A
license-
qualityA
maintenanceTranslates text, detects languages, and compares translations across multiple languages using a built-in phrase dictionary.
11
MIT
word-aligner
Server Details
Turn a phrase and its translation into a shareable word-alignment diagram.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- tinygodsdev/bitext-word-alignment
- GitHub Stars
- 4
- Server Listing
- Word Aligner MCP Server
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Tool Definition Quality
A4.9/5.0
Tool DescriptionsA
Average 5/5 across 1 of 1 tools scored.
Server CoherenceA
Disambiguation5/5
With only one tool, there is no possibility of confusion with other tools. The single tool's purpose is clearly defined and detailed, leaving no ambiguity for an agent.
Naming Consistency5/5
The tool name 'create_word_alignment' follows a clear verb_noun pattern, which is consistent and predictable. Even as a single tool, its naming is well-structured and informative.
Tool Count4/5
The server has only one tool, which is slightly under the typical 3-15 range, but it is highly specialized and comprehensive for its narrow purpose. The single tool fully covers the intended functionality of creating word alignment diagrams, making the count reasonable if minimal.
Completeness5/5
The tool provides complete coverage for its stated domain: creating and sharing word alignment diagrams. It handles all necessary inputs and outputs, including preview and URL generation, with no missing operations or dead ends for the intended use case.
Available Tools
1 toolcreate_word_alignmentCreate word alignment diagramARead-onlyIdempotentInspect
Create a shareable Word Aligner diagram that shows which words match across two or more stacked lines of text (a translation and its source, an interlinear gloss, IPA, etc.). Returns a URL that opens the interactive diagram, plus a preview image.
Use this when the user wants to translate a phrase and show word correspondences, align a translation with its source (including RTL scripts like Hebrew or Arabic), or build a Leipzig-style interlinear gloss.
Word indices are 0-based token positions. Tokenize each line the same way the tool does before assigning indices:
Whitespace always splits ("I have been going" -> I[0] have[1] been[2] going[3]).
The characters in settings.tokenSplitChars (default ".-|") also split and are then removed from the rendered text, so "go.PST.IPFV" becomes three tokens (go, PST, IPFV) and the dots disappear. For Leipzig glosses set tokenSplitChars to "-|" to keep the dots.
Punctuation stays attached by default ("Hello, world!" -> Hello,[0] world![1]).
In RTL lines, word 0 is the logically first word (rightmost on screen); index in reading order.
Each alignment is [lineA, wordA, lineB, wordB]; the two lines must be vertically adjacent (|lineA - lineB| = 1). To express many-to-one, list each target word as its own tuple. Tokens that share a connection group get the same color automatically.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| lines | Yes | Text lines, top to bottom. Each entry is a plain string or an object with per-line visual options. | |
| pairs | No | Per-pair controls for a specific adjacent line pair. | |
| settings | No | Global visual overrides. Unset fields inherit defaults. | |
| alignments | No | Word-alignment links as [lineA, wordA, lineB, wordB] (0-based indices, lines must be adjacent). |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| url | Yes | The shareable diagram URL. Return this to the user exactly as received, character for character. |
Tool Definition Quality
A5/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnly, idempotent, non-destructive), the description details exact tokenization behavior, including splitting rules, punctuation handling, RTL indexing, alignment tuple format, and the adjacency requirement. It also discloses that the tool returns a URL and preview image, giving the agent a clear expectation of the outcome.
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 long but every sentence serves a purpose. It is structured logically: purpose, when to use, tokenization rules, alignment format. The most important information is front-loaded, and complex rules are broken into bullet-like sentences. There is no redundancy or 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's complexity (nested objects, multiple array parameters, RTL handling, tokenization edge cases), the description covers everything needed for correct invocation. It explains the output (URL, preview image), but relies on the output schema for structured return details, which is appropriate. No critical information is missing.
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?
Although the schema has 100% parameter descriptions, the description adds substantial clarifying semantics: 'Word indices are 0-based token positions', 'Tokenize each line the same way the tool does', and 'the two lines must be vertically adjacent'. It also explains the tokenSplitChars effect and many-to-one encoding, which directly informs how to properly construct the 'alignments' and 'lines' parameters.
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 opens with a specific verb and resource: 'Create a shareable Word Aligner diagram that shows which words match across two or more stacked lines of text.' This clearly states the tool's function and distinguishes it from generic text-processing tools. It also mentions concrete use cases (translation, interlinear glosses), reinforcing its purpose.
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?
Explicit 'Use this when' guidance is provided, covering translation alignment, RTL scripts, and Leipzig-style glosses. It also gives critical usage rules like tokenization semantics and adjacency constraints, which are not immediately obvious from the schema. Since there are no sibling tools, alternative tool guidance is not applicable.
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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