The HooksMCP server provides a secure interface for coding agents to execute essential development tasks including:
• Testing - Run all tests or target specific files/directories by path or test name • Code Quality - Lint source code with automatic error/warning detection and fixing • Formatting - Check and format source code for consistency • Type Checking - Validate TypeScript code for type safety • Security - Execute commands with validated parameters, whitelisted commands, and path restrictions within project boundaries • Environment Management - Handle required/optional environment variables and execute commands in specific working directories (useful for mono-repos) • Command Control - Enforce execution timeouts and prevent shell injection attacks
This server enables coding agents to manage development workflows safely and efficiently through a comprehensive set of predefined tools and commands.
Supports loading environment variables from .env files for use in tool configurations and command execution
Enables running test suites through pytest, with support for targeting specific test files
Provides ability to execute Python-based tools and scripts, supporting project-specific development workflows
Uses YAML configuration files to define tools, parameters, and execution environments for the MCP server
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., "@HooksMCP run the typechecker on the current file"
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.
Simple setup: one YAML file is all it takes to create a custom MCP server for your coding agents. Similar to package.json scripts or Github Actions workflows, but commands are triggered by MCP functions.
Tool discovery: coding agents know which dev-tools are available and the exact arguments they require. No more guessing CLI strings.
Improved security: limit which commands agents can run. Validate the arguments agents generate (e.g. ensure a file path is inside the project).
Works anywhere MCP works: Cursor, Windsurf, Cline, etc
Prompt Library provide access to shared prompts in a standard way. Solves that Cursor/Cline/Codex all have different search paths/filenames.
Speed: using MCP unlocks parallel execution, requires fewer tokens for generating commands, and eliminates errors in commands requiring iteration.
Collaboration: Check in the YAML file to share with your team.
And more: strip ANSI codes/control characters, .env file loading, define required secrets without checking them in, supports exit codes/stdout/stderr, etc
Related MCP server: Command Executor MCP Server
Install with uv:
uv tool install hooks-mcpCreate an hooks_mcp.yaml file in your project root defining your tools and prompts. For example:
actions:
- name: "all_tests"
description: "Run all tests in the project"
command: "uv run python -m pytest ./tests"
- name: "check_format"
description: "Check if the source code is formatted correctly"
command: "uvx ruff format --check ."
- name: "typecheck"
description: "Typecheck the source code"
command: "uv run pyright ."
- name: "test_file"
description: "Run tests in a specific file or directory"
command: "python -m pytest $TEST_PATH"
parameters:
- name: "TEST_PATH"
type: "project_file_path"
description: "Path to test file or directory"
prompts:
- name: "test_guide.md"
description: "Guide for testing best practices in this library"
prompt-file: "agents/test_guide.md"
Run the server:
uvx hooks-mcpWe recommend running HooksMCP with uvx:
# Install
uv tool install hooks-mcp
# Run
uvx hooks-mcp Optional command line arguments include:
--working-directory/-wd: Typically the path to your project root. Set if not running from project root.
--http-streaming-port: Run the server with HTTP streaming on the specified port instead of stdio. The server will listen on http://localhost:<port>/mcp.
--disable-prompt-tool: Disable the get_prompt tool entirely, similar to setting get_prompt_tool_filter to an empty array.
The last argument is the path to the hooks_mcp.yaml file, if not using the default ./hooks_mcp.yaml
Or open this cursor deeplink.
Most other IDEs use a variant of mcp.json. Create an entry for HooksMCP.
Note: Be sure it's run from the root of your project, or manually pass the working directory on startup:
{
"HooksMCP": {
"command": "uvx",
"args": [
"hooks-mcp",
"--working-directory",
"."
]
}
}The hooks_mcp.yaml file defines the tools that will be exposed through the MCP server.
See this project's hooks_mcp.yaml as an example.
server_name (optional): Name of the MCP server (default: "HooksMCP")
server_description (optional): Description of the MCP server (default: "Project-specific development tools and prompts exposed via MCP")
actions (optional): Array of action definitions. If not provided, only prompts will be available.
prompts (optional): Array of prompt definitions
get_prompt_tool_filter (optional): Array of prompt names to expose via the get_prompt tool. If unset, all prompts are exposed. If empty, the get_prompt tool is not exposed.
Each action in the actions array can have the following fields:
name (required): Unique identifier for the tool
description (required): Human-readable description of what the tool does
command (required): The CLI command to execute. May include dynamic parameters like $TEST_FILE_PATH.
parameters (optional): Definitions of each parameter used in the command.
run_path (optional): Relative path from project root where the command should be executed. Useful for mono-repos.
timeout (optional): Timeout in seconds for command execution (default: 60 seconds)
Each parameter in an action's parameters array can have the following fields:
name (required): The parameter name to substitute into the command. For example TEST_FILE_PATH.
type (required): One of the following parameter types:
project_file_path: A local path within the project, relative to project root.
insecure_string: Any string from the model. No validation. Use with caution.
required_env_var: An environment variable that must be set before the server starts.
optional_env_var : An optional environment variable. Not specified by the calling model.
description (optional): description of the parameter
default (optional): Default value for the parameter if not passed
This parameter type ensures security by validating that the path parameter is within the project boundaries:
- name: "test_file"
description: "Run tests in a specific file"
command: "python -m pytest $TEST_FILE"
parameters:
- name: "TEST_FILE"
type: "project_file_path"
description: "Path to test file"
default: "./tests"Allows any string input from the agent without validation. Use with caution:
- name: "grep_code"
description: "Search code for pattern"
command: "grep -r $PATTERN src/"
parameters:
- name: "PATTERN"
type: "insecure_string"
description: "Pattern to search for"This is a tool parameter expected to exist as an environment variable. The server will fail to start if the environment is missing this var.
This is useful for specifying that a secret (e.g., API key) is needed, without checking the value into your repository. Typically set up when you configure your MCP server (in mcp.json and similar). When trying to set up the MCP server, it will output a user‑friendly message informing the user they need to add the env var to continue.
HooksMCP will load env vars from the environment, and any set in a .env file in your working directory.
This cannot be passed by the calling model.
- name: "deploy"
description: "Deploy the application"
command: "deploy-tool --key=$DEPLOY_KEY"
parameters:
- name: "DEPLOY_KEY"
type: "required_env_var"
description: "Deployment key for the service"Similar to required_env_var but optional. The server will not error on startup if this is missing.
- name: "build"
description: "Build the application"
command: "build-tool"
parameters:
- name: "BUILD_FLAGS"
type: "optional_env_var"
description: "Additional build flags"HooksMCP can be used to share prompts. For example, a "test_guide" prompt explaining preferred test libraries and best practices for tests.
Each prompt in the prompts array can have the following fields:
name (required): Unique identifier for the prompt (max 32 characters)
description (required): description of what the prompt does (max 256 characters)
prompt (optional): Inline prompt text content. Either prompt or prompt-file must be specified.
prompt-file (optional): Relative path to a file containing the prompt text content. Either prompt or prompt-file must be specified.
arguments (optional): Definitions of each argument used in the prompt.
Each argument in a prompt's arguments array can have the following fields:
name (required): The argument name
description (optional): description of the argument
required (optional): Boolean indicating if the argument is required (default: false)
To add a prompt in your template, include it in double curly brackets: {{CODE_SNIPPET}}
Prompts can be defined inline or loaded from files:
prompts:
- name: "code_review"
description: "Review code for best practices and potential bugs"
prompt: "Please review this code for best practices and potential bugs:\n\n{{CODE_SNIPPET}}"
arguments:
- name: "CODE_SNIPPET"
description: "The code to review"
required: true
- name: "architecture_review"
description: "Review system architecture decisions"
prompt-file: "./prompts/architecture_review.md"The MCP protocol supports prompts natively; HooksMCP will provide prompts through the official protocol.
However, many clients only support MCP for tool calls. They either completely ignore prompts, or only expose prompts via a dropdown requiring manual human selection. For these clients, we also expose a MCP tool called get_prompt. This tool automatically enabled when prompts are defined, allowing coding agents to retrieve prompt content by name. Note: the get_prompt tool does not support argument substitution. The model will have to infer how to use the prompt from it's template.
To disable the get_prompt tool you can set:
Use the --disable-prompt-tool CLI argument. This is local to each user.
set get_prompt_tool_filter in the yaml to limit which prompts are exposed, with an empty list disabling the tool. This is for all users.
get_prompt_tool_filter:
- "code_review"
- "architecture_review"HooksMCP implements several security measures to help improve security of giving agents access to terminal commands:
Allow List of Commands: Your agents can only run the commands you give it access to in your hooks_mcp.yaml, not arbitrary terminal commands.
Path Parameter Validation All project_file_path parameters are validated to ensure they:
Are within the project directory
Actually exist in the project
Environment Variable Controls:
required_env_var and optional_env_var parameters are managed by the developer, not the coding assistant. This prevents coding assistants from accessing sensitive variables.
Safe Command Execution:
Uses Python subprocess.run with shell=False to prevent shell injection
Uses shlex.split to properly separate command arguments
Implements timeouts to prevent infinite running commands
There are some risks to using HooksMCP:
If your agent can edit your hooks_mcp.yaml, it can add commands which it can then run via MCP
If your agent can add code to your project and any of your actions will invoke arbitrary code (like a test runner), the agent can use this pattern to run arbitrary code
HooksMCP may contain bugs or security issues
We don't promise it's perfect, but it's probably better than giving an agent unfettered terminal access. Running inside a container is always recommended for agents.
I built this for my own use building Kiln. The first draft was written by Qwen-Coder-405b, and then it was edited by me. See the initial commit for the prompt.
MIT
Run all tests in the project
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. 'Run all tests' implies an action that may have side effects (e.g., executing tests, generating reports), but it doesn't disclose behavioral traits like whether it's destructive, requires specific permissions, has rate limits, or what the output entails. This leaves significant gaps for a tool with no annotation coverage.
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 a single, clear sentence with zero wasted words. It's front-loaded with the core action, making it highly efficient and easy to parse, which is ideal for conciseness.
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 complexity (executing all tests, which could involve significant behavior) and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'running tests' entails, what results to expect, or any constraints, leaving the agent with insufficient context for safe and effective use.
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 input schema has 0 parameters with 100% coverage, meaning no parameters need documentation. The description doesn't add parameter details, which is appropriate here, and it implicitly confirms no inputs are required by not mentioning any. This meets the baseline for tools with no parameters.
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 'Run all tests in the project' clearly states the verb ('Run') and resource ('all tests in the project'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'test_file' or 'test_specific' that likely run subsets of tests, so it misses full sibling differentiation.
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 'test_file' or 'test_specific'. It lacks any context about prerequisites, when it's appropriate, or exclusions, leaving the agent to infer usage from tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check if the source code is formatted correctly
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool checks formatting but doesn't reveal what 'correctly' entails, whether it's read-only or has side effects, what permissions or context it requires, or what happens on failure. For a tool with zero annotation coverage, this is insufficient behavioral 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?
The description is a single, efficient sentence that states the core function without unnecessary words. It's appropriately sized for a simple tool, though it could be slightly more specific (e.g., mentioning code language or formatting standards) to improve clarity without losing conciseness.
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 lack of annotations and output schema, the description is incomplete for effective use. It doesn't explain what 'formatted correctly' means, what standards or rules are applied, what the return value indicates (e.g., pass/fail, detailed errors), or how it interacts with siblings. For a code quality tool in a server with multiple similar tools, more context is needed.
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 tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter information, and it appropriately doesn't mention any. This meets the baseline for tools with no parameters, as there's nothing to compensate for.
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 the tool's purpose as checking source code formatting correctness, which is clear but vague. It specifies the action ('check') and resource ('source code'), but doesn't distinguish it from siblings like 'format' or 'lint' that might handle similar concerns. The purpose is understandable but lacks specificity about what 'formatted correctly' means in this context.
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. With siblings like 'format', 'lint', 'lint_fix', and 'typecheck' that might overlap in code quality checking, there's no indication of when this specific formatting check is appropriate, what prerequisites exist, or what distinguishes it from other tools. This leaves usage decisions unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Format the source code
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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. 'Format the source code' implies a mutation operation that modifies code, but it doesn't specify whether this is destructive, reversible, requires specific permissions, or has side effects. It lacks details on output format, error handling, or any behavioral traits beyond the basic action.
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 a single, efficient sentence with zero waste—'Format the source code' is front-loaded and appropriately sized for a tool with no parameters. Every word contributes to the core purpose without redundancy or unnecessary elaboration.
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 complexity (implied mutation with no annotations) and lack of output schema, the description is incomplete. It doesn't explain what 'formatting' entails, what standards are applied, or what the return value might be (e.g., formatted code, success status). For a tool that likely modifies source code, more context is needed to guide effective use.
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 input schema has 0 parameters with 100% coverage, meaning there are no parameters to document. The description doesn't need to add parameter semantics, so it meets the baseline of 4 for tools with no parameters, as it doesn't contradict or omit any parameter information.
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 'Format the source code' states a clear verb ('Format') and resource ('source code'), but it's vague about scope and doesn't differentiate from sibling tools like 'check_format' or 'lint_fix'. It provides basic purpose but lacks specificity about what formatting entails or how it differs from related 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 offers no guidance on when to use this tool versus alternatives like 'check_format' (which might verify formatting) or 'lint_fix' (which might fix linting issues). There's no mention of prerequisites, context, or exclusions, leaving the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a prompt designed for this codebase. The prompts include:
test_guide.md: Guide for testing best practices in this library
code_analysis: Analyze code quality
| Name | Required | Description | Default |
|---|---|---|---|
| prompt_name | Yes | The name of the prompt to retrieve |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. The description states it 'gets' a prompt, implying a read operation, but doesn't specify whether this requires authentication, has rate limits, returns structured data or raw text, or what happens with invalid prompt names. For a tool with zero annotation coverage, this is inadequate behavioral 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?
The description is appropriately concise with two sentences. The first sentence states the core purpose, and the second provides specific examples in a bullet-like format. There's no wasted text, though the structure could be slightly improved by integrating the examples more smoothly.
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 annotations and no output schema, the description is incomplete. It doesn't explain what format the prompts are returned in (markdown text? structured data?), whether there are additional prompts beyond the two listed, or how this tool fits within the codebase context alongside sibling testing/analysis tools. For a tool in a development environment with multiple sibling tools, more contextual information would be helpful.
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 a single enum parameter clearly documented. The description lists the two available prompts ('test_guide.md' and 'code_analysis'), which aligns with the enum values but doesn't add meaningful semantic context beyond what the schema already provides. The baseline of 3 is appropriate when the schema does the heavy lifting.
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: 'Get a prompt designed for this codebase' with specific examples of what prompts are available. It uses a specific verb ('Get') and resource ('prompt'), but doesn't explicitly differentiate from sibling tools like 'all_tests' or 'check_format' which appear to be testing/analysis tools rather than prompt retrieval tools.
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. While it lists available prompts, it doesn't explain when to retrieve prompts versus using sibling tools like 'test_file' or 'code_analysis' (if that's a sibling tool's function). There's no mention of prerequisites, timing considerations, or alternative approaches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Lint the source code, checking for errors and warnings
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 states the tool checks for errors and warnings, but doesn't disclose behavioral traits such as whether it's read-only or destructive, what permissions are needed, how results are returned, or any rate limits. For a tool with zero annotation coverage, this is a significant gap 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Lint the source code') and adds clarifying detail ('checking for errors and warnings'). There is zero waste, and every word earns its place in conveying essential 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?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a report, status code, or list of issues), how to interpret results, or any side effects. For a tool that likely produces diagnostic output, this leaves critical gaps for an AI agent to use it effectively.
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 tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter details, and it doesn't contradict the schema. A baseline of 4 is appropriate since no parameters exist to explain.
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 a specific verb ('lint') and resource ('source code'), and specifies what it does ('checking for errors and warnings'). However, it doesn't explicitly distinguish this from sibling tools like 'lint_fix' or 'check_format', which likely have related functionality.
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_fix' (which might fix issues) or 'check_format' (which might check formatting). It doesn't mention prerequisites, context, or exclusions, leaving the agent to infer usage from tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Lint the source code, fixing errors and warnings which it can fix. Not all errors can be fixed automatically.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 key behavioral traits: the tool performs linting with automatic fixes, but not all errors are fixable. However, it lacks details on what types of errors are fixable, potential side effects (e.g., code modifications), or error handling, which are important for a mutation 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 two concise sentences that are front-loaded with the core purpose and followed by an important limitation. Every sentence earns its place by providing essential information without waste, making it highly efficient.
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 complexity (a mutation tool that modifies code), no annotations, and no output schema, the description is somewhat incomplete. It covers the basic action and a key limitation but lacks details on what gets fixed, how fixes are applied, or what the output looks like, which could hinder an agent's ability to use it correctly.
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 input schema has 0 parameters with 100% coverage, so no parameters need documentation. The description doesn't add parameter semantics, but this is acceptable given the lack of parameters. A baseline of 4 is appropriate as there are no parameters to explain.
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: 'Lint the source code, fixing errors and warnings which it can fix.' It specifies the verb (lint with fixing) and resource (source code). However, it doesn't explicitly differentiate from sibling tools like 'lint' or 'check_format', which likely perform similar but distinct functions.
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 by stating 'Not all errors can be fixed automatically,' suggesting this tool should be used when automatic fixes are desired but with limitations. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like 'lint' (which might only report issues) or 'format' (which might handle style fixes), leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run tests in a specific file or directory
| Name | Required | Description | Default |
|---|---|---|---|
| TEST_PATH | Yes | Path to test file or directory |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('Run tests') but does not explain what this entails—such as whether it executes tests, returns results, modifies files, requires specific environments, or has side effects like generating reports. This leaves significant gaps in understanding the tool's behavior beyond its basic purpose.
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 a single, clear sentence that efficiently conveys the tool's purpose without unnecessary words. It is front-loaded with the core action and target, making it easy to understand at a glance, and every part of the sentence contributes directly to the functional definition.
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 lack of annotations and output schema, the description is incomplete for a tool that performs an action like running tests. It does not cover behavioral aspects such as what the tool returns, how errors are handled, or dependencies required, leaving the agent with insufficient context to use the tool effectively beyond basic invocation.
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 input schema has 100% description coverage, with the parameter 'TEST_PATH' fully documented in the schema. The description adds no additional meaning beyond the schema, as it does not elaborate on parameter usage, formats, or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract from the schema's information.
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 action ('Run tests') and target ('in a specific file or directory'), providing a specific verb+resource combination. However, it does not explicitly differentiate from sibling tools like 'test_specific' or 'all_tests', which might offer similar testing functionality, leaving some ambiguity in sibling distinction.
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. It does not mention sibling tools like 'test_specific' or 'all_tests' for comparison, nor does it specify contexts, prerequisites, or exclusions for usage, offering only a basic functional statement without operational context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run a specific test by name
| Name | Required | Description | Default |
|---|---|---|---|
| TEST_PATH | No | Path to test file or directory | |
| TEST_NAME | Yes | Name of the test to run |
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 states the tool runs a test, implying execution and potential side effects, but doesn't disclose behavioral traits such as whether it's read-only, destructive, requires specific permissions, has rate limits, or what the output looks like. This is a significant gap for a tool that likely performs actions.
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 a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, clearly stating the core action without unnecessary elaboration, making it easy for an agent to parse quickly.
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 complexity of a test-running tool with no annotations and no output schema, the description is incomplete. It lacks details on behavior, output format, error handling, and how it differs from siblings. For a tool that likely involves execution, this minimal description is inadequate for proper agent usage.
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 input schema has 100% description coverage, with parameters 'TEST_PATH' and 'TEST_NAME' clearly documented. The description adds no additional meaning beyond what the schema provides, such as format examples or constraints. Baseline 3 is appropriate since the schema does the heavy lifting.
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 'Run a specific test by name' clearly states the verb ('Run') and resource ('a specific test'), but it's vague about what 'test' refers to and doesn't distinguish it from sibling tools like 'test_file' or 'all_tests'. It provides basic purpose but lacks specificity about scope or differentiation.
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 is provided on when to use this tool versus alternatives like 'test_file' or 'all_tests'. The description implies it runs a single test by name, but it doesn't specify prerequisites, exclusions, or contextual usage, leaving the agent without clear selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Typecheck the source code
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 only states the action without disclosing behavioral traits such as whether it modifies files, requires specific permissions, has side effects, or provides output details. This is inadequate for a tool with zero annotation coverage.
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 a single, efficient sentence with no wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly.
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 complexity (a code analysis tool with no annotations and no output schema), the description is incomplete. It doesn't explain what 'typecheck' entails, what the output might be, or how it fits with siblings, leaving significant gaps for the agent.
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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add param info, but this is acceptable given the lack of parameters, aligning with the baseline for 0 params.
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 'Typecheck the source code' states the action (typecheck) and target (source code), which is clear but vague. It doesn't specify what 'source code' refers to (current file, project, etc.) or how it differs from sibling tools like 'lint' or 'check_format', so it lacks sibling differentiation.
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 is provided on when to use this tool versus alternatives. With siblings like 'lint', 'check_format', and 'test_file', the description doesn't indicate if this is for static analysis, pre-compilation checks, or other contexts, leaving the agent without usage direction.
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. Dates show when Glama detected each change.
v1.0.0get_promptall_testscheck_formatformatlintlint_fixtest_filetest_specifictypecheckSome tools have overlapping purposes that could cause confusion, such as 'lint' and 'lint_fix' (where the distinction is clear but they target the same core function), and 'test_file' and 'test_specific' (both for running tests but with different scopes). However, descriptions help clarify differences, and most tools like 'format', 'typecheck', and 'code_analysis' are distinct in their functions.
The naming is mixed with some consistency issues: most tools use snake_case (e.g., 'all_tests', 'check_format'), but there are deviations like 'get_prompt' and 'test_guide.md' (which includes a file extension, breaking the pattern). Verb styles vary, with some using action verbs (e.g., 'format', 'lint') and others using descriptive nouns (e.g., 'code_analysis'), leading to a readable but inconsistent convention.
With 9 tools, the count is well-scoped for a code quality and testing server. Each tool appears to earn its place by covering distinct aspects like formatting, linting, testing, and analysis, without feeling overly heavy or thin for the domain.
The tool set provides good coverage for code quality workflows, including formatting, linting, testing, and analysis. Minor gaps exist, such as no explicit tool for code generation or dependency management, but core operations are well-represented, and agents can likely work around these omissions without significant failures.
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A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
A Model Context Protocol server for Wix AI tools
The Mercado Pago MCP Server implements the Model Context Protocol to provide AI agents and LLMs with access to Mercado Pago's APIs and tools within compatible development environments. It acts as an intermediary that translates Mercado Pago resources into executable functions (tools) that AI applications can invoke to perform actions and automate flows. The server simplifies integration, enables using documentation to implement or improve code, and optimizes operations through natural language interactions without manual implementations.
Enable secure connectivity between Sentry issues and debugging data, and LLM clients, using a Model Context Protocol (MCP) server.
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