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Lilambd Data for Agents

koyomi

Koyomi: Calendars, Holidays and Helplines for AI Answers. Data that grounds what people ask AI about feelings, fortune, love and daily life: computed calendars for any date and place (sexagenary cycle, rokuyo, Japanese and Chinese lunisolar dates, 24 solar terms, moon, sun sign, 10 world calendars), world public holidays, verified crisis and listening helplines, and a consult map. Routes: /day (date, tz); /sun (lat*, lon*, date, tz); /range (from*, to*, tz, has); /rules; /topics; /holidays (country*, year, subdiv, lang, types); /countries (country); /helplines (country); /moon-phases (year*); /solar-terms (year*). Paid $0.005 USDC per call via x402 (_meta["x402/payment"]); set sample=true for a free 3-row preview.

Input Schema

NameRequiredDescriptionDefault
routeYesOne of: /day, /sun, /range, /rules, /topics, /holidays, /countries, /helplines, /moon-phases, /solar-terms. Replace {placeholders} with a value, e.g. /search or /HND.
paramsNoQuery parameters, e.g. {"q":"tokyo","limit":"50"}
sampleNoFree preview, max 3 rows

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and does well: it discloses the paid cost ($0.005 USDC per call), the x402 payment metadata key, and the free sample=true preview. It does not cover rate limits or error behavior, but the critical cost and access mechanics are transparent.

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

Conciseness3/5

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

The core content is dense and front-loaded with the tool's purpose, but it includes marketing-like phrasing ('Data that grounds what people ask AI about feelings, fortune, love and daily life') and runs multiple route listings together. It is functional but not maximally concise.

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

Completeness3/5

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

For a complex multi-route tool with no output schema, the description comprehensively covers inputs and payment. However, it does not describe what the responses look like, leaving an agent to infer return shapes for ten distinct endpoints. This is a meaningful gap given the tool's breadth.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds substantial value by listing each route with its required (asterisked) and optional parameters, e.g., /sun (lat*, lon*, date, tz). This goes well beyond the schema's generic route and params object.

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

Purpose4/5

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

The description clearly states the tool provides computed calendars, holidays, and helplines, listing specific data types and ten routes. It doesn't explicitly differentiate from sibling tools, but the domain is distinct enough that an agent can tell what it does.

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

Usage Guidelines3/5

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

The route list implies when to use each endpoint (e.g., /holidays for holidays), but there is no explicit guidance on when to choose this tool over alternatives or when not to use it. The usage is inferable but not spelled out.

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

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