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
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
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?
The description thoroughly explains behavioral traits beyond annotations: tokenization rules (whitespace, tokenSplitChars, punctuation handling), RTL handling, alignment constraints (adjacent lines, 0-based indices), and output format (URL plus preview). Annotations indicate idempotent and read-only hints, which are consistent (creation is not destructive). 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is comprehensive and well-structured, starting with the primary purpose and output, followed by usage guidance, then detailed tokenization and alignment rules. While it is lengthier than some, every section adds necessary context for a tool with complex input parameters. Minor redundancy could be trimmed, but overall it is efficient.
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?
Given the tool's complexity (4 parameters, nested objects, array parameters, 100% schema coverage, and an output schema), the description covers all essential aspects: what the tool does, when to use it, detailed tokenization behavior, alignment rules, and what the user gets (URL and preview). No gaps remain.
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?
With 100% schema description coverage, the description adds substantial meaning: it explains 0-based indexing for word positions, how tokenization works for each parameter (e.g., tokenSplitChars default and effect), the structure of alignment tuples, and constraints like vertical adjacency. The schema provides formal definitions, but the description makes usage intuitive.
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 the tool creates a shareable Word Aligner diagram showing word matches across stacked lines of text. It specifies the output (URL and preview image) and lists concrete use cases like translation alignment and interlinear glosses. The verb and resource are specific, and no sibling tools exist for differentiation.
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?
The description explicitly provides usage scenarios: translating phrases with word correspondences, aligning translations with sources (including RTL scripts), and building Leipzig-style glosses. While it does not state when not to use the tool, the absence of sibling tools means no alternative context is needed, making the guidance sufficiently clear.
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.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!