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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool is 'Essential for provenance and fact-checking,' which hints at its role in verifying information, but doesn't describe key behaviors such as authentication needs (implied by 'userId' parameter), rate limits, response format, or whether it's a read-only operation. This leaves significant gaps in understanding how the tool behaves in practice.

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

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

The description is concise and front-loaded, consisting of two sentences that directly state the tool's purpose and importance. There's no unnecessary information, and each sentence contributes value by explaining what the tool does and its context. However, it could be slightly more structured by explicitly separating usage guidance from 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?

Given the tool has an output schema (which covers return values) and high input schema coverage, the description provides a basic but incomplete context. It explains the purpose and importance but lacks details on behavioral traits, usage guidelines, and how it integrates with sibling tools. For a tool with 4 parameters and no annotations, this leaves gaps in overall completeness, though the structured data mitigates some issues.

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

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

The input schema has 100% description coverage, clearly documenting all four parameters. The description adds some context by mentioning 'for a specific node,' which aligns with the 'for_node_id' parameter, but doesn't provide additional semantic details beyond what the schema already covers. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't significantly enhance parameter understanding.

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

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

The description clearly states the tool's purpose: 'Get source signals, articles, and evidentiary depth for a specific node.' It specifies the verb ('Get') and resources ('source signals, articles, evidentiary depth'), and mentions the context ('provenance and fact-checking'). However, it doesn't explicitly differentiate this from sibling tools like 'get_node' or 'get_neighbors', which might also retrieve node-related information.

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 minimal usage guidance: it implies this tool is used for 'provenance and fact-checking' of a node, but doesn't specify when to use it versus alternatives like 'get_node' or 'search_graph'. There's no explicit mention of prerequisites, exclusions, or comparative contexts with sibling tools, leaving the agent to infer 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?

No annotations are provided, so the description carries the full burden. It implies a read-only operation ('discover') and hints at use cases like UI filters, but doesn't disclose behavioral traits such as authentication needs (though 'userId' is required), rate limits, or what happens if invalid inputs are provided. It adds some context but lacks depth for a tool with no annotations.

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

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

The description is concise and well-structured in two sentences: the first states the purpose with examples, and the second provides usage guidelines. Every sentence adds value without redundancy, making it efficient 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 moderate complexity (3 required parameters, no annotations, but with an output schema), the description is reasonably complete. It covers purpose and usage, and since an output schema exists, it doesn't need to explain return values. However, it could improve by addressing authentication or error handling, given the lack of annotations.

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

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

The input schema has 100% description coverage, with clear parameter descriptions (e.g., 'The graph ID', 'The label to fetch values for'). The description adds no additional parameter semantics beyond what the schema provides, such as explaining label examples further or clarifying user ID requirements. Baseline 3 is appropriate given high schema coverage.

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

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

The description clearly states the tool's purpose: 'Discover valid values for a specific node label' with examples like 'RetailerType, Technology'. It specifies the action (discover) and resource (node label values), but doesn't explicitly differentiate from sibling tools like 'get_node' or 'search_graph' that might also retrieve node-related information.

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

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

The description provides clear usage context: 'Use for discovery, UI filters, and category exploration.' This gives practical scenarios for when to use the tool. However, it doesn't explicitly state when NOT to use it or name alternatives among the sibling tools, such as when to prefer 'get_node' for node details instead.

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 of behavioral disclosure. It mentions 'depth-first discovery,' which hints at the traversal algorithm, but doesn't cover critical aspects like whether this is a read-only operation, potential performance impacts, rate limits, authentication needs (implied by userId parameter), or what the output contains. For a graph traversal tool with 6 parameters, this leaves significant behavioral gaps.

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

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

The description is highly concise and front-loaded: two sentences that directly state the purpose and usage context without any fluff. Every word earns its place, making it easy for an AI agent to parse quickly. It efficiently communicates the core functionality in a structured manner.

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 a graph traversal tool with 6 parameters, no annotations, but an output schema (which handles return values), the description is minimally adequate. It covers the basic purpose and hints at usage but lacks details on behavioral traits, error handling, or integration with sibling tools. The presence of an output schema reduces the need to explain returns, but more context on operational aspects would improve completeness.

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

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

Schema description coverage is 100%, so the input schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by implying traversal from 'seed nodes' and 'depth-first' approach, but it doesn't provide additional semantics like examples or edge cases. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't detract either.

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

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

The description clearly states the tool's purpose: 'Traverse the graph from seed nodes to find related concepts and relationships.' It specifies the verb ('traverse') and resource ('graph'), though it doesn't explicitly differentiate from sibling tools like 'search_graph' or 'get_node', which might also involve graph operations. The phrase 'depth-first discovery' adds specificity about the traversal approach.

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 implied usage guidance with 'Useful for depth-first discovery,' suggesting it's for exploration rather than targeted queries. However, it doesn't explicitly state when to use this tool versus alternatives like 'search_graph' or 'get_node,' nor does it mention prerequisites or exclusions. The guidance is helpful but lacks direct sibling differentiation.

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 of behavioral disclosure. It states the tool retrieves metadata and properties, implying a read-only operation, but doesn't cover aspects like authentication needs (e.g., based on userId), rate limits, error handling, or what specific metadata/properties are returned. This leaves gaps in understanding the tool's behavior beyond basic functionality.

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 that directly states the tool's purpose without unnecessary words. It is front-loaded with the key action and resource, making it easy to parse quickly. There is no wasted information, earning a high score for 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 an output schema, the description doesn't need to explain return values. However, with no annotations and a read-focused tool, the description adequately covers the basic operation but lacks details on behavioral traits like permissions or limits. It's minimally viable for a retrieval tool but could be more complete by addressing usage context or behavioral nuances.

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

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

The input schema has 100% description coverage, with clear documentation for all three parameters (graphId, nodeId, userId). The description adds no additional meaning beyond this, as it doesn't explain parameter interactions, formats, or examples. Given the high schema coverage, the baseline score of 3 is appropriate, as the schema adequately handles parameter semantics without extra description.

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

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

The description clearly states the action ('Directly retrieve') and resource ('metadata and properties for a single node by its ID'), making the purpose specific and understandable. However, it doesn't explicitly differentiate this tool from its siblings like 'get_neighbors' or 'search_graph', which might also retrieve node-related information but with different scopes or methods.

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. It mentions retrieving a single node by ID, but doesn't specify scenarios where this is preferred over sibling tools like 'get_neighbors' (for related nodes) or 'search_graph' (for broader queries), leaving the agent without explicit usage context.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns 'up to 3 meta_patterns' and has a parameter constraint ('At least one of 'industry' or 'sector' must be provided'), which adds useful behavioral context. However, it doesn't mention other important traits like whether this is a read/write operation, authentication needs, rate limits, or error conditions.

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 and well-structured in just three sentences. Each sentence earns its place: the first states the core purpose, the second specifies output and use case, and the third provides a critical parameter constraint. There's zero wasted language and it's front-loaded with the most important 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 that there's an output schema (which handles return values), no annotations, and 100% schema coverage for parameters, the description provides adequate context for this tool's complexity. It covers the purpose, output scope, use case, and a key parameter constraint. The main gap is lack of behavioral details about the operation's nature (read/write, side effects, etc.) which would be important without annotations.

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

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

Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description adds minimal value beyond the schema by emphasizing that 'At least one of 'industry' or 'sector' must be provided' (a constraint not in the schema's required field). This provides some additional semantic context, but most parameter meaning comes from the schema.

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

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

The description clearly states the tool's purpose: 'Get a structured macro overview from the PSFK Graph' with specific output ('up to 3 meta_patterns') and context ('useful for top-level briefings before deeper exploration'). It uses a specific verb ('Get') and resource ('structured macro overview'), but doesn't explicitly differentiate from sibling tools like 'get_node' or 'search_graph' beyond mentioning it's for 'top-level briefings'.

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 some usage context: 'Useful for top-level briefings before deeper exploration' implies this tool should be used for initial exploration rather than detailed queries. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_node' or 'search_graph', nor does it provide clear exclusions or prerequisites beyond the parameter requirement.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: the hybrid search approach, the 3-tier fallback (vector → keyword → all trends), and the guarantee to 'Always returns results'. This covers search methodology and reliability, though it omits details like rate limits, authentication needs, or error handling, which are relevant for a search 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 front-loaded with the core purpose in the first sentence, followed by additional behavioral details in a concise manner. Every sentence earns its place by explaining the search type, return content, fallback mechanism, and reliability guarantee without redundancy or unnecessary elaboration.

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

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

Given the tool's complexity (5 parameters, hybrid search) and the presence of an output schema (which covers return values), the description is largely complete. It explains the search behavior and fallback, but could improve by addressing potential limitations or how it differs from sibling tools. The lack of annotations is compensated by the description's behavioral details, making it adequate for agent 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 description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'trends and articles' as return types, which hints at the output but doesn't elaborate on parameter interactions or usage nuances. Baseline 3 is appropriate as the schema handles most of the parameter semantics.

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

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

The description clearly states the tool's purpose with specific verbs ('perform hybrid search') and resources ('Fodda knowledge graph'), and distinguishes it from siblings by specifying it returns 'trends and articles matching the query' rather than individual nodes or evidence. It explicitly mentions the 3-tier fallback mechanism, which further clarifies its unique functionality.

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

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

The description implies usage through the mention of 'hybrid (vector + keyword) search' and the fallback mechanism, suggesting it's for retrieving content based on queries. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_node' or 'get_evidence', and does not specify prerequisites or exclusions, leaving the agent to infer context from the tool name and parameters.

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