Mythsensus

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

No annotations provided, so description carries full burden. It discloses that the tool returns metadata (architecture, limitations, roadmap, links) and emphasizes 'engineering-honest'. It does not mention side effects or required permissions, but for a read-only information 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?

Two sentences with zero wasted words. Key information is front-loaded: 'Engineering-honest metadata about the Mythsensus engine'. Each sentence serves a 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 no parameters and no output schema, the description covers the purpose, content areas, and usage cue. It is complete enough for an agent to determine when to call it. Could mention return format, but not required.

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?

No parameters (baseline 4). The description correctly implies no input needed.

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 provides 'Engineering-honest metadata about the Mythsensus engine' covering architecture, limitations, roadmap, and links. This distinguishes it from sibling tools like get_deity_lore or calculate_cosmic_score.

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 specifies when to use: when a user asks 'is this real?' or 'how accurate is it?'. While it doesn't mention alternatives, the context implies it's for meta-discussions. No explicit when-not-to-use, but the guidance 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 are provided, so the description carries the full burden. It discloses determinism and lists output components, but lacks details on potential errors or performance. Nonetheless, it is transparent about 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 two sentences, front-loaded with the core purpose, and includes essential details without waste. Every sentence earns its place.

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 9 parameters with full schema descriptions and no output schema, the description sufficiently explains the output (score, tier, percentile, consensus preview). It is complete for a computational 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 description coverage is 100%, so the baseline is 3. The description does not add per-parameter details beyond what the schema already provides, but it contextualizes the overall input (birth date with optional fields).

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 computes a specific score (Cosmic Score) for a birth date using 26 systems. It clearly distinguishes from siblings like 'get_deep_reading' and 'daily_blessing'.

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?

While it doesn't explicitly contrast with siblings, the description's mention of 'deterministic' and the listing of systems implies when to use (for a general score). The sibling names provide enough context for 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?

With no annotations, the description carries the full burden. It discloses determinism and bias from Cosmic Score, but does not mention authentication, side effects, or whether the tool modifies any state. It states returns but not pagination or error behavior. Adequate but not thorough.

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 brief (two sentences) and front-loaded with purpose. However, the second sentence introduces confusion about birth date vs. date parameter, reducing effectiveness. Every sentence should add value without ambiguity.

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 4 parameters, no output schema, and no annotations, the description should provide more context. It lacks explanation of how the date parameter relates to birth date, what 'Cosmic Score tiers' means, and details about the return format beyond a list of fields. The description feels incomplete.

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 low (25%, only date has description). The description mentions 'birth date' and 'current date' but the input parameters (year, month, day) do not clarify which is which. This creates confusion rather than adding meaning. The description does not adequately compensate for the low 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 what the tool does: 'Draw today's deity card' from a specific collection. The verb and resource are specific, and the context of determinism and bias adds clarity. However, it does not explicitly differentiate from sibling tools like 'get_deity_lore' or 'get_deep_reading', though the purpose is distinct enough.

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 for drawing a daily card based on dates and Cosmic Score bias, but it does not provide explicit guidance on when to use this tool versus alternatives (e.g., when to use calculate_cosmic_score instead). No when-not or comparison context is 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 exist, so the description must disclose behavioral traits. It mentions reading one system and pricing tiers but omits critical details: whether optional parameters are used, what errors occur for invalid systems or free tier restrictions, and the output format.

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 short and front-loads the core purpose, but it omits important details. While concise, it sacrifices completeness needed for a 10-parameter tool.

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 10 parameters, no output schema, and low schema coverage, the description fails to provide adequate context. It lacks parameter explanations, output description, error handling, and usage scenarios.

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 10%, and the tool description adds no semantics beyond naming 'system'. The required date parameters are not explained (e.g., birth date), and optional location/time parameters lack 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 clearly states the tool gets a focused reading for one specific divination system from a set of 26. It distinguishes from siblings like list_26_systems by focusing on a single system's reading. However, it does not elaborate on what constitutes a 'deep reading', leaving some ambiguity.

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 guidance on free vs paid systems but does not explicitly state when to use this tool over siblings like get_system_rules or daily_blessing. The pricing note is helpful but not a full usage guideline.

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 partial matches return candidates, and the output includes origin tradition, rarity tier, and full lore in English and Thai. This is adequate for a read-only lookup, though it doesn't mention any potential limitations 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 concise, using two sentences to convey scope, examples, and usage triggers. Every sentence adds value with no redundancy. The front-loaded examples clarify the tool's domain immediately.

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 explains return contents (origin tradition, rarity tier, full lore). It covers the required parameter and optional behavior. It could be more complete by elaborating on what 'rarity tier' means, but it is sufficient for a lore lookup 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 schema already describes both parameters. The description adds value by explaining that 'deity' accepts partial matches returning candidates and that omitting 'lang' returns both languages. This extra context helps agents use the parameters correctly.

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 specifies the tool looks up encyclopedic profiles of deities across 9 mythologies, listing examples like Ganesha, Zeus, Odin. It clearly states what it returns (origin tradition, rarity tier, full lore), distinguishing it from sibling tools like 'daily_blessing' or 'calculate_cosmic_score' which have 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?

The description provides explicit query patterns ('who is X?', 'tell me about the god/goddess X') and states 'Use whenever a user asks...'. It gives clear context but does not explicitly mention when not to use this tool or suggest alternatives, missing some 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?

With no annotations, the description carries full burden. It discloses typo-tolerant parameter, authoritative nature, and the distinction between system-specific rules and consensus methodology. Does not mention side effects, but given read-only nature, it's 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?

Three sentences, front-loaded with core purpose. Each sentence adds value, though could be slightly tighter. No filler, appropriate length for the information conveyed.

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 explains both modes thoroughly: providing system returns that system's ruleset, omitting returns consensus methodology and overview. Covers main use cases, though lacks details on error handling or pagination (likely irrelevant).

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 has 100% coverage for the single optional parameter. Description adds meaning: 'typo-tolerant' and explains the different result when omitted (consensus methodology + overview), which is beyond the schema's 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 tool returns 'Mythsensus's canonical interpretation rules' and distinguishes it from siblings like list_26_systems by detailing that it provides the reference methodology for reading systems and forming consensus, not just a list.

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

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

Explicitly states when to use: 'to GROUND a divination/astrology answer in Mythsensus's framework instead of improvising'. Also explains two modes (with or without system parameter) but does not explicitly exclude alternatives among 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?

No annotations are provided, so the description carries full burden. It states the tool returns a list, but does not disclose any additional behavioral traits such as idempotency, pagination, or stability of the list. For a simple read-only tool with no parameters, the description is adequate, but could mention that the list is static or canonical.

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 one sentence plus a usage hint, very concise and front-loaded. Every part serves a purpose: what the tool returns and when to use it. No wasted words.

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

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

The tool has no parameters and no output schema, but the description specifies the content of the list (slug, English+Thai name, region, required inputs). For a simple list tool, this is complete enough for an agent to understand the return value and use case.

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 tool has 0 parameters and schema description coverage is 100%. According to guidelines, baseline is 4 for 0 parameters. No parameter details are needed, so scoring 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 a canonical list of 26 ancient divination systems with specific fields (slug, English+Thai name, region, required inputs), and explicitly ties it to a common user query: 'Use first when asked "what systems do you support?"'. This distinguishes it from sibling tools like get_system_rules or get_deity_lore.

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 a when-to-use scenario ('Use first when asked "what systems do you support?"'), which is helpful. However, it does not mention when not to use this tool or describe alternatives. Given the sibling tool list, the context is clear but lacks exclusions.

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