word-aligner
Server Details
Turn a phrase and its translation into a shareable word-alignment diagram.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.7/5 across 1 of 1 tools scored.
With only one tool, there is no risk of confusion or overlap between tools. The single tool's purpose is clearly described and distinct.
The single tool name 'create_word_alignment' follows a consistent verb_noun pattern, which is clear and predictable.
One tool feels thin for most domains, but given the specialized nature of word alignment, it is borderline acceptable. A few more tools (e.g., list, delete) would improve scoping.
The server only supports creating alignments, with no ability to retrieve, update, or delete existing ones. This is a significant gap for typical usage patterns.
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.
| 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
| Name | Required | Description |
|---|---|---|
| url | Yes | The shareable diagram URL. Return this to the user exactly as received, character for character. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. Description adds valuable behavioral details: tokenization logic, word index handling, RTL behavior, and alignment constraints. This goes beyond annotations, though some repeated context from schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is lengthy but every sentence adds value. It is front-loaded with purpose and use cases, then details tokenization rules. No redundancy, but could be slightly more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 4 params and nested objects, the description covers tokenization, alignment rules, and global settings. Output schema handles return details. Complete enough for accurate use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but description adds practical meaning: explains tokenization exactly as needed for correctly specifying indices, clarifies that alignments must be between adjacent lines, and gives tokenSplitChars examples. This is crucial for correct tool invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that it creates a Word Aligner diagram showing word matches across stacked lines, and returns a URL and preview image. The verb 'Create' and resource 'Word Aligner diagram' are specific. No sibling tools exist, so no differentiation needed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use: for translating phrases and showing correspondences, aligning translation with source, or building interlinear glosses. Also provides tokenization rules for correct usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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