Vale MCP Server
Adds Vale prose linting tools to GitHub Copilot in VS Code, allowing developers to check documents for style and grammar issues directly within their development environment
Provides Vale prose linting functionality through the Gemini command-line tool, enabling style and grammar checking of text files
Click on "Install 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., "@Vale MCP Servercheck this README for style issues"
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.
I am now co-developinghttps://github.com/ChrisChinchilla/Vale-MCP together with @ChrisW. Head over there for new releases!
AI usage disclosure
I've created this project using Claude Code with the Claude 4.5 model.
Related MCP server: MCP Code Checker
License
This project is licensed under the MIT License. Refer to the LICENSE file for details.
Available Tools
3 toolscheck_fileA
Lint a file at a specific path against Vale style rules. Returns issues found with their locations and severity. If Vale is not installed, returns error with installation guidance.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Absolute or relative path to the file to check |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behaviors: it returns issues with locations/severity, and handles the error case when Vale is not installed with guidance. It doesn't mention rate limits, authentication needs, or whether this is read-only vs destructive, but provides useful operational context.
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 efficient sentences with zero waste. First sentence states purpose and output, second handles error case. Every sentence earns its place with important operational information.
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?
For a single-parameter tool with no annotations and no output schema, the description provides good completeness: purpose, behavior, error handling. It could benefit from more detail about the return format (structure of issues) and whether this is a read-only operation, but covers the essential context well.
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 100% with the single 'path' parameter well-documented in the schema. The description adds minimal value beyond the schema by mentioning 'specific path' and 'file to check', but doesn't provide additional syntax, format details, or constraints beyond what's already in the schema.
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 specific action ('lint a file'), target resource ('file at a specific path'), and purpose ('against Vale style rules'). It distinguishes from sibling tools by focusing on file checking rather than status or synchronization operations.
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 usage context through the mention of Vale style rules and installation requirements, but doesn't explicitly state when to use this tool versus the 'vale_status' or 'vale_sync' siblings. No explicit alternatives or exclusions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
vale_statusA
Check if Vale (vale.sh) is installed and accessible. Use this first if other Vale tools fail. Returns installation status, version if available, and installation instructions for the current platform.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and effectively discloses behavioral traits: it describes what the tool returns ('installation status, version if available, and installation instructions for the current platform'), including output format and context for missing installations. It doesn't mention error handling or performance aspects, but covers core behavior well.
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 front-loaded with the main purpose, followed by usage guidance and return details in two concise sentences. Every sentence adds value without waste, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is complete enough: it explains the tool's purpose, usage context, and return values. It could slightly improve by specifying error cases or platform details, but it adequately covers the essential context for this simple diagnostic tool.
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?
Since there are 0 parameters and schema description coverage is 100%, the baseline is 4. The description adds no parameter information, which is appropriate given the lack of parameters, so it meets expectations without redundancy.
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 with specific verbs ('Check if Vale is installed and accessible') and resource ('Vale (vale.sh)'), distinguishing it from sibling tools like 'check_file' and 'vale_sync' by focusing on installation status rather than file validation or synchronization.
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?
It provides explicit usage guidance: 'Use this first if other Vale tools fail' indicates when to use this tool versus alternatives, and it implies a troubleshooting context, offering clear direction for the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
vale_syncA
Download Vale styles and packages by running 'vale sync'. Use this when you see errors about missing styles directories (E100 errors like 'The path does not exist'). This command reads the .vale.ini configuration and downloads the required style packages.
| Name | Required | Description | Default |
|---|---|---|---|
| config_path | No | Optional path to .vale.ini file. If not provided, uses the server's configured path or searches in the current directory. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that the tool downloads packages based on configuration and mentions it reads '.vale.ini', which is useful context. However, it doesn't disclose important behavioral aspects like whether this requires network access, what happens if downloads fail, or if there are any side effects on existing files.
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 perfectly concise with three sentences that each serve a distinct purpose: stating what the tool does, when to use it, and how it works. There's no redundancy or wasted words, and the information is front-loaded with the most important details first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (single optional parameter, no output schema, no annotations), the description provides good contextual coverage. It explains the purpose, usage scenario, and basic operation. The main gap is the lack of output information or error handling details, but for a configuration-based download tool, this is reasonably 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?
The schema description coverage is 100%, so the schema already documents the single parameter completely. The description doesn't add any additional parameter semantics beyond what's in the schema - it mentions the configuration file but doesn't provide additional context about the parameter's usage or implications. This meets the baseline expectation when schema coverage is high.
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 specific action ('Download Vale styles and packages') and the method ('by running "vale sync"'), distinguishing it from sibling tools like 'check_file' and 'vale_status'. It provides a concrete verb+resource combination that leaves no ambiguity about what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool ('Use this when you see errors about missing styles directories (E100 errors like "The path does not exist")'). It provides a clear trigger condition and distinguishes it from alternatives by specifying the specific error scenario it addresses.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
3 tool updates
- First observed
check_file - First observed
vale_status - First observed
vale_sync
TDQS
Each tool has a clearly distinct purpose: check_file performs linting, vale_status checks installation status, and vale_sync downloads styles. There is no overlap in functionality, making it easy for an agent to select the correct tool based on the task at hand.
All tool names follow a consistent verb_noun pattern with snake_case: check_file, vale_status, and vale_sync. The naming is predictable and readable, with no deviations in style or convention.
With 3 tools, the server is well-scoped for its purpose of Vale integration. Each tool earns its place by covering essential functions: installation check, style management, and linting, avoiding unnecessary complexity or redundancy.
The tool surface provides complete coverage for the Vale domain: vale_status handles setup verification, vale_sync manages style dependencies, and check_file performs the core linting operation. There are no obvious gaps, ensuring agents can handle the full workflow from installation to linting.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Research-backed linting + generation for agent context files (CLAUDE.md, AGENTS.md, Cursor rules).
Prose linter + AI-slop detector: weasel words, passive voice, hedging, and research-cited AI tells
Connect AI assistants to your GitHub-hosted Obsidian vault to seamlessly access, search, and analy…
Lints + auto-fixes how AI coding agents discover any new product. 24 rules, 6 tools, score 0-100.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceProvides AI assistants with the ability to lint, validate, and auto-fix Markdown files to ensure compliance with established Markdown standards and best practices.6MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI assistants to perform comprehensive code quality checks including pylint, pytest, and mypy analysis on Python projects, with smart prompts for explaining issues and suggesting fixes.18MIT
- AlicenseAqualityDmaintenanceEnables AI coding assistants like Claude Code to perform real-time code linting and get violation summaries from tools such as ESLint, Stylelint, P3C, and Checkstyle via a local server.320Apache 2.0
- AlicenseAqualityAmaintenanceEnables AI coding assistants to validate HTML/CSS markup using W3C APIs, perform technical SEO audits, check broken links, and validate JSON-LD schemas directly in local workspaces.28237MIT
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/theletterf/vale-mcp-server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server