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get_calc_dates

Read-onlyIdempotent

Exact date arithmetic — the calendar math LLMs guess at. ?from=&to= → days, business days, weeks, hours between; ?date=&add=30d|2w|3m|1y|10bd → the resulting date (bd = business days); ?date= alone → weekday, ISO week, day-of-year, leap year, unix. All UTC, Mon-Fri business days, real-calendar validation (Feb 30 is rejected). ($0.001 per call, paid via x402)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNo
addNoe.g. 30d, -2w, 3m, 1y, 10bd
dateNoISO date — alone: info; with add: arithmetic
fromNoISO date — with to: difference

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNo
dateNo
diffNo
fromNo
resultNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already indicate the tool is read-only, idempotent, and non-destructive. The description adds valuable behavioral context: all dates are UTC, business days are Mon-Fri, invalid dates like Feb 30 are rejected, and there is a $0.001 cost via x402. This goes beyond annotations without contradiction.

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?

The description is dense but efficient, using concise examples (e.g., ?from=&to= ) to convey multiple use cases in a compact format. Every sentence adds unique information, with no filler.

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 the tool has an output schema and the description covers all input combinations and behaviors (including edge cases and cost), the description is fully sufficient for an AI agent to understand and invoke the tool correctly.

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

Parameters5/5

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

The schema covers 3 of 4 parameters with descriptions (75% coverage). The tool description greatly enhances understanding by showing exact result types for each parameter combination (e.g., from+to yields days, business days, weeks, hours; date+add yields resulting date; date alone yields weekday, ISO week, etc.). This adds significant semantic value beyond the schema.

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 clearly states the tool performs exact date arithmetic, listing specific use cases: date differences, date addition/subtraction, and single-date info. This distinguishes it from other tools in the sibling list, none of which focus on date calculations.

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

Usage Guidelines4/5

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

The description provides explicit usage patterns via query examples (from+to, date+add, date alone), making it clear when to use each combination. It does not explicitly mention when not to use the tool or alternatives, but given its unique functionality, the guidance is effective.

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

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes, but the SEO-related tools (head_check, full_audit, site_audit, etc.) overlap in scope, potentially causing confusion despite clear descriptions.

Naming Consistency5/5

Tool names consistently follow a get_/post_/delete_ verb pattern with descriptive noun phrases (e.g., get_seo_head_check, post_store_collection), with no mixing of naming conventions.

Tool Count2/5

With 46 tools covering a wide breadth of domains (SEO, accessibility, music, crypto, linting, etc.), the count is excessive for a single server, feeling unfocused and heavy.

Completeness4/5

The tool set covers most core operations for each sub-domain, but minor gaps exist (e.g., missing update for datastore, limited music operations).