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Moltline TimeOps

by com.moltlinestudio.mcp

Business Days

business_days
Read-onlyIdempotent

Do business-day math that skips weekends and your holiday list. FREE.

Either add N business days to start_date (set add_days), or count the business days between start_date and end_date (set end_date). Typical input {"start_date": "2026-03-02", "add_days": 10} returns {"result_date": "2026-03-16", "result_weekday": "Monday", ...}; with end_date set it returns {"business_days_between": N, "from": ..., "to": ...}.

Use when the span must exclude weekends and named holidays. Not for plain calendar-day differences, which are a direct subtraction, and not for scheduling backward from a fixed deadline (deadline_planner). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "dates must be YYYY-MM-DD"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
add_daysNoBusiness days to add; negative subtracts. Range -5000 to 5000. Ignored when end_date is provided.
end_dateNoOptional end date, ISO YYYY-MM-DD. When set, the tool counts business days between start_date and end_date instead of adding.
holidaysNoOptional list of ISO YYYY-MM-DD dates to treat as non-working days, e.g. ["2026-12-25"].
start_dateYesAnchor date in ISO format YYYY-MM-DD, e.g. "2026-03-02".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context beyond annotations: the tool never raises protocol errors, instead returns a structured error object with fix instructions. It also explains that retries are safe after correction, which aligns with idempotency. No contradictions.

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

Conciseness5/5

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

Three well-structured paragraphs: first states core function and format, second gives concrete examples, third provides usage guidance and error handling. Every sentence serves a purpose—no fluff. Front-loaded with the key idea 'skip weekends and your holiday list'.

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

Completeness5/5

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

Given low parameter count (4), 100% schema coverage, and an output schema (not shown but referenced), the description is highly complete. It covers purpose, usage modes, error behavior, idempotency, and sibling differentiation. Nothing critical is missing.

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 description coverage is 100%, so baseline is 3. The description adds meaning by explaining the two complementary input modes (add_days vs end_date) and gives typical examples with expected outputs. It clarifies that add_days is ignored when end_date is provided, which isn't explicit in the schema. However, it doesn't detail edge cases like negative values beyond what the schema provides.

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

Purpose5/5

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

The description uses specific verbs ('Do business-day math', 'skip weekends and your holiday list') and clearly differentiates the two modes (adding N days or counting between). The examples illustrate typical input/output, making the purpose unambiguous. It also distinguishes from sibling tools like deadline_planner, reinforcing its unique role.

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

Usage Guidelines5/5

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

Explicitly states when to use: 'when the span must exclude weekends and named holidays'. It also tells when not to use: 'Not for plain calendar-day differences... and not for scheduling backward from a fixed deadline (deadline_planner).' This gives clear context and alternatives.

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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TDQS

A4.7/5.0
Disambiguation4/5

Each tool targets a distinct time-related operation: business day math, backward scheduling, timezone overlap, recurrence rules, and SLA due times. The descriptions include 'Use when ... Not for ...' sections that clearly differentiate them, though business_days and deadline_planner both involve business days and could be confused for near-simple tasks.

Naming Consistency5/5

All tool names use a consistent snake_case pattern with clear, descriptive verb+noun or noun_verb combinations (business_days, deadline_planner, meeting_overlap, recurrence_expand, sla_due). The naming is predictable and intuitive for the domain.

Tool Count5/5

Five tools is a well-scoped set for a time operations server, covering core scheduling needs without being too few or too many. Each tool addresses a distinct use case and the count is appropriate for the server's purpose.

Completeness4/5

The tool set covers major temporal operations: date arithmetic, backward planning, timezone coordination, recurrence, and SLA tracking. A minor gap is the lack of a plain calendar-day difference tool (as noted in business_days description), but the core domain is well covered and agents can work around this gap.

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