Docker Forge MCP

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a unique, clearly defined purpose: Traefik labels, compose auditing, Dockerfile generation, env template extraction, stack generation, and listing templates. No functional overlap exists.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., add_traefik, audit_compose, list_stacks). No mixing of conventions or vague verbs.
Tool Count5/5
With 6 tools, the server is tightly scoped to Docker/Compose utilities. Every tool addresses a distinct need; no excess and no trivial tools.
Completeness4/5
The set covers generation, auditing, and listing for common Docker workflows. Missing a tool for deploying or modifying stacks, but the existing tools handle core creation and validation tasks.
Average 3.9/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 4 commits in the last 12 weeks
- No stable releases found
- 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",
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}
```
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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?
With no annotations, the description bears full responsibility for behavioral disclosure. It mentions included security best practices and optional Traefik labels, but does not detail output format, error handling, or prerequisites. Adequate but not comprehensive.
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 succinct sentences: first states the core purpose, second adds key feature (optional Traefik). No wasted words, front-loaded content.
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 description covers main functionality and key differentiators (security, Traefik). Given the high schema coverage and no output schema, it is sufficiently complete for a generation tool, though examples or output format clarification would improve it.
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 the schema already documents parameters well. The description adds little beyond schema, only noting that domain triggers Traefik labels. Baseline 3 is appropriate.
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 that the tool generates a Docker Compose stack from a natural language description or a specific stack type. It lists included security features and optional Traefik labels. However, it does not explicitly differentiate from sibling tools like generate_dockerfile or add_traefik, which have overlapping scopes.
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 when a full stack is needed but does not provide explicit guidance on when to use this tool versus alternatives (e.g., generate_dockerfile for a single Dockerfile). No when-not-to-use or exclusions are mentioned.
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?
No annotations provided, so the description carries the burden. It discloses features like multi-stage builds, non-root user, minimal image, and HEALTHCHECK, which are positive behavioral traits. However, it does not specify whether the tool writes to a file or returns text, nor does it mention side effects or permissions needed.
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 includes a '[PRO]' prefix for intended audience, lists the core purpose, supported technologies, and key features. Every part is meaningful and there is no redundant content. It is compact and front-loaded.
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 five parameters (all described in schema) and no output schema, the description is moderately complete. It covers supported runtimes and app types, but lacks information about the output format (e.g., returns text, saves to file), error handling, or any prerequisites. For a generation tool, these would be helpful for agents.
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 baseline is 3. The description adds context by mentioning supported runtimes and app types (Next.js standalone, FastAPI/Django), which aligns with the 'runtime' and 'app_type' parameters. However, it does not provide additional syntax or format details beyond what the schema already offers.
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 'Generate' and the resource 'optimized, security-hardened multi-stage Dockerfile'. It lists supported runtimes (Node.js, Python, Go) and specific app types (Next.js, FastAPI/Django), distinguishing it from sibling tools like add_traefik or audit_compose which serve different purposes.
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 does not explicitly state when to use this tool versus alternatives. It implies usage for generating Dockerfiles for Node.js, Python, or Go projects, but lacks guidance on when not to use it or when to prefer a sibling tool. The features are listed but no direct comparison.
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?
No annotations are provided, so the description carries the full burden. It mentions generating a categorized .env template with secrets hints, giving some insight into behavior. However, it does not disclose whether the tool is read-only, modifies files, or requires any permissions, leaving gaps in transparency.
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 sentence that efficiently conveys the tool's purpose and output. It is front-loaded with key actions. The '[PRO]' prefix is minor noise but does not detract significantly. Concise without sacrificing clarity.
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 (single parameter, no output schema), the description adequately covers what the tool does and what it produces. It explains the output's nature (categorized comments, secrets hints). No major gaps are apparent for this use case.
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 single parameter 'compose_yaml' is described). The description does not add significant meaning beyond the schema's description of the parameter. The baseline of 3 applies since the schema already documents the parameter adequately.
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 uses specific verbs ('extract', 'generate') and clearly states the resource (env var placeholders from Docker Compose YAML) and output (a .env template with categorized comments). It distinguishes from sibling tools like generate_dockerfile and generate_stack, which have different purposes.
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 extracting env vars from Docker Compose YAML but lacks explicit guidance on when to use this tool versus alternatives (e.g., audit_compose might also examine environment). No when-not or prerequisite information is provided, leaving room for ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries the full burden. It does not disclose any behavioral traits beyond the basic listing action, such as safety, idempotence, or performance characteristics.
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?
A single, clear sentence with no redundant information. Every word 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?
Given the simplicity (no parameters, no output schema), the description fully covers what the tool does and what it returns. It is complete for this context.
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 baseline is 4. The description correctly reflects that no parameters are 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 specifies the verb 'list' and the resource 'Docker Compose stack templates', and mentions the return details (descriptions and tags). It distinguishes from siblings by focusing on listing, while siblings are about adding, auditing, or generating.
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 siblings are listed but no explicit when/when-not or context provided.
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?
Describes outputs (labels, networks section) and supported features (TLS, redirect, middleware presets) without annotations. Does not mention potential side effects, but as a label generator, mutation is unlikely.
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 concise sentences, front-loaded with the action and resource, 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?
Covers generation of labels and main options. Lacks output format details, but no output schema exists. Adequate for a simple generation 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?
Schema coverage is 100% with descriptions for all parameters. Description adds no new parameter-specific details beyond summarizing features like TLS and middlewares, keeping score at 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?
States 'Generate Traefik v3 labels for a Docker service' with a specific verb and resource. Distinguishes from siblings like generate_dockerfile and generate_env.
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?
Implied usage by describing output for docker-compose.yml, but no explicit when-to-use or when-not-to-use vs alternatives like audit_compose.
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?
With no annotations, the description carries full burden. It lists specific checks (e.g., privileged mode, root user) and states return format (score 0-100, categorized issues). It does not mention side effects, but the tool is read-only. The description adds value beyond schema by detailing what the tool evaluates.
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 (two sentences) yet packed with useful information: what it does, what it checks, and what it returns. Every sentence adds value without unnecessary words. It is front-loaded with the primary purpose.
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 complexity (single-parameter tool with no output schema), the description fully explains inputs and outputs. It lists 10 checks and the output format (score and issues). The agent has enough context to decide and use the tool correctly.
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 schema has one required parameter 'compose_yaml' with a description. The tool description adds 'The full content of your docker-compose.yml file to audit' which reinforces that it expects the entire file. Since schema coverage is 100%, the description provides marginal additional meaning but is sufficient.
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 audits Docker Compose YAML strings for security vulnerabilities and best-practice violations. The verb 'audit' and resource 'Docker Compose YAML string' are specific. Sibling tools like generate_dockerfile and list_stacks indicate different purposes, making this tool's role distinct.
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 implicitly indicates usage: when you have a compose YAML to check. It lists detailed checks and returns a score and issues. However, it does not explicitly mention when not to use or compare with siblings (e.g., use generate_dockerfile to create Dockerfiles). This is a minor gap.
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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