Kairos Signal — Neural ODE DAG + Data Products

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

No annotations are provided, so the description carries full burden. It only states the basic functionality without disclosing behavioral traits such as return format, rate limits, authorization requirements, or side effects. The schema adds details but the description itself is too minimal.

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 short (one sentence), which is concise but sacrifices necessary detail. It is front-loaded but under-informative. A balanced score reflects adequate brevity without sufficient content.

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

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

Given no output schema, no annotations, and sibling tools with varied purposes, the description is incomplete. It lacks information about return values, error conditions, pagination details, or usage examples that would help the agent correctly 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?

Schema coverage is 100%, so the description's addition of 'with limit/offset' is redundant. It does not explain parameter relationships or usage beyond what the schema already provides. A neutral score is appropriate as the description adds little 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?

The description clearly states the action ('Query records') and the resource ('dataset'), with explicit parameters ('limit/offset'). This effectively distinguishes it from sibling tools like 'get_stats' and 'list_datasets' which serve different purposes.

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. The description does not specify context, prerequisites, or when to avoid it. With several sibling tools, the lack of usage direction is a significant gap.

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

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

No annotations exist, so the description carries full burden. Only states 'get', implying read-only, but offers no details on auth requirements, cost, or side effects. Minimal disclosure for a tool with no safety 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?

Single, short sentence is concise and front-loaded. However, it sacrifices potentially useful details like the nature of statistics or scope of databases.

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 parameters, output schema, or annotations, and sibling tools that are more specific, the description is too sparse. It fails to clarify what 'aggregate statistics' means or how results are structured, leaving the agent underinformed.

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 zero parameters, and schema description coverage is 100%. With no params to document, baseline is 4; description adds nothing beyond schema but is not required to.

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

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

Specifies verb 'get' and resource 'aggregate statistics from the databases', clearly indicating a read operation. However, it does not differentiate from siblings like fetch_dataset or list_datasets, and the term 'aggregate statistics' remains vague.

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 no guidance on when to use this tool versus alternatives. Lacks context on prerequisites, typical scenarios, or exclusions, leaving the agent to infer applicability.

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, and the description only states the basic action. Lacks disclosure of behavioral traits such as idempotency, sensitivity of proof data, or performance implications.

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 that is efficient and front-loaded. Could be expanded slightly, but no extraneous content.

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

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

With only one parameter and no output schema, the description is adequate for basic understanding. However, it omits context about the proof format or any constraints, which would be 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?

Schema coverage is 100% for the single required parameter 'lead_id'. The description adds no additional meaning beyond what the schema already 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?

Clearly states the tool returns a zero-knowledge provenance proof for a lead ID. This is distinct from sibling tools like fetch_dataset or verify_footprint.

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 provided. Does not specify 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?

No annotations are provided, so the description carries the full burden. It declares a read operation ('List') and mentions output contents, which is transparent for this simple tool. However, it does not disclose potential limits (e.g., pagination, rate limits) or performance characteristics, though the zero-parameter nature reduces the need for such detail.

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 front-loads the main action and output. Every word is earned, and there is no extraneous 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 no output schema, the description could elaborate on the structure of the return value (e.g., list of objects with fields like 'name', 'record_count', 'description'). The current description is minimal and adequate but leaves room for interpretation about the exact 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 input schema has zero parameters, and schema description coverage is 100% (vacuous). The description adds meaning beyond the schema by specifying 'all available datasets' and the output fields. For a no-parameter tool, a baseline of 4 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 'List all available datasets with record counts and descriptions' clearly states the verb (List), resource (all available datasets), and key output details (record counts, descriptions). It distinguishes from siblings like fetch_dataset (which likely fetches a specific dataset) and get_stats (which might be more granular).

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 this tool: when you need a list of all datasets. However, it does not explicitly state when not to use it or mention alternatives among the sibling tools. The context is clear but lacks exclusionary guidance.

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

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

No annotations exist, so the description must carry behavioral transparency. It states 'create a Stripe checkout session,' which implies a write operation, but lacks details on side effects (e.g., does it charge the user immediately?), rate limits, or required permissions. The behavioral intent is clear but not fully 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 a single, efficient sentence that immediately conveys the tool's purpose. No unnecessary words. However, it is quite minimal; slightly more structure (e.g., breaking out key constraints) could improve clarity without adding bulk.

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

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

Given no output schema and three parameters (all well-documented in schema), the description provides the high-level action. However, it omits the return value (e.g., a session ID or URL), which is crucial for an AI agent to proceed. For a transaction tool, this gap reduces 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 schema already documents all parameters. The description provides general context ('data purchase') but adds no specific semantics beyond what's in the schema. Baseline score 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 clearly states the action ('Create a Stripe checkout session') and the context ('for data purchase'), which is a specific verb-resource pair. It effectively distinguishes this tool from sibling tools like fetch_dataset or list_datasets, which are read-oriented.

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

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

The description implies when to use (to initiate a data purchase) but provides no explicit guidance on when not to use, prerequisites, or alternatives. For example, it doesn't mention that users should already have selected a dataset or that this tool requires authentication.

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 present, so the description must fully convey behavioral traits. It states the tool returns a verification, but does not disclose whether it is read-only, requires authentication, or has side effects. The behavior beyond the return value is unclear.

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 that efficiently communicates the core function without unnecessary words. It is appropriately sized for a simple tool but lacks structure like bullets or sections.

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 of the tool (2 parameters, no nested objects, no output schema), the description adequately identifies the purpose and return type. However, it omits details about the verification format (e.g., hex string) and does not explain what 'verify' means in context, leaving some ambiguity.

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 provides 100% coverage with clear descriptions for both parameters ('Dataset name', 'Record ID to verify'). The tool description adds the context of 'SHA-256 cryptographic verification' but does not enhance the meaning beyond what the schema already provides. 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 the tool returns SHA-256 cryptographic verification for a dataset or record, specifying the verb and resource. However, it does not differentiate from the sibling tool 'get_zk_provenance', which likely provides a similar verification function.

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

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

No guidance is provided on when to use this tool versus alternatives like 'get_zk_provenance' or 'fetch_dataset'. There is no mention of prerequisites, context, or conditions for use.

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