains what the tool returns (verdict, structured form, actual value, citation, percent delta). It also specifies the domain (company-financial claims) and data source (SEC EDGAR+XBRL). For a single-parameter tool, this is complete.

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

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

The single parameter 'claim' has a schema description that already includes examples. The description adds domain-specific context ('company-financial claims') and the v1 limitation, which is valuable beyond the schema. With 100% schema coverage, the description adds meaningful extra context.

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 uses specific verbs (fact-check, verify, validate, confirm/refute) and clearly states the resource (natural-language factual claim) and scope (company-financial claims via SEC EDGAR+XBRL). It distinguishes itself from sibling tools by focusing on NL claim verification, which no other sibling does.

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 this tool (checking truth of user statements) with example queries. It implies limitations (v1 supports only company-financial claims) and notes it replaces multiple sequential calls, but does not explicitly mention when not to use or name alternatives.

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