This server provides YAML processing tools via MCP, including validation, conversion, linting, and merging.
validate_yaml: Check YAML syntax and report errors.
convert_yaml_json: Convert between YAML and JSON formats (yaml_to_json or json_to_yaml).
lint_yaml: Lint YAML content for style issues and best practices.
merge_yaml: Merge two YAML documents using deep or shallow merge strategies.
Provides tools for validating, linting, and converting YAML files, enabling AI agents to manage YAML content programmatically through the MCP server.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@yaml-ai-mcp screen this transaction for AML red flags"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
mcp-name: io.github.CSOAI-ORG/yaml-ai-mcp
By MEOK AI Labs ā MEOK AI Labs MCP Server
6AMLD + UK MLR 2017 + FinCEN AML/CFT compliance MCP ā customer due diligence, transaction monitor...
6AMLD + UK MLR 2017 + FinCEN AML/CFT compliance MCP ā customer due diligence, transaction monitoring, SAR filing. MIT.
# Install via pip
pip install yaml_ai_mcp
# Or install via Smithery
npx -y @smithery/cli@latest install yaml-ai-mcp --client claudeRelated MCP server: Enterprise Financial Compliance Audit Framework
MCP protocol compliant
Easy installation
Well-documented API
Production-ready
Active maintenance
This MCP server is built with EU AI Act compliance built-in:
ā Article 9 ā Risk Management System
ā Article 13 ā Transparency & Instructions for Use
ā Article 15 ā Bias Detection & Testing
ā Article 26 ā FRIA Support (where applicable)
ā Article 50 ā AI Content Watermarking (where applicable)
Need help getting compliant? Book a free 15-min diagnostic ā
Need custom development, SLA guarantees, or white-label deployment?
Pro: $99/mo ā Full MCP suite + EU AI Act tracking
Enterprise: $499/mo ā Custom dev + SLA + Dedicated support
View Pricing ā | Contact Sales ā
This server is part of the MEOK AI Labs ecosystem ā 300+ MCP servers for sovereign AI governance.
Domain | Purpose |
EU AI Act compliance marketplace | |
AI safety & monitoring | |
Sovereign AI platform | |
Legacy modernization |
MIT Ā© CSOAI-ORG
Build something that touches users? You need compliance. MEOK ships 38 governance MCPs that drop in alongside this tool ā EU AI Act, DORA, NIS2, CRA, GDPR, ISO 42001, FDA SaMD, MDR, Basel, MiFID II, MiCA, COPPA, and more.
# One-shot install of the governance pack
npx meok-setup --pack governanceFree tier: 10 calls/day per MCP. Pro tier (Ā£79/mo): unlimited + cryptographically signed compliance attestations your auditor verifies independently.
ā Full catalogue: councilof.ai/catalogue ā MEOK AI Labs: meok.ai
Tier | Price | What you get | Stripe |
Smoke test | Ā£1 | Signed sample MCP-Hardening report + Article 50 PDF | |
Quick Kit | Ā£9 | EU AI Act Article 50 implementation guide (C2PA + EU-Icon) | |
Founder Call | Ā£29 | 30-min 1-on-1 with the founder |
Refundable. UK Stripe ā VAT-clean. Builds on the 81-MCP MEOK fleet. Verify any signed report at https://meok.ai/verify.
Add to your claude_desktop_config.json (Claude Desktop) or your MCP client config:
{
"mcpServers": {
"yaml-ai-mcp": {
"command": "uvx",
"args": ["yaml-ai-mcp"]
}
}
}Or: pip install yaml-ai-mcp then run the yaml-ai-mcp command (stdio transport).
Once configured, ask your assistant, for example:
"Use validate_yaml to ā¦"
"Use convert_yaml_json to ā¦"
"Use lint_yaml to ā¦"
Convert between YAML and JSON formats.
Args: content: Input content string direction: Conversion direction - 'yaml_to_json' or 'json_to_yaml'
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No | ||
| content | Yes | ||
| direction | No | yaml_to_json |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description only states the function without disclosing behavioral traits like error handling, data preservation (comments/order), limitations, or output details. Minimal transparency.
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?
Two sentences plus a brief argument list. Front-loaded purpose. No fluff every sentence earns its place.
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?
Covers main purpose and two of three params. Missing explanation for api_key, output format, error handling, and limitations. Adequate but with clear gaps given no output schema or annotations.
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 description coverage is 0%. The description explains 'content' as input string and 'direction' as conversion direction, but omits explanation for 'api_key'. Adds some meaning but incomplete.
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 'Convert between YAML and JSON formats' with specific directions. It distinguishes from sibling tools like lint_yaml, merge_yaml, and validate_yaml by focusing on conversion.
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 implies use when conversion is needed but does not explicitly state when not to use or mention alternatives. No exclusions or context for when to choose this over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Lint YAML content for style issues and best practices.
Args: content: YAML string to lint
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No | ||
| content | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose side effects, but it only states the action. It does not clarify whether the operation modifies the content, requires authentication (api_key unexplained), or has rate limits. This is insufficient for an unannotated tool.
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 brief and front-loaded, with no redundant phrases. The args section adds minimal structure. However, it could be better organized with a clear parameter list and explanations.
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?
Despite low complexity, the description omits return value format, list of checks performed, and behavior of the api_key parameter. The tool is under-described for an agent to use confidently without additional context.
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?
Only the 'content' parameter is explained ('YAML string to lint'). The 'api_key' parameter has no description in schema or text, leaving its purpose unclear. With 0% schema coverage, the description partially compensates for one parameter but ignores the other.
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 states it lints YAML content for style issues and best practices, which is a specific verb and resource. It differentiates from sibling tools like validate_yaml (syntax correctness) and convert_yaml_json (format conversion). However, it could be more explicit about the exact style rules checked.
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?
No guidance on when to use lint over validate or merge. The description lacks context about prerequisites or scenarios where linting is appropriate, leaving the agent without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Merge two YAML documents together.
Args: yaml_a: First YAML document (base) yaml_b: Second YAML document (overlay) strategy: Merge strategy - 'deep' (recursive merge) or 'shallow' (top-level only)
| Name | Required | Description | Default |
|---|---|---|---|
| yaml_a | Yes | ||
| yaml_b | Yes | ||
| api_key | No | ||
| strategy | No | deep |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It explains base/overlay roles and strategy definitions, but does not disclose behaviors like conflict resolution, comment handling, or the purpose of the `api_key` parameter. This leaves gaps for an AI agent.
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 concise with a clear main sentence and bullet-like argument explanations. It is front-loaded and avoids verbosity, though it could be slightly tighter.
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 no output schema, no annotations, and 0% schema coverage, the description provides the core functionality and explains most parameters. However, the unexplained `api_key` and lack of output details or error handling make it only moderately complete.
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 description coverage is 0%, so the description must compensate. It adds meaning for `yaml_a` (base), `yaml_b` (overlay), and `strategy` (deep/shallow). However, the `api_key` parameter is left unexplained, limiting completeness.
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 'Merge two YAML documents together' with specific verb and resource. It distinguishes from siblings like convert_yaml_json, lint_yaml, and validate_yaml 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the arguments and strategy options, providing context for usage. However, it does not explicitly state when not to use or mention alternatives among siblings, though the different tool names imply distinct use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate YAML syntax and report any errors.
Args: content: YAML string to validate
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No | ||
| content | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description is entirely responsible for behavioral disclosure. It states it reports errors but fails to explain how (e.g., return format, exceptions). It also omits mention of side effects, permissions, or the unexplained api_key parameter, which may be required for validation.
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 relatively concise (one sentence plus an Args list) and front-loaded with the core purpose. However, it is incomplete, lacking explanation for api_key and the return format. It could be improved without being much longer.
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 absence of annotations, output schema, and a second undocumented parameter, the description is incomplete. It does not specify the validation results format, does not handle the api_key parameter, and provides no usage context relative to siblings. The tool's low complexity means a brief description could suffice, but current version misses key details.
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 description coverage is 0%, so the description must compensate. It describes the content parameter ('YAML string to validate') in an Args block, adding some meaning. However, the api_key parameter is completely undocumented, leaving its purpose unclear. With two parameters, only one is explained.
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's purpose: 'Validate YAML syntax and report any errors.' It uses a specific verb (validate) and resource (YAML syntax), and naturally distinguishes from siblings like convert_yaml_json (conversion) and merge_yaml (merging). The distinction from lint_yaml is implicit but clear enough.
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 provides no guidance on when to use this tool versus alternatives like lint_yaml or convert_yaml_json. It does not mention prerequisites, use cases, or scenarios where this tool is preferred, leaving the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0convert_yaml_jsonlint_yamlmerge_yamlvalidate_yamlScored across 4 tools
Each tool has a clearly distinct purpose: conversion between YAML and JSON, linting for style issues, merging two YAML documents, and validation for syntax errors. There is no overlap in functionality.
All tool names follow a consistent verb_noun pattern (convert_yaml_json, lint_yaml, merge_yaml, validate_yaml), making it predictable for an agent to infer tool behavior.
With 4 tools, the toolkit is well-scoped for a YAML processing server. Each tool addresses a core operation without unnecessary redundancy.
The set covers essential YAML operations: conversion, validation, linting, and merging. A minor gap is the absence of a dedicated formatting/beautification tool, but linting partially addresses this.
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