asktian

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 explains the output (top 3 best windows and windows to avoid) but does not disclose any behavioral traits like data persistence, authentication needs, or side effects. More transparency would be beneficial.

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 at two sentences, front-loading the core purpose and immediately following with usage examples. Every sentence adds value without redundancy.

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

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

Given the tool has 3 parameters, no output schema, and no annotations, the description generally covers purpose, usage, and output format. However, it lacks details about the exact return structure or potential constraints beyond the schema, which could be improved.

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%, with each parameter already described in the input schema. The description adds no additional semantic value beyond the schema, so 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 tool finds auspicious time windows for a specific action, with concrete examples like 'when should I do X' and 'should I move this meeting'. It differentiates from sibling tools by emphasizing its unique function for real-time scheduling advice.

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 example queries and states it returns top 3 best windows and windows to avoid. However, it does not explicitly mention when not to use this tool compared to siblings, though the context implies it.

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. It discloses that the numeric score is hidden if below 60 to avoid negative perceptions, and that a qualitative label is returned first. This is a critical behavioral trait beyond the schema. However, it does not specify if the operation is read-only or describe all output 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 two sentences, each earning its place. The first sentence introduces the core function and method; the second explains the output behavior and rationale. It is front-loaded with the main purpose and avoids extraneous detail.

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 (Chinese metaphysics, conditional score hiding) and lack of output schema, the description adequately explains what the tool does and how the result is presented. It could mention that the calculation is based on birthdates, but the schema already provides that. It is complete enough for an agent to understand when to invoke and what to expect.

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 baseline is 3. The description adds context about the function's purpose and output behavior but does not elaborate on how the 'dimension' parameter affects the calculation or how birthdates are used. It provides marginal added value beyond the schema.

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

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

The description clearly states the tool computes fate compatibility using Chinese metaphysics (八字 pairing, 5-element generation/clash). It specifies the resource (compatibility between two people) and the verb (compute). This distinguishes it from siblings like asktian_name_analysis or asktian_daily_reading, which address different domains.

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

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

The description explicitly says 'Useful when user asks about compatibility, fit, or 'will this person and I work'', providing clear usage triggers. While it does not list alternatives or when-not-to-use, the sibling context provides contrast, and the targeting is specific enough for an agent to select correctly.

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 fully describes the return content: archetype, energy, favorable colors/direction/hours, headline advice, caution. This gives a clear behavioral picture. It does not mention authentication or rate limits, but the tool is a read-only reading 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?

Two sentences with no waste. The first sentence states the purpose and output, the second provides usage guidelines. Front-loaded with 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 has no output schema and no annotations, the description provides a clear overview of output and usage. It could mention that the output is structured, but it is sufficient for selection and invocation.

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

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

Schema coverage is 100%, so baseline is 3. The description does not add new meaning beyond the schema; it only reiterates the purpose. The parameters (birthdate, gender, birth_hour) are adequately described in the schema, and the description does not enhance them.

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 the resource 'today's personalized Chinese metaphysics reading for a person'. It specifies the components (bazi, daily energy) and distinguishes from siblings like 'asktian_today_energy' by emphasizing personalization.

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 provides use cases: 'when the user asks how today will be for them, what colors to wear, where to face their desk, or for general daily guidance.' It does not explicitly mention when not to use or alternatives, but the context from sibling names implies 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 provided, so description carries full burden. It discloses the return components (yes/no/neutral, confidence, reasoning, disclaimer) and emphasizes entertainment and non-financial advice. It does not mention permissions or side effects, but as a read operation it 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 concise, front-loads the purpose, and each sentence adds value. No redundant or filler 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?

For a simple tool with 3 params and no output schema, the description covers purpose, usage, return components, and disclaimer. It is sufficient for an agent to understand and 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?

Schema coverage is 100%, so parameters are already documented. The description adds meaning: explains the question format, defaults for resolve_date, and the special use of subject_birthdate for person-specific markets.

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 gets a Chinese-metaphysics signal for binary prediction-market questions, with specific examples (Polymarket/Kalshi). It distinguishes from siblings which cover different functions like best time, compatibility, etc.

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 it (for an uncorrelated, for-fun read on a market) and provides the question format. It does not explicitly mention when not to use or list alternatives, but the usage context is clear.

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

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

No annotations provided, so description carries full burden. It discloses output format ('one-line vibe + dominant element guess') and the nature as a 'quick' and 'guess', indicating approximate results. However, it does not detail underlying methodology or limitations.

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

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

Two sentences, no redundancy. Front-loaded with action and context. Every sentence adds value.

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

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

Given the tool's simplicity, the description covers purpose, usage, and output sufficiently. No output schema exists, but description explains return format. No missing context for an AI agent to select and invoke correctly.

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

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

Schema coverage is 100%, so baseline is 3. Description does not add additional meaning beyond what the schema already provides for 'name' and 'language' parameters.

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: a quick energetic profile of a name. It explicitly lists use cases (baby name, company name, 'what kind of person is X' without birthdate) and distinguishes it from siblings which focus on compatibility, daily readings, and market reads.

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 user asks about a name without birthdate. Also implies when not to use (if birthdate available, may need other tools). No explicit sibling name, but the context is clear.

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

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

No annotations provided. Description discloses it returns 干支 and dominant element, but does not elaborate on side effects, permissions, or constraints. As a read-only query, this is acceptable but could be more explicit about what is not returned (e.g., no personalized analysis).

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

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

Two concise sentences, front-loaded with core functionality and followed by usage guidance. No redundant words, every sentence adds value.

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

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

Given no output schema, the description adequately mentions return values (干支 and dominant element). It could specify the format, but for a simple tool with one optional parameter, it provides sufficient context 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 coverage is 100%, with one optional parameter described sufficiently in the schema (ISO YYYY-MM-DD, defaults today UTC). The tool description does not add additional meaning beyond the schema, meeting but not exceeding the baseline.

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

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

The description clearly states the tool retrieves today's general energy, specifying the 干支 (stem+branch) and dominant 5-element character. It distinguishes from sibling tools by noting no person/birthdate needed, making its purpose unique and specific.

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

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

Explicitly provides use cases: user asking 'what kind of day is it', 'what's the energy today', or AI adding cosmic context without birthdate. Lacks explicit 'when not to use', but context strongly implies it's for general rather than personalized readings, which differentiates from siblings.

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