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TypeScript Definitions MCP Server

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TypeScript Definitions MCP Server

Supercharge your test mocking with intelligent TypeScript type definitions

The Problem

When writing unit and integration tests with AI assistance, you've probably experienced this frustration:

// You ask Claude Code: "Help me mock this API response"
// But Claude doesn't know the exact shape of your types...

const mockUserResponse = {
  id: 1,
  name: "John", 
  // ❌ What other properties should this have?
  // ❌ What's the exact type structure from my packages?
  // ❌ Am I missing required fields?
};

Before this tool, I found myself constantly:

  • Copy-pasting type definitions from node_modules into prompts

  • Manually looking up package types to write proper mocks

  • Getting incomplete test mocks because AI couldn't see the full type structure

  • Wasting time on back-and-forth to get the types right

Related MCP server: TypeScript LSP MCP

The Solution

This MCP (Model Context Protocol) server gives Claude Code instant access to your project's TypeScript definitions—no more hunting through node_modules or incomplete mocks.

Works with ANY TypeScript project: React, Vue, Angular, Node.js, whatever you're building. Just point it at your project directory and it automatically discovers all your dependencies.

Now your AI assistant can: ✅ See exact type structures from any package in your project
✅ Generate complete, type-safe mocks for your tests
✅ Understand your project's interfaces automatically
✅ Validate mock data against real type definitions
✅ Work with your specific package versions - no generic examples

Quick Example

Before (manual type hunting):

// You: "Help me mock an axios response"
// Claude: "Here's a basic mock..." (incomplete, might be wrong)

const mockResponse = {
  data: { /* ??? what goes here? */ },
  status: 200
  // Missing properties? Wrong structure?
};

After (with this MCP server):

// You: "Help me mock an axios response for a User type"
// Claude automatically knows the full AxiosResponse<T> structure:

const mockResponse: AxiosResponse<User> = {
  data: {
    id: 1,
    name: "John Doe",
    email: "john@example.com",
    createdAt: "2023-01-01T00:00:00Z"
  },
  status: 200,
  statusText: "OK",
  headers: {},
  config: {} as InternalAxiosRequestConfig,
  request: {}
};

Installation & Setup

Step 1: Clone and Build

git clone https://github.com/blake-yoder/typescript-definitions-mcp.git
cd typescript-definitions-mcp
npm install
npm run build

Step 2: Install Globally

npm install -g .

This makes the typescript-definitions-mcp command available globally.

Step 3: Install in Claude Code

The easiest way to install is using the Claude Code MCP command:

claude mcp add typescript-definitions -- typescript-definitions-mcp

This automatically configures the MCP server for use in Claude Code.

Alternative Configuration Options

If you prefer manual configuration:

Option A: User-Wide (Works in all projects)

Create or edit ~/.claude/mcp_servers.json:

macOS/Linux:

{
  "typescript-definitions": {
    "command": "typescript-definitions-mcp",
    "args": []
  }
}

Windows: Edit %APPDATA%\claude\mcp_servers.json:

{
  "typescript-definitions": {
    "command": "typescript-definitions-mcp.cmd",
    "args": []
  }
}

Option B: Project-Specific

In your TypeScript project root, create .mcp.json:

{
  "mcpServers": {
    "typescript-definitions": {
      "command": "typescript-definitions-mcp",
      "args": []
    }
  }
}

Or if using local build:

{
  "mcpServers": {
    "typescript-definitions": {
      "command": "node",
      "args": ["/absolute/path/to/typescript-definitions-mcp/build/index.js"]
    }
  }
}

Step 4: Restart Claude Code

Completely quit and restart Claude Code for the MCP server to load.

Step 5: Test It Out

Open Claude Code in any TypeScript project and try:

"Help me create a mock for a React component that uses these props: ButtonProps from my UI library"

"What's the exact structure of an AxiosResponse? I need to mock it for testing"

"Show me all the interfaces in this project that end with 'Config'"

Real-World Usage Examples

🧪 Test Mocking Made Easy

You: "I need to mock a jest.SpyInstance for testing. What's the exact type structure?"

Claude Code with MCP: Instantly knows jest.SpyInstance<ReturnType, Args> and helps you create:

const mockFn = jest.fn() as jest.SpyInstance<Promise<User>, [number]>;
mockFn.mockResolvedValue({
  id: 1,
  name: "Test User",
  email: "test@example.com"
});

🔌 API Response Mocking

You: "Help me mock a complete axios error response for my error handling tests"

Claude Code: Now sees the full AxiosError structure and creates proper mocks:

const mockAxiosError: AxiosError = {
  message: "Network Error",
  name: "AxiosError",
  code: "NETWORK_ERROR",
  config: {} as InternalAxiosRequestConfig,
  request: {},
  response: {
    data: { error: "Service unavailable" },
    status: 503,
    statusText: "Service Unavailable",
    headers: {},
    config: {} as InternalAxiosRequestConfig,
    request: {}
  },
  isAxiosError: true,
  toJSON: () => ({})
};

⚛️ React Component Testing

You: "I'm testing a component that takes complex props. Help me create comprehensive test data."

Claude Code: Analyzes your component's prop interface and generates complete mock data:

// Claude knows your exact ButtonProps interface
const mockProps: ButtonProps = {
  variant: "primary",
  size: "medium", 
  disabled: false,
  loading: false,
  onClick: jest.fn(),
  children: "Test Button",
  className: "test-class",
  "data-testid": "button-test"
};

🏗 Complex Library Integration

You: "I'm using react-hook-form and need to mock the useForm return value. What's the complete structure?"

Claude Code: Understands UseFormReturn<T> and creates accurate mocks:

const mockUseForm: UseFormReturn<FormData> = {
  register: jest.fn(),
  handleSubmit: jest.fn(),
  formState: {
    errors: {},
    isValid: true,
    isSubmitting: false,
    isDirty: false,
    dirtyFields: {},
    touchedFields: {},
    isSubmitted: false,
    submitCount: 0
  },
  control: {} as Control<FormData>,
  // ... all other UseFormReturn properties
};

Why This Matters

Before: Manual Type Hunting

  • 🕐 Time wasted digging through node_modules

  • 😤 Frustrating copy-paste workflows

  • ❌ Incomplete mocks that break tests

  • 🐛 Type mismatches in test data

After: AI-Powered Type Intelligence

  • ⚡ Instant type lookup and mock generation

  • 🎯 Accurate test data that matches real types

  • 🛡 Type-safe mocks prevent runtime errors

  • 🚀 Faster test development workflow

Available Tools

The MCP server provides these tools to Claude Code:

  • lookup_type - Find specific interfaces, types, or classes

  • find_interfaces - Search for interfaces using patterns (e.g., *Props, User*)

  • get_package_types - Get all exported types from a specific package

  • validate_type_usage - Check if your code matches expected types

  • check_type_compatibility - Verify if two types are compatible

Advanced Configuration

Team Setup

For teams, commit the project-specific configuration to your repository so everyone gets the same setup:

# In your project root
cat > .mcp.json << EOF
{
  "mcpServers": {
    "typescript-definitions": {
      "command": "typescript-definitions-mcp",
      "args": []
    }
  }
}
EOF

# Commit to your repo
git add .mcp.json
git commit -m "Add TypeScript Definitions MCP server for team"

Now everyone on your team will have TypeScript intelligence when they open the project in Claude Code.

Performance Optimization

For large codebases:

{
  "typescript-definitions": {
    "command": "typescript-definitions-mcp",
    "args": ["--exclude-patterns", "**/*.test.ts,**/dist/**"],
    "env": {
      "NODE_OPTIONS": "--max-old-space-size=4096"
    }
  }
}

Troubleshooting

MCP server not connecting?

  1. Verify the JSON syntax in mcp_servers.json

  2. Check that typescript-definitions-mcp is in your PATH

  3. Restart Claude Code completely

  4. Test with: typescript-definitions-mcp --version

Not finding types in your project?

  • Make sure you're running Claude Code from your TypeScript project directory

  • Check that tsconfig.json exists in your project

  • Verify your project builds with npx tsc --noEmit

Contributing

Built with ❤️ by Blake Yoder

Found a bug or have a feature request? Open an issue or submit a PR!

License

MIT License - see LICENSE file for details.

Transform your TypeScript testing workflow today. No more manual type hunting, no more incomplete mocks. Just intelligent, type-safe test development with Claude Code.

Available Tools

8 tools
check_type_compatibilityC

Check if two types are compatible/assignable

ParametersJSON Schema
NameRequiredDescriptionDefault
sourceTypeYesThe source type
targetTypeYesThe target type

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only states what the tool does ('check if two types are compatible/assignable') but doesn't reveal any behavioral traits such as whether it's a read-only operation, what the output format might be, error conditions, or performance characteristics. This is inadequate 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise—a single sentence that directly states the tool's purpose without any fluff. It's front-loaded with the core functionality and wastes no words. Every word earns its place in this minimal description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there are no annotations and no output schema, the description is incomplete. It doesn't explain what 'compatible/assignable' means in this context, what the return value might be (e.g., boolean, detailed report), or any error handling. For a tool that performs type checking—which can be complex—this description lacks necessary context for effective use by an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with both parameters ('sourceType' and 'targetType') documented as 'The source type' and 'The target type' respectively. The description adds no additional parameter semantics beyond what the schema already provides. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description.

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 the tool's purpose: 'Check if two types are compatible/assignable'. It specifies the verb ('check') and the resource/operation (type compatibility). However, it doesn't differentiate this tool from its siblings like 'validate_interface_implementation' or 'validate_type_usage', which might involve similar type-checking operations.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, context for usage, or how it differs from sibling tools like 'validate_type_usage' or 'lookup_type'. The agent must infer usage based solely on the tool name and description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

find_interfacesC

Find interfaces matching a pattern

ParametersJSON Schema
NameRequiredDescriptionDefault
patternYesPattern to match interface names (supports wildcards with *)

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions pattern matching with wildcards, but doesn't disclose whether this is a read-only operation, what permissions are needed, how results are returned, or any rate limits. For a search tool with zero annotation coverage, this is inadequate.

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, efficient sentence with zero wasted words. It's appropriately sized for a simple search tool and gets straight to the point without unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a search tool with no annotations and no output schema, the description is insufficient. It doesn't explain what constitutes an 'interface' in this context, what format results are returned in, or how pattern matching behaves beyond the basic wildcard mention. The agent lacks critical context for proper tool selection and use.

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 the single 'pattern' parameter with its wildcard support. The description adds no additional parameter semantics beyond what's in the schema, meeting the baseline for high schema coverage.

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 the verb 'find' and resource 'interfaces matching a pattern', which is specific and actionable. It doesn't explicitly distinguish from siblings like 'lookup_type' or 'validate_interface_implementation', but the focus on pattern-based searching provides reasonable differentiation.

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?

The description provides no guidance on when to use this tool versus alternatives like 'lookup_type' or 'validate_interface_implementation'. There's no mention of prerequisites, context, or comparison with sibling tools, leaving the agent to guess based on tool names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_package_typesC

Get all type definitions from a specific package

ParametersJSON Schema
NameRequiredDescriptionDefault
packageNameYesName of the package to get types from

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It doesn't disclose whether this is a read-only operation, what format the type definitions are returned in, if there are pagination limits, or authentication requirements. 'Get' implies a safe read, but this isn't explicitly stated.

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, efficient sentence with zero wasted words. It's front-loaded with the core action and resource, making it immediately understandable. No structural issues or redundancy are present.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'type definitions' include (e.g., classes, interfaces, enums), the return format, error conditions, or how it differs from siblings. The agent lacks critical context for effective use.

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 parameter 'packageName' is already documented in the schema. The description adds no additional semantic context about parameter usage, constraints, or examples. It meets the baseline for high schema coverage but doesn't enhance understanding.

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 the verb 'Get' and the resource 'all type definitions from a specific package', making the purpose unambiguous. It doesn't explicitly differentiate from sibling tools like 'lookup_type' or 'check_type_compatibility', but the scope ('all type definitions') provides some implicit distinction.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, when-not-to-use scenarios, or compare to siblings like 'lookup_type' (for single types) or 'find_interfaces' (for interface-specific queries). The agent must 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_project_infoB

Get information about the current TypeScript project

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.2/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'gets information,' implying a read-only operation, but doesn't specify what kind of information (e.g., configuration, dependencies, structure) or any constraints like performance, permissions, or output format, leaving significant gaps.

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, clear sentence that directly states the tool's purpose without any unnecessary words. It's front-loaded and appropriately sized for a simple tool, making it highly efficient and easy to understand.

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 the tool's simplicity (0 parameters, no annotations, no output schema), the description is minimally adequate but lacks depth. It doesn't explain what 'information' includes or how it relates to sibling tools, which could leave the agent uncertain about when and how to use it effectively in 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?

The tool has 0 parameters, and schema description coverage is 100%, so there's no need for parameter documentation in the description. The description appropriately doesn't discuss parameters, which is efficient, but it doesn't add any semantic context beyond the schema, so it doesn't reach the highest score.

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 the action ('Get information') and the target resource ('current TypeScript project'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its siblings like 'get_package_types' or 'lookup_type', which might also provide project-related information, so it doesn't reach the highest score.

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?

The description provides no guidance on when to use this tool versus alternatives. With siblings like 'get_package_types' and 'lookup_type' that might overlap in functionality, there's no indication of when this tool is preferred or what specific context it serves, leaving usage ambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

lookup_typeC

Look up TypeScript type definitions by name and optional package

ParametersJSON Schema
NameRequiredDescriptionDefault
typeNameYesThe name of the type to look up
packageNameNoOptional package name to filter results

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool looks up type definitions but doesn't describe what 'look up' entails—whether it returns documentation, source code, metadata, or something else. It doesn't mention error handling, rate limits, authentication needs, or whether results are cached. For a tool with no annotations, this leaves significant behavioral gaps.

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, efficient sentence with zero waste. It's front-loaded with the core purpose and includes key details (TypeScript, name, optional package) without unnecessary elaboration. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

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., type signatures, documentation, or metadata), how results are formatted, or any limitations (e.g., only works for indexed packages). For a lookup tool with no structured output information, 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.

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 both parameters ('typeName' and 'packageName') with clear descriptions. The description adds marginal value by mentioning the optional package filter but doesn't provide additional syntax, format details, or examples beyond what the schema provides. Baseline 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.

Purpose4/5

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 ('look up') and resource ('TypeScript type definitions'), and specifies the lookup criteria ('by name and optional package'). However, it doesn't explicitly differentiate this from sibling tools like 'get_package_types' or 'find_interfaces', which might offer similar functionality.

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?

The description provides no guidance on when to use this tool versus alternatives like 'get_package_types' or 'find_interfaces'. It mentions the optional package parameter but doesn't explain when to include it or what happens if omitted. No prerequisites, exclusions, or comparison to siblings are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

reinitialize_indexerB

Reinitialize the type indexer (useful after package installations)

ParametersJSON Schema
NameRequiredDescriptionDefault
workingDirNoOptional working directory to reinitialize from

TDQS

B3.3/5.0
Behavior2/5

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 the tool is 'useful after package installations,' which hints at a specific scenario, but lacks critical behavioral details: whether it's destructive (e.g., clears existing index data), requires permissions, has side effects (e.g., temporary downtime), or what the expected outcome is (e.g., success/failure indicators). For a tool with no annotations, this is a significant gap.

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, efficient sentence that states the purpose and provides a use case without any fluff. It's front-loaded with the main action and resource, making it easy to parse. Every word earns its place, and there's no wasted text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (likely a system operation with potential side effects), no annotations, and no output schema, the description is incomplete. It fails to explain what 'reinitialize' entails behaviorally (e.g., does it rebuild from scratch, clear caches?), what happens on success or error, or any prerequisites. For a tool with no structured safety or output info, the description should do more to guide safe usage.

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 input schema has 100% description coverage, with one optional parameter 'workingDir' clearly documented. The description doesn't add any parameter details beyond the schema, but with high coverage and only one parameter, the baseline is strong. Since there are no parameters to compensate for, a score of 4 reflects that the schema alone is sufficient, and the description doesn't detract from it.

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 the action ('reinitialize') and target ('the type indexer'), and provides a specific use case ('after package installations'). It distinguishes from siblings like 'check_type_compatibility' or 'get_package_types' by focusing on resetting/refreshing the indexer rather than querying or validating types. However, it doesn't explicitly contrast with all siblings, so it's not a perfect 5.

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 when to use it ('after package installations'), which gives some context. However, it doesn't provide explicit alternatives (e.g., when to use this vs. other tools like 'get_package_types'), nor does it specify when not to use it (e.g., during active development without package changes). The guidance is helpful but incomplete.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

validate_interface_implementationC

Validate if code correctly implements an interface

ParametersJSON Schema
NameRequiredDescriptionDefault
implementationYesThe implementation code to validate
interfaceNameYesName of the interface being implemented
interfaceDefinitionYesThe interface definition

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool validates code against an interface but does not explain how validation works (e.g., static analysis, runtime checks), what constitutes success/failure, error handling, or any side effects. For a validation 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, direct sentence: 'Validate if code correctly implements an interface.' It is front-loaded, with no unnecessary words, making it highly efficient and easy to understand. Every part of the sentence earns its place by clearly stating the tool's function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of code validation and the lack of annotations and output schema, the description is insufficient. It does not cover behavioral aspects like validation methods, result formats, or error conditions. For a tool with three parameters and no structured output information, 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.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with clear parameter descriptions (e.g., 'The implementation code to validate'). The description adds no additional meaning beyond what the schema provides, as it does not elaborate on parameter interactions or usage nuances. Given the high schema coverage, a baseline score of 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 the tool's purpose: 'Validate if code correctly implements an interface.' It specifies the verb (validate) and the resource (code implementing an interface), making the function unambiguous. However, it does not explicitly differentiate from sibling tools like 'check_type_compatibility' or 'validate_type_usage,' which might have overlapping purposes, so it falls short of a perfect score.

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?

The description provides no guidance on when to use this tool versus alternatives. With siblings such as 'check_type_compatibility' and 'validate_type_usage,' there is no indication of specific contexts, prerequisites, or exclusions for using this tool. This lack of differentiation leaves the agent without clear usage instructions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

validate_type_usageC

Validate TypeScript code for type correctness

ParametersJSON Schema
NameRequiredDescriptionDefault
codeYesThe TypeScript code to validate
expectedTypeNoOptional expected type to validate against

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool validates type correctness, implying it performs a read-only analysis, but doesn't specify what happens during validation (e.g., error reporting, success indicators, or side effects). For a tool with zero annotation coverage, this is insufficient, as it misses details like output format or potential limitations.

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, direct sentence: 'Validate TypeScript code for type correctness.' It is front-loaded with the core purpose, has no unnecessary words, and efficiently conveys the tool's function without waste, earning a score of 5 for optimal conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of type validation, the lack of annotations, no output schema, and 100% schema coverage, the description is incomplete. It doesn't explain what the validation entails, how results are returned, or any behavioral traits. For a tool that likely produces detailed output (e.g., errors or type mismatches), this leaves significant gaps, scoring 2.

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 both parameters ('code' and 'expectedType') with clear descriptions. The description adds no additional meaning beyond what the schema provides, such as syntax examples or validation rules. According to the rules, with high schema coverage (>80%), the baseline is 3, which is appropriate here.

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 the tool's purpose: 'Validate TypeScript code for type correctness.' It specifies the verb ('validate') and resource ('TypeScript code'), making the function unambiguous. However, it doesn't explicitly differentiate from siblings like 'check_type_compatibility' or 'validate_interface_implementation,' which might have overlapping purposes, so it doesn't reach a score of 5.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any context, prerequisites, or exclusions, and with siblings like 'check_type_compatibility' and 'validate_interface_implementation' available, the lack of differentiation leaves usage unclear. This is a minimal level of guidance, scoring 2.

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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TDQS

B3.4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: checking type compatibility, finding interfaces, getting package types, getting project info, looking up types, reinitializing the indexer, validating interface implementation, and validating type usage. The descriptions make it easy for an agent to select the right tool without confusion.

Naming Consistency4/5

The naming follows a consistent verb_noun pattern (e.g., check_type_compatibility, find_interfaces, get_package_types) with one minor deviation: 'lookup_type' uses 'lookup' instead of 'look_up' or 'get', but it still fits the overall style. The pattern is predictable and readable throughout.

Tool Count5/5

With 8 tools, the count is well-scoped for a TypeScript definitions server. Each tool serves a specific, useful function in the domain of type checking and project management, with no redundancy. This number allows comprehensive coverage without being overwhelming.

Completeness4/5

The tool set covers core TypeScript definition workflows well, including type lookup, compatibility checks, interface handling, project info, and validation. A minor gap is the lack of tools for modifying or generating types (e.g., create_type or update_definition), but agents can work around this with the provided tools for most common tasks.

Maintenance

ActivityInactive
ResponsivenessNo issues

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