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HebrewCore by baba Hebrew

Analyze RTL/LTR direction

analyze_direction
Read-onlyIdempotent

Resolve the right-to-left / left-to-right structure of mixed Hebrew text: base direction, whether it mixes directions, dominant language and script, and the runs to render, where every left-to-right unit (a Latin term, a price, a range such as 10–20%, a date, a model number) stays whole. Send one text, or fields (a map of name → string, up to 100) to analyze a whole record at once. base: auto (default), ltr or rtl. Deterministic and free: no model, no character quota.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
baseNoParagraph direction to resolve against. `auto` (default) uses the first strong letter.auto
textNoA single string, up to 20,000 characters. Send this or `fields`.
fieldsNoA map of field name → string, up to 100 fields and 20,000 characters in total. Send this or `text`.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsNoPresent when the request sent `fields`: one analysis per field name.
analysisNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds meaningful behavioral context beyond those: it is deterministic, free, has no character quota, and preserves left-to-right units whole during run generation. This provides useful expectations for an agent without contradicting any annotation.

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 sentences, each earning its place: the first states the core purpose and outputs, the second gives invocation instructions and the `base` options, and the third adds the determinism/quota guarantee. No fluff or repetition of schema fields.

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 an output schema is present, the description need not explain return values. It adequately covers the tool's purpose, input alternatives, constraints (20k chars, 100 fields), and the `base` enum behavior. An agent can determine whether and how to call this tool correctly with the information provided.

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?

With 100% schema description coverage, the baseline is 3. The description adds value by explaining the intended use of `text` versus `fields` ('analyze a whole record at once') and by defining the run-splitting rule with concrete examples (Latin term, price, range, date, model number). This enriches the parameters beyond the schema's basic definitions.

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 names a specific verb ('Resolve') and resource ('the right-to-left / left-to-right structure of mixed Hebrew text') and enumerates the concrete outputs: base direction, mix status, dominant language/script, and render runs. This clearly distinguishes it from siblings like detect_language or transliterate, which target different aspects of text processing.

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 description explains how to invoke the tool — sending either a single `text` or a `fields` map, and choosing `base` — but it does not explicitly state when to prefer this tool over siblings (e.g., when not to use detect_language). The 'Deterministic and free' note hints at selection criteria but leaves the comparison to the agent to infer.

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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