archiet-xray

by Anioko

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v0.2.4
Disambiguation5/5
Each tool targets a distinct concern: scanning, summarizing, dependency analysis, risk findings, scoring, and diagram generation. No two tools overlap in purpose, and descriptions reinforce their boundaries.
Naming Consistency5/5
All tool names use lowercase snake_case, creating a uniform style. Although not all follow a strict verb_noun pattern, the naming convention is consistent across the set.
Tool Count5/5
With 6 tools, the server is well-scoped for architecture analysis. Each tool addresses a core need, and the count is appropriate.
Completeness5/5
The set covers the full workflow from scanning the codebase to generating summaries, analyzing dependencies, surfacing findings, scoring readiness, and producing diagrams. No obvious gaps for an architecture analysis tool.
Average 4.1/5 across 6 of 6 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 10 commits in the last 12 weeks
- Last stable release on June 11, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds 'deterministic,' which clarifies that results are reproducible, and lists specific finding types. However, it does not explain how the analysis works, what assumptions are made, or what coverage/limitations exist. The readOnlyHint annotation already covers safety, so the description provides moderate additional value.
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 that front-loads the core concept ('Deterministic risk findings') and then enumerates specific categories. Every word contributes value, with no repetition or irrelevant detail.
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?
For a read-only tool with no parameters and no output schema, the description lists output types but lacks details on the format of findings (e.g., list, report), the number of results, or whether these categories are exhaustive. The description is adequate but leaves some gaps, especially regarding how the agent should process the returned findings.
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?
The tool has zero parameters, so the schema fully covers inputs. The description correctly mentions no parameters and does not need to add semantic detail beyond the schema baseline.
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 identifies the tool as reporting deterministic risk findings and enumerates specific categories (hardcoded secrets, raw SQL, auth tokens, routes without auth guards). It is specific enough to distinguish from sibling tools, though it lacks an explicit verb like 'list' or 'report'.
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 is given on when to use this tool versus alternatives such as xray_scan or production_readiness. It does not mention prerequisites, exclusions, or preferred contexts, leaving the agent to infer usage.
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 and openWorldHint: false, so the safety profile is known. The description adds the output specifics (fan-in/fan-out/routes/entities) and a workflow hint, but does not disclose additional behavioral traits like cost, side effects, or data sources. With annotations covering the main safety aspect, this is adequate but not rich.
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, no redundancy. The first sentence front-loads the function, the second gives a crisp usage directive. Every word earns its place.
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?
The tool has one parameter, no output schema, and readable annotations. The description explains what the tool reports (fan-in, fan-out, routes/entities) and when to call it, which is sufficient for a simple read-only analysis command. It could be more explicit about output format, but the overview is adequate.
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?
The schema already provides 100% coverage for the single 'path' parameter with a clear description ('file path or module, relative to repo root'). The description reinforces this by saying 'Given a file or top-level module path' but adds no new semantic detail beyond the schema. Baseline 3 applies due to high schema coverage.
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 exactly what the tool does: given a path, it reports fan-in, fan-out, and routes/entities. This is a specific verb ('report') with clear resource ('file or top-level module path') and outputs, making it easily distinguishable from sibling analysis tools like xray_scan or arch_summary.
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 context: 'Call BEFORE editing anything load-bearing.' This tells the agent when to invoke the tool but does not explicitly name alternatives or state when not to use it. It lacks the 'when-not/alternatives' clarity of a 5.
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 declare readOnlyHint=true, so the description need not repeat safe read behavior. It adds valuable info about determinism ('Same repo always scores the same') and the output structure (8 dimensions, evidence, ranked fixes), which goes beyond the 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?
The description is a single, information-dense sentence that leads with the core purpose, then enumerates the dimensions and outputs efficiently. No wasted words.
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 no-parameter, read-only tool with no output schema, the description covers the essential behavior and output semantics. It could specify the target repo (implicitly the current repo) more explicitly, but this is a 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?
The tool has zero parameters and the schema is empty (100% coverage). Per the rubric, the baseline is 4. The description does not need to explain parameters, and the low complexity is handled well.
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 function: 'Score the repo's production readiness 0-100' across 8 specific dimensions. It also distinguishes it from siblings by highlighting the deterministic nature and the structured output (per-dimension evidence, ranked fixes).
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 explicit usage guidance or comparison to sibling tools is provided. The description does not state when to use this tool instead of alternatives like xray_scan or arch_summary, nor any exclusions or prerequisites.
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 indicate readOnlyHint=false, and the description adds concrete side effects by stating it writes .archiet/diagrams/ with 3 .mmd files and an HTML viewer. This goes beyond the basic mutation flag and provides useful details about output locations and file types, though it does not mention overwrite behavior or permissions.
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: the first front-loads the core purpose and diagram types, the second explains the file output and rendering compatibility. Every word earns its place; 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?
The tool is simple (one optional boolean), there is no output schema, and the description fully covers what it produces, where it writes when requested, and how to use the output. It is complete enough for an agent to confidently select and invoke the tool.
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?
The input schema has one boolean parameter 'write' with a full description already covering the write-to-disk behavior, and the tool description echoes this. With 100% schema coverage, the description adds no new parameter semantics, so the baseline score of 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 tool generates architecture diagrams as Mermaid sources, listing three specific diagram types (module dependency graph, domain-model ER diagram, HTTP route map). It uses a specific verb ('Generate') and resource, and the distinctive purpose separates it from sibling tools that handle scanning, summaries, or impact analysis.
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 context for when to use the tool: when architecture documentation diagrams are needed, and it notes that the Mermaid output is compatible with GitHub, GitLab, Notion, and VS Code. However, it does not explicitly mention alternatives or exclusions, so it stops short of full when-not-to-use guidance.
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 declare readOnlyHint=true, so safety is known. The description adds behavioral context: 'compact' signals a high-level overview, and 'ground truth' asserts authority. This goes beyond the 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?
Two sentences with distinct purposes: the first lists contents, the second gives usage guidance. No redundancy, perfectly front-loaded and concise.
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 no-parameter tool, the description adequately covers what it returns and when to use it. The lack of an output schema is mitigated by the explicit content list, though a bit more detail on return format would be helpful.
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?
The tool accepts zero parameters, so the input schema fully defines the invocation. The description enriches understanding by listing the output content, which is more valuable than parameter detail.
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 this tool provides a 'compact architecture summary' and enumerates its contents (modules, route prefixes, domain entities, hotspots, visibility score). It distinguishes itself from siblings by labeling it as 'ground truth for where things live', implying a foundational overview rather than a deep-dive.
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 to use this 'before searching the codebase', giving clear when-to-use guidance. It does not explicitly name alternatives, but the positioning as an entry point is sufficient given the sibling tool names.
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 declare read-only and closed-world behavior. The description adds that the tool builds a persistent architecture model, implying a session-level side effect, and the timing guidance suggests it may be resource-intensive. No contradictions with annotations.
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 action and then adding usage timing. Every word is purposeful, with no 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?
For a zero-parameter, read-only tool, the description covers its function and when to run it. It lists model components, providing a clear expectation of output, though it doesn't specify how the model is accessed or returned.
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?
The tool has zero parameters, so schema coverage is trivially 100%. The description adds context about what the scan covers, which indirectly helps the agent understand the tool's scope, though parameter detail is unnecessary.
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 action ('Scan the repository') and resource ('architecture model'), enumerating specific components like routes, entities, tasks, dependency graph, and findings. This differentiates it from sibling tools that summarize or generate 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?
The description provides explicit usage timing: 'Run once at session start and after structural changes.' It does not name alternatives or exclusions, but the context as a prerequisite scan is clear.
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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