dgmo-mcp

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by diagrammo

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Server Quality Checklist

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Latest release: v0.28.4
Disambiguation5/5
Each tool has a clearly distinct purpose, from installation checks to rendering and sharing. No overlaps are apparent.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case, making them predictable and easy to understand.
Tool Count5/5
11 tools is well within the ideal range for a domain-specific server, covering diagram creation, validation, rendering, sharing, and reporting without unnecessary bloat.
Completeness5/5
The tool surface covers the full lifecycle: environment check, type suggestion, examples, reference, validation, rendering, preview, sharing, and reporting. No obvious gaps.
Average 4.1/5 across 11 of 11 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 2 community issues answered or closed in the last 6 months
- 169 commits in the last 12 weeks
- Last stable release on August 28, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds no behavioral details beyond retrieving documentation, such as output format or side effects. Minimal added value.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no superfluous words. It front-loads the main action but could benefit from slightly more detail on output.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema is provided, and the description does not hint at the return format or structure. Given the simplicity of the tool, more context about the documentation content would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear description of the chart_type parameter. The description echoes the schema without adding new semantics, meeting the baseline for schema-covered parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves DGMO language reference documentation and optionally filters by chart type. This distinguishes it from tools like 'get_examples' or 'list_chart_types' but does not explicitly contrast with siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The description does not specify scenarios where filtering is needed or when other tools are more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations give readOnlyHint=false and destructiveHint=false. The description adds behavioral context by stating it opens an HTML preview in the browser and supports theme toggling, but does not mention potential side effects like preventing concurrent operations or browser tab management.
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 two sentences, front-loading the core action and adding a concise usage hint. Every sentence provides value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (four parameters including nested array, optional fields) and no output schema, the description adequately covers the basic preview functionality but lacks details on browser interaction, error cases, or the `openWorldHint` implication.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are well-documented in the schema. The description reinforces the color label rule and provides an example, but does not add significant new meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool renders DGMO diagrams and opens an HTML preview in the browser, including support for theme and source display. It differentiates from siblings like 'render_diagram' by explicitly mentioning the browser preview, but does not directly compare to related tools.
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 implies usage for previewing DGMO diagrams and references `get_language_reference` for syntax help, providing some guidance. However, it does not specify when to use alternatives like `render_diagram` or `validate_diagram`, nor does it indicate prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description 'Generate a shareable URL' is consistent with these annotations, but adds minimal additional behavioral context (e.g., whether it creates server-side state or only transforms input). 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that immediately conveys the tool's purpose. Every word is necessary, and it is front-loaded with the key action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description combined with annotations is largely complete. It does not explain the URL format or expected usage, but these are minor gaps given the tool's straightforward nature.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the single parameter 'dgmo' is well-described as 'DGMO diagram markup'. The tool description does not add further meaning beyond what the schema provides, placing it at the baseline.
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 it generates a shareable URL for a DGMO diagram. It uses a specific verb ('Generate') and resource ('shareable diagrammo.app URL'), and implicitly distinguishes from siblings like 'preview_diagram' and 'open_in_app'.
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 indicates a clear purpose but provides no explicit guidance on when to use this tool versus alternatives. It does not mention prerequisites, restrictions, or when not to use it, which would be helpful given several sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds minimal behavioral context beyond stating the content (chart types with descriptions). It does not contradict annotations, but it also does not elaborate on traits like return format or error states.
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 a single sentence of 6 words, conveying the essential purpose without any extraneous information. It is front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool (no parameters, annotations present, no output schema), the description is largely complete. It informs the agent what the tool does and what content to expect. However, it could hint at the output structure (e.g., 'returns an array of chart type objects with name and description'). Still, it is sufficient for an agent to understand the tool's purpose.
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?
No parameters exist, so schema coverage is 100%. The description adds context by specifying that the list includes descriptions, which adds meaning beyond the empty schema. Baseline for 0 params is 4.
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 'List all supported DGMO chart types with descriptions' clearly states the verb (list), the resource (chart types), and the scope (all supported, with descriptions). It distinguishes from sibling tools like 'suggest_chart_type' which is for recommendations, not listing.
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?
Usage is implied: use to get a list of chart types. However, there is no explicit guidance on when to use versus alternatives such as 'suggest_chart_type', nor any exclusions or prerequisites, so the description lacks clear context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It states it returns real-world examples, but does not mention behavior when the parameter is omitted (lists all names) or any read-only implications. Adequate but not fully transparent.
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?
Two short, front-loaded sentences with no redundancy. Every phrase earns its place.
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?
For a tool with one optional parameter and no output schema, the description fully explains purpose, return content, and usage context. Complete for its complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the description does not need to add much. It provides example values and tells to omit for listing names, which adds slight value beyond the schema. Baseline 3 is appropriate.
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 action ('get'), the resource ('example DGMO diagrams for a chart type'), and the purpose ('few-shot references'). It is specific and distinguishes from siblings like generate_report or validate_diagram.
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 explicitly says 'Use these as few-shot references when generating new diagrams,' indicating when to use. It does not explicitly state when not to use or mention alternatives, but the context makes it clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds value beyond annotations by stating 'Opens in browser by default' and mentioning optional source blocks. Annotations already cover readOnlyHint=false, destructiveHint=false, so 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no waste. The cross-reference is efficient and well-placed. Front-loaded with key purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers the main purpose, output characteristics (HTML, browser, TOC, source blocks), and cross-reference. Lacks explicit return value info, but output schema absent. Adequate for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description includes a brief example for the dgmo parameter but does not add substantial new meaning beyond the schema's descriptions.
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?
Description states the verb 'Generate' and resource 'polished HTML report with multiple DGMO diagrams, table of contents, and optional source blocks', clearly distinguishing from sibling tools like preview_diagram or render_diagram which handle single diagrams.
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?
Provides explicit cross-reference to get_language_reference for DGMO syntax, guiding when to use that sibling tool. However, it does not specify when to avoid this tool or contrast with alternatives like share_diagram.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds useful context beyond annotations: it reveals that when format is 'png', the tool saves to a temp file and returns the path, and describes return formats (SVG text vs base64 PNG). This complements the readOnlyHint and idempotentHint annotations without contradicting them.
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 compact, with four short sentences that front-load the purpose and then add return details and a syntax pointer. No information is redundant or wasted.
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?
The tool has 4 parameters and a clear return type, and the description covers output formats, side effects, and a reference to the syntax guide. It is sufficiently complete for an agent to invoke it correctly, given the schema and annotations.
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?
Schema coverage is 100%, but the description reinforces the dgmo parameter with the 'Sales red' example and adds the side effect of the format parameter for PNG. This adds value beyond the schema's 'Output format' description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool renders DGMO markup to SVG or PNG, with a specific verb and resource. It also distinguishes itself from get_language_reference by directing syntax questions there, but does not explicitly differentiate from the similar preview_diagram sibling.
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?
It provides explicit guidance to call get_language_reference for DGMO syntax, implying this tool is for rendering. However, it doesn't offer exclusion criteria for when not to use this tool versus preview_diagram or other siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the two possible return shapes (confident pick or ambiguous directive) and the required action on ambiguity. However, it does not explicitly state that the tool is non-destructive, though that is implicit for a suggestion tool.
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 concise and well-structured: three paragraphs starting with the core purpose, followed by a critical usage guideline, and finishing with detailed return behavior. Every sentence adds value with no redundancy.
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 simplicity (one parameter, no output schema), the description covers all necessary aspects: purpose, when to call, return types, and agent action on ambiguity. It is fully sufficient for an AI agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter 'prompt', with a basic description. The tool description adds some context about the prompt being 'plain-English diagram request' but does not elaborate on format or examples. Baseline 3 is appropriate as the schema already does the heavy lifting.
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's purpose: 'Suggest the best DGMO chart type for a user's plain-English diagram request.' This distinguishes it from sibling tools like generate_report or render_diagram, and the two return shapes are explicitly described.
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?
The description provides explicit guidance: 'ALWAYS CALL THIS FIRST when creating a new diagram — it prevents guessing and is the authoritative selection mechanism.' It also details what to do on an 'ASK THE USER' directive, including not picking a type yourself.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description bears full responsibility. It discloses that the tool does not render and returns structured errors/warnings, and that it is faster. While it doesn't discuss auth or rate limits, these are less critical for a validation tool. A minor omission: it could explicitly state that it does not modify data, but 'without rendering' implies no side effects.
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 two concise sentences, front-loads the purpose, and contains no extraneous information. Every sentence adds value.
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 simplicity (one required parameter, no output schema), the description fully covers what the tool does, when to use it, and what it returns. No gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%: the parameter 'dgmo' is described as 'DGMO diagram markup to validate'. The description adds no additional parameter-level detail beyond the schema. Per guidelines, baseline is 3 when schema coverage is high.
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 verb 'validate' and the resource 'DGMO markup', and specifies it returns structured parse errors and warnings. It effectively distinguishes from sibling tools like render_diagram by noting it does not render.
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?
The description explicitly says to use this tool to check syntax before rendering and mentions it's much faster than render_diagram. This provides clear when-to-use guidance and contrasts with an alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, safe operation. Description adds platform specificity (macOS) and the behavioral implication of checking existence. Could mention if it triggers any UI or prompts, but overall sufficient.
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, front-loaded with purpose, then usage guidance. No redundant or extra information. Every sentence is essential.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description lacks explicit return type (likely boolean). However, for a simple existence check, the purpose is clear enough. Minor gap.
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?
No parameters; schema coverage is 100% trivially. Baseline is 4 per guidelines. Description correctly has no parameter info needed.
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 checks if the Diagrammo desktop app is installed on macOS, using specific verbs and resource. It distinguishes itself from siblings like open_in_app and share_diagram by defining its role in a conditional workflow.
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?
Explicitly says to call ONCE before choosing display method, and provides explicit alternatives: use open_in_app if installed, else fall back to share URL. No ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are minimal (readOnlyHint, destructiveHint, openWorldHint). The description adds critical behavior: macOS only, fallback to browser preview, filePath autosaving and live re-render, and the distinction between ephemeral and persistent modes.
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?
Five sentences, front-loaded with the main action and platform. Every sentence adds essential information without redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, but the description explains the two modes and fallback. It covers platform restriction and file persistence. Could mention installation requirement more explicitly, but 'preferred path when app is installed' implies it.
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?
Schema coverage is 100% with descriptions. The description adds value by explaining that filePath opens the exact file for live editing (source of truth) and that dgmo is the markup. This enriches the basic schema 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 specifies the verb 'open', the resource 'DGMO diagram', and the platform 'macOS only'. It distinguishes from sibling tools like 'preview_diagram' by noting fallback behavior and file handling.
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 clear guidance on when to use filePath (for saved files with live editing) vs omit (ephemeral diagram). It implicitly contrasts with preview_diagram as fallback, but does not explicitly list when not to use.
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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