wikipedia-mcp
Provides access to Wikipedia's content, allowing search for articles, retrieval of full article text, summaries, specific sections, link discovery, and related topic exploration, with support for multiple languages and regional variants.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@wikipedia-mcpFind a summary of the Wikipedia article on machine learning"
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.
Wikipedia MCP Server
A Model Context Protocol (MCP) server that retrieves information from Wikipedia to provide context to Large Language Models (LLMs). This tool helps AI assistants access factual information from Wikipedia to ground their responses in reliable sources.
Overview
The Wikipedia MCP server provides real-time access to Wikipedia information through a standardized Model Context Protocol interface. This allows LLMs to retrieve accurate and up-to-date information directly from Wikipedia to enhance their responses.
Related MCP server: mediawiki-mcp-server
Verified By
Features
Search Wikipedia: Find articles matching specific queries with enhanced diagnostics
Retrieve Article Content: Get full article text with all information
Article Summaries: Get concise summaries of articles
Section Extraction: Retrieve specific sections from articles
Link Discovery: Find links within articles to related topics
Related Topics: Discover topics related to a specific article
Multi-language Support: Access Wikipedia in different languages by specifying the
--languageor-largument when running the server (e.g.,wikipedia-mcp --language tafor Tamil).Country/Locale Support: Use intuitive country codes like
--country US,--country China, or--country TWinstead of language codes. Automatically maps to appropriate Wikipedia language variants.Language Variant Support: Support for language variants such as Chinese traditional/simplified (e.g.,
zh-hansfor Simplified Chinese,zh-twfor Traditional Chinese), Serbian scripts (sr-latn,sr-cyrl), and other regional variants.Optional caching: Cache API responses for improved performance using --enable-cache
Modern MCP Transport Support: Supports
stdio,http, andstreamable-http(with legacyssecompatibility).Optional MCP Transport Auth: Secure network transports with
--auth-mode staticor--auth-mode jwt.Google ADK Compatibility: Fully compatible with Google ADK agents and other AI frameworks that use strict function calling schemas
Installation
Using pipx (Recommended for Claude Desktop)
The best way to install for Claude Desktop usage is with pipx, which installs the command globally:
# Install pipx if you don't have it
pip install pipx
pipx ensurepath
# Install the Wikipedia MCP server
pipx install wikipedia-mcpThis ensures the wikipedia-mcp command is available in Claude Desktop's PATH.
Installing via Smithery
To install wikipedia-mcp for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @Rudra-ravi/wikipedia-mcp --client claudeFrom PyPI (Alternative)
You can also install directly from PyPI:
pip install wikipedia-mcpNote: If you use this method and encounter connection issues with Claude Desktop, you may need to use the full path to the command in your configuration. See the Configuration section for details.
Using a virtual environment
# Create a virtual environment
python3 -m venv venv
# Activate the virtual environment
source venv/bin/activate
# Install the package
pip install git+https://github.com/rudra-ravi/wikipedia-mcp.gitFrom source
# Clone the repository
git clone https://github.com/rudra-ravi/wikipedia-mcp.git
cd wikipedia-mcp
# Create a virtual environment
python3 -m venv wikipedia-mcp-env
source wikipedia-mcp-env/bin/activate
# Install in development mode
pip install -e .Usage
Running the server
# If installed with pipx
wikipedia-mcp
# If installed in a virtual environment
source venv/bin/activate
wikipedia-mcp
# Specify transport protocol (default: stdio)
wikipedia-mcp --transport stdio # For Claude Desktop
wikipedia-mcp --transport http --host 0.0.0.0 --port 8080 --path /mcp
wikipedia-mcp --transport streamable-http --host 0.0.0.0 --port 8080 --path /mcp
wikipedia-mcp --transport sse # Legacy compatibility transport
# Specify language (default: en for English)
wikipedia-mcp --language ja # Example for Japanese
wikipedia-mcp --language zh-hans # Example for Simplified Chinese
wikipedia-mcp --language zh-tw # Example for Traditional Chinese (Taiwan)
wikipedia-mcp --language sr-latn # Example for Serbian Latin script
# Specify country/locale (alternative to language codes)
wikipedia-mcp --country US # English (United States)
wikipedia-mcp --country China # Chinese Simplified
wikipedia-mcp --country Taiwan # Chinese Traditional (Taiwan)
wikipedia-mcp --country Japan # Japanese
wikipedia-mcp --country Germany # German
wikipedia-mcp --country france # French (case insensitive)
# List all supported countries
wikipedia-mcp --list-countries
# Optional: Specify host/port/path for network transport (use 0.0.0.0 for containers)
wikipedia-mcp --transport http --host 0.0.0.0 --port 8080 --path /mcp
# Optional: Enable caching
wikipedia-mcp --enable-cache
# Optional: Use Personal Access Token to avoid rate limiting (403 errors)
wikipedia-mcp --access-token your_wikipedia_token_here
# Or set via environment variable
export WIKIPEDIA_ACCESS_TOKEN=your_wikipedia_token_here
wikipedia-mcp
# Optional: Secure incoming MCP network requests with static bearer token
wikipedia-mcp --transport http --auth-mode static --auth-token your_mcp_token --host 0.0.0.0 --port 8080
# Optional: Secure incoming MCP network requests with JWT validation
wikipedia-mcp --transport http --auth-mode jwt --auth-jwks-uri https://issuer/.well-known/jwks.json --auth-issuer https://issuer
# Security note: prefer http/streamable-http + auth-mode for exposed network transport.
# Combine options
wikipedia-mcp --country Taiwan --enable-cache --access-token your_wikipedia_token --transport http --path /mcp --port 8080
### Docker/Kubernetes
When running inside containers, bind the HTTP MCP server to all interfaces and map
the container port to the host or service:
```bash
# Build and run with Docker
docker build -t wikipedia-mcp .
docker run --rm -p 8080:8080 wikipedia-mcp --transport http --host 0.0.0.0 --port 8080 --path /mcpKubernetes example (minimal):
apiVersion: apps/v1
kind: Deployment
metadata:
name: wikipedia-mcp
spec:
replicas: 1
selector:
matchLabels:
app: wikipedia-mcp
template:
metadata:
labels:
app: wikipedia-mcp
spec:
containers:
- name: server
image: your-repo/wikipedia-mcp:latest
args: ["--transport", "http", "--host", "0.0.0.0", "--port", "8080", "--path", "/mcp"]
ports:
- containerPort: 8080
---
apiVersion: v1
kind: Service
metadata:
name: wikipedia-mcp
spec:
selector:
app: wikipedia-mcp
ports:
- name: http
port: 8080
targetPort: 8080
### Configuration for Claude Desktop
Add the following to your Claude Desktop configuration file:
**Option 1: Using command name (requires `wikipedia-mcp` to be in PATH)**
```json
{
"mcpServers": {
"wikipedia": {
"command": "wikipedia-mcp"
}
}
}Option 2: Using full path (recommended if you get connection errors)
{
"mcpServers": {
"wikipedia": {
"command": "/full/path/to/wikipedia-mcp"
}
}
}Option 3: With country/language specification
{
"mcpServers": {
"wikipedia-us": {
"command": "wikipedia-mcp",
"args": ["--country", "US"]
},
"wikipedia-taiwan": {
"command": "wikipedia-mcp",
"args": ["--country", "TW"]
},
"wikipedia-japan": {
"command": "wikipedia-mcp",
"args": ["--country", "Japan"]
}
}
}To find the full path, run: which wikipedia-mcp
Configuration file locations:
macOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:
%APPDATA%/Claude/claude_desktop_config.jsonLinux:
~/.config/Claude/claude_desktop_config.json
Note: If you encounter connection errors, see the Troubleshooting section for solutions.
Documentation Index
CLI usage and options: see
docs/CLI.mdAPI and MCP tools/resources: see
docs/API.mdArchitecture overview: see
docs/ARCHITECTURE.mdUser guide and troubleshooting: see
docs/USER_GUIDE.mdDevelopment guide: see
docs/DEVELOPMENT.mdTesting guide: see
docs/TESTING.md
Available MCP Tools
The Wikipedia MCP server provides the following tools for LLMs to interact with Wikipedia:
Each tool is also exposed with a wikipedia_-prefixed alias (for example, wikipedia_get_article) for improved cross-server discoverability.
search_wikipedia
Search Wikipedia for articles matching a query.
Parameters:
query(string): The search termlimit(integer, optional): Maximum number of results to return (default: 10)
Returns:
A list of search results with titles, snippets, and metadata
get_article
Get the full content of a Wikipedia article.
Parameters:
title(string): The title of the Wikipedia article
Returns:
Article content including text, summary, sections, links, and categories
get_summary
Get a concise summary of a Wikipedia article.
Parameters:
title(string): The title of the Wikipedia article
Returns:
A text summary of the article
get_sections
Get the sections of a Wikipedia article.
Parameters:
title(string): The title of the Wikipedia article
Returns:
A structured list of article sections with their content
get_links
Get the links contained within a Wikipedia article.
Parameters:
title(string): The title of the Wikipedia article
Returns:
A list of links to other Wikipedia articles
get_coordinates
Get the coordinates of a Wikipedia article.
Parameters:
title(string): The title of the Wikipedia article
Returns:
A dictionary containing coordinate information including:
title: The article titlepageid: The page IDcoordinates: List of coordinate objects with latitude, longitude, and metadataexists: Whether the article existserror: Any error message if retrieval failed
get_related_topics
Get topics related to a Wikipedia article based on links and categories.
Parameters:
title(string): The title of the Wikipedia articlelimit(integer, optional): Maximum number of related topics (default: 10)
Returns:
A list of related topics with relevance information
summarize_article_for_query
Get a summary of a Wikipedia article tailored to a specific query.
Parameters:
title(string): The title of the Wikipedia articlequery(string): The query to focus the summary onmax_length(integer, optional): Maximum length of the summary (default: 250)
Returns:
A dictionary containing the title, query, and the focused summary
summarize_article_section
Get a summary of a specific section of a Wikipedia article.
Parameters:
title(string): The title of the Wikipedia articlesection_title(string): The title of the section to summarizemax_length(integer, optional): Maximum length of the summary (default: 150)
Returns:
A dictionary containing the title, section title, and the section summary
extract_key_facts
Extract key facts from a Wikipedia article, optionally focused on a specific topic within the article.
Parameters:
title(string): The title of the Wikipedia articletopic_within_article(string, optional): A specific topic within the article to focus fact extractioncount(integer, optional): Number of key facts to extract (default: 5)
Returns:
A dictionary containing the title, topic, and a list of extracted facts
Country/Locale Support
The Wikipedia MCP server supports intuitive country and region codes as an alternative to language codes. This makes it easier to access region-specific Wikipedia content without needing to know language codes.
Supported Countries and Regions
Use --list-countries to see all supported countries:
wikipedia-mcp --list-countriesThis will display countries organized by language, for example:
Supported Country/Locale Codes:
========================================
en: US, USA, United States, UK, GB, Canada, Australia, ...
zh-hans: CN, China
zh-tw: TW, Taiwan
ja: JP, Japan
de: DE, Germany
fr: FR, France
es: ES, Spain, MX, Mexico, AR, Argentina, ...
pt: PT, Portugal, BR, Brazil
ru: RU, Russia
ar: SA, Saudi Arabia, AE, UAE, EG, Egypt, ...Usage Examples
# Major countries by code
wikipedia-mcp --country US # United States (English)
wikipedia-mcp --country CN # China (Simplified Chinese)
wikipedia-mcp --country TW # Taiwan (Traditional Chinese)
wikipedia-mcp --country JP # Japan (Japanese)
wikipedia-mcp --country DE # Germany (German)
wikipedia-mcp --country FR # France (French)
wikipedia-mcp --country BR # Brazil (Portuguese)
wikipedia-mcp --country RU # Russia (Russian)
# Countries by full name (case insensitive)
wikipedia-mcp --country "United States"
wikipedia-mcp --country China
wikipedia-mcp --country Taiwan
wikipedia-mcp --country Japan
wikipedia-mcp --country Germany
wikipedia-mcp --country france # Case insensitive
# Regional variants
wikipedia-mcp --country HK # Hong Kong (Traditional Chinese)
wikipedia-mcp --country SG # Singapore (Simplified Chinese)
wikipedia-mcp --country "Saudi Arabia" # Arabic
wikipedia-mcp --country Mexico # SpanishCountry-to-Language Mapping
The server automatically maps country codes to appropriate Wikipedia language editions:
English-speaking: US, UK, Canada, Australia, New Zealand, Ireland, South Africa →
enChinese regions:
CN, China →
zh-hans(Simplified Chinese)TW, Taiwan →
zh-tw(Traditional Chinese - Taiwan)HK, Hong Kong →
zh-hk(Traditional Chinese - Hong Kong)SG, Singapore →
zh-sg(Simplified Chinese - Singapore)
Major languages: JP→
ja, DE→de, FR→fr, ES→es, IT→it, RU→ru, etc.Regional variants: Supports 140+ countries and regions
Error Handling
If you specify an unsupported country, you'll get a helpful error message:
$ wikipedia-mcp --country INVALID
Error: Unsupported country/locale: 'INVALID'.
Supported country codes include: US, USA, UK, GB, CA, AU, NZ, IE, ZA, CN.
Use --language parameter for direct language codes instead.
Use --list-countries to see supported country codes.Language Variants
The Wikipedia MCP server supports language variants for languages that have multiple writing systems or regional variations. This feature is particularly useful for Chinese, Serbian, Kurdish, and other languages with multiple scripts or regional differences.
Supported Language Variants
Chinese Language Variants
zh-hans- Simplified Chinesezh-hant- Traditional Chinesezh-tw- Traditional Chinese (Taiwan)zh-hk- Traditional Chinese (Hong Kong)zh-mo- Traditional Chinese (Macau)zh-cn- Simplified Chinese (China)zh-sg- Simplified Chinese (Singapore)zh-my- Simplified Chinese (Malaysia)
Serbian Language Variants
sr-latn- Serbian Latin scriptsr-cyrl- Serbian Cyrillic script
Kurdish Language Variants
ku-latn- Kurdish Latin scriptku-arab- Kurdish Arabic script
Norwegian Language Variants
no- Norwegian (automatically mapped to Bokmål)
Usage Examples
# Access Simplified Chinese Wikipedia
wikipedia-mcp --language zh-hans
# Access Traditional Chinese Wikipedia (Taiwan)
wikipedia-mcp --language zh-tw
# Access Serbian Wikipedia in Latin script
wikipedia-mcp --language sr-latn
# Access Serbian Wikipedia in Cyrillic script
wikipedia-mcp --language sr-cyrlHow Language Variants Work
When you specify a language variant like zh-hans, the server:
Maps the variant to the base Wikipedia language (e.g.,
zhfor Chinese variants)Uses the base language for API connections to the Wikipedia servers
Includes the variant parameter in API requests to get content in the specific variant
Returns content formatted according to the specified variant's conventions
This approach ensures optimal compatibility with Wikipedia's API while providing access to variant-specific content and formatting.
Example Prompts
Once the server is running and configured with Claude Desktop, you can use prompts like:
General Wikipedia queries:
"Tell me about quantum computing using the Wikipedia information."
"Summarize the history of artificial intelligence based on Wikipedia."
"What does Wikipedia say about climate change?"
"Find Wikipedia articles related to machine learning."
"Get me the introduction section of the article on neural networks from Wikipedia."
"What are the coordinates of the Eiffel Tower?"
"Find the latitude and longitude of Mount Everest from Wikipedia."
"Get coordinate information for famous landmarks in Paris."
Using country-specific Wikipedia:
"Search Wikipedia China for information about the Great Wall." (uses Chinese Wikipedia)
"Tell me about Tokyo from Japanese Wikipedia sources."
"What does German Wikipedia say about the Berlin Wall?"
"Find information about the Eiffel Tower from French Wikipedia."
"Get Taiwan Wikipedia's article about Taiwanese cuisine."
Language variant examples:
"Search Traditional Chinese Wikipedia for information about Taiwan."
"Find Simplified Chinese articles about modern China."
"Get information from Serbian Latin Wikipedia about Belgrade."
MCP Resources
The server also provides MCP resources (similar to HTTP endpoints but for MCP):
search/{query}: Search Wikipedia for articles matching the queryarticle/{title}: Get the full content of a Wikipedia articlesummary/{title}: Get a summary of a Wikipedia articlesections/{title}: Get the sections of a Wikipedia articlelinks/{title}: Get the links in a Wikipedia articlecoordinates/{title}: Get the coordinates of a Wikipedia articlesummary/{title}/query/{query}/length/{max_length}: Get a query-focused summary of an articlesummary/{title}/section/{section_title}/length/{max_length}: Get a summary of a specific article sectionfacts/{title}/topic/{topic_within_article}/count/{count}: Extract key facts from an article
Development
Local Development Setup
# Clone the repository
git clone https://github.com/rudra-ravi/wikipedia-mcp.git
cd wikipedia-mcp
# Create a virtual environment
python3 -m venv venv
source venv/bin/activate
# Install the package in development mode
pip install -e .
# Install development and test dependencies
pip install -r requirements-dev.txt
# Run the server
wikipedia-mcpProject Structure
wikipedia_mcp/: Main package__main__.py: Entry point for the packageserver.py: MCP server implementationwikipedia_client.py: Wikipedia API clientapi/: API implementationcore/: Core functionalityutils/: Utility functions
tests/: Test suitetest_basic.py: Basic package teststest_cli.py: Command-line interface teststest_server_tools.py: Comprehensive server and tool tests
Testing
The project includes a comprehensive test suite to ensure reliability and functionality.
Test Structure
The test suite is organized in the tests/ directory with the following test files:
test_basic.py: Basic package functionality teststest_cli.py: Command-line interface and transport teststest_server_tools.py: Comprehensive tests for all MCP tools and Wikipedia client functionality
Running Tests
Run All Tests
# Install test dependencies
pip install -r requirements-dev.txt
# Run all tests
python -m pytest tests/ -v
# Run tests with coverage
python -m pytest tests/ --cov=wikipedia_mcp --cov-report=htmlRun Specific Test Categories
# Run only unit tests (excludes integration tests)
python -m pytest tests/ -v -m "not integration"
# Run only integration tests (requires internet connection)
python -m pytest tests/ -v -m "integration"
# Run specific test file
python -m pytest tests/test_server_tools.py -vTest Categories
Unit Tests
WikipediaClient Tests: Mock-based tests for all client methods
Search functionality
Article retrieval
Summary extraction
Section parsing
Link extraction
Related topics discovery
Server Tests: MCP server creation and tool registration
CLI Tests: Command-line interface functionality
Integration Tests
Real API Tests: Tests that make actual calls to Wikipedia API
End-to-End Tests: Complete workflow testing
Test Configuration
The project uses pytest.ini for test configuration:
[pytest]
markers =
integration: marks tests as integration tests (may require network access)
slow: marks tests as slow running
testpaths = tests
addopts = -v --tb=shortContinuous Integration
All tests are designed to:
Run reliably in CI/CD environments
Handle network failures gracefully
Provide clear error messages
Cover edge cases and error conditions
Adding New Tests
When contributing new features:
Add unit tests for new functionality
Include both success and failure scenarios
Mock external dependencies (Wikipedia API)
Add integration tests for end-to-end validation
Follow existing test patterns and naming conventions
Troubleshooting
Common Issues
Claude Desktop Connection Issues
Problem: Claude Desktop shows errors like spawn wikipedia-mcp ENOENT or cannot find the command.
Cause: This occurs when the wikipedia-mcp command is installed in a user-specific location (like ~/.local/bin/) that's not in Claude Desktop's PATH.
Solutions:
Use full path to the command (Recommended):
{ "mcpServers": { "wikipedia": { "command": "/home/username/.local/bin/wikipedia-mcp" } } }To find your exact path, run:
which wikipedia-mcpInstall with pipx for global access:
pipx install wikipedia-mcpThen use the standard configuration:
{ "mcpServers": { "wikipedia": { "command": "wikipedia-mcp" } } }Create a symlink to a global location:
sudo ln -s ~/.local/bin/wikipedia-mcp /usr/local/bin/wikipedia-mcp
Other Issues
Article Not Found: Check the exact spelling of article titles
Rate Limiting: Wikipedia API has rate limits; consider adding delays between requests
Large Articles: Some Wikipedia articles are very large and may exceed token limits
Troubleshooting Search Issues
If you're experiencing empty search results, use the new diagnostic tools:
1. Test Connectivity
Use the test_wikipedia_connectivity tool to check if you can reach Wikipedia's API:
{
"tool": "test_wikipedia_connectivity"
}This returns diagnostics including:
Connection status (
successorfailed)Response time in milliseconds
Site/host information when successful
Error details when connectivity fails
2. Enhanced Search Error Information
The search_wikipedia tool now returns detailed metadata:
{
"tool": "search_wikipedia",
"arguments": {
"query": "Ada Lovelace",
"limit": 10
}
}Example response:
{
"query": "Ada Lovelace",
"results": [...],
"count": 5,
"status": "success",
"language": "en"
}When no results are found, you receive:
{
"query": "nonexistent",
"results": [],
"status": "no_results",
"count": 0,
"language": "en",
"message": "No search results found. This could indicate connectivity issues, API errors, or simply no matching articles."
}3. Common Search Issues and Solutions
Empty results: Run the connectivity test, verify query spelling, try broader terms.
Connection errors: Check firewall or proxy settings, ensure
*.wikipedia.orgis reachable.API limits: Requests with
limit > 500are automatically capped; negative values reset to the default (10).
4. Debugging with Verbose Logging
Launch the server with debug logging for deeper insight:
wikipedia-mcp --log-level DEBUGThis emits the request parameters, response status codes, and any warnings returned by the API.
Understanding the Model Context Protocol (MCP)
The Model Context Protocol (MCP) is not a traditional HTTP API but a specialized protocol for communication between LLMs and external tools. Key characteristics:
Uses stdio for local integrations and streamable HTTP for network integrations (
sseretained for legacy compatibility)Designed specifically for AI model interaction
Provides standardized formats for tools, resources, and prompts
Integrates directly with Claude and other MCP-compatible AI systems
Claude Desktop acts as the MCP client, while this server provides the tools and resources that Claude can use to access Wikipedia information.
Contributing
Contributions are welcome! Please see CONTRIBUTING.md for guidelines.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Connect with the Author
🌐 Portfolio: ravikumar-dev.me
📝 Blog: Medium
💼 LinkedIn: in/ravi-kumar-e
🐦 Twitter: @Ravikumar_d3v
Available Tools
22 toolsextract_key_factsARead-onlyIdempotent
Extract key facts from a Wikipedia article, optionally focused on a topic.
Returns a dictionary containing a list of facts.
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | ||
| title | Yes | ||
| topic_within_article | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| facts | Yes | |
| title | Yes | |
| topic_within_article | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds the return format ('dictionary containing a list of facts') and optional topic behavior, but does not disclose rate limits, error behavior, or other runtime traits. With strong annotations, this is sufficient but not exceptional.
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 short sentences, each earning its place: the first states the action, the second states the return type. No fluff, front-loaded with purpose.
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?
The tool is simple with strong annotations and an output schema, so the description doesn't need to explain return details. However, the 'count' parameter is left undocumented in both the schema and description, creating a clear gap. The description is adequate for a basic read tool but not complete for all parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only hints at the 'topic_within_article' parameter via 'optionally focused on a topic', leaving 'count' and 'title' semantics unexplained. The 'count' parameter's effect on the number of facts is not mentioned, making the description insufficient for full parameter understanding.
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 ('Extract key facts') and the resource ('Wikipedia article'), with an optional topic focus. It distinguishes itself from sibling tools like get_summary or get_sections by explicitly mentioning the output is a list of facts.
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 when to use the tool (when key facts are needed) but does not provide explicit exclusions or alternatives. The phrase 'optionally focused on a topic' offers some context, but it does not mention when not to use this tool or compare it to the many summary/section tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_articleARead-onlyIdempotent
Get the full content of a Wikipedia article.
Returns a dictionary containing article details or an error message if retrieval fails.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| url | No | |
| text | No | |
| error | No | |
| links | No | |
| title | Yes | |
| exists | Yes | |
| pageid | No | |
| summary | No | |
| sections | No | |
| categories | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint. The description adds that it returns a dictionary or an error message on failure, which provides some behavioral context beyond annotations, but does not disclose additional details like rate limits or response structure.
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, front-loaded with the primary purpose and no redundant information. Every sentence contributes to understanding the tool's function.
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?
The tool is simple with one parameter and an output schema, and annotations are comprehensive. However, given the many sibling tools, the description could be more complete by noting the scope (e.g., full content vs. summary) and any prerequisites. It is adequate but under-specified for the broader context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain the 'title' parameter beyond the tool's name. Since there is only one parameter, the description should compensate but does not, leaving the parameter semantics largely undefined.
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 'Get the full content of a Wikipedia article' with a specific verb and resource. It distinguishes from sibling tools like get_summary and get_sections by emphasizing 'full content'.
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?
Usage is implied: this tool is for fetching full article content. However, it does not explicitly mention alternatives or when to prefer other tools like get_summary or get_sections, so guidance is limited.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_coordinatesARead-onlyIdempotent
Get the coordinates of a Wikipedia article.
Returns a dictionary containing coordinate information.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| error | No | |
| title | Yes | |
| exists | Yes | |
| pageid | No | |
| message | No | |
| coordinates | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering the safety profile. The description adds 'Returns a dictionary containing coordinate information,' which is a behavioral trait but largely redundant given the output schema. No additional context about errors or coordinate format is provided, but annotations lower the bar.
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 extremely concise—two short sentences with no redundant information. It is front-loaded with the core action and immediately useful.
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 simplicity (one parameter, read-only, strong annotations, and an output schema), the description is mostly sufficient. It covers the core purpose and return type. However, it lacks usage guidelines and parameter elaboration, which would make it fully 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 input schema has one required parameter 'title' with no description (0% schema coverage). The description's phrase 'of a Wikipedia article' implicitly links title to an article, but it does not elaborate on expected format or any additional constraints. The description does not fully compensate for the lack of schema documentation.
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 function with a specific verb and resource: 'Get the coordinates of a Wikipedia article.' This distinguishes it from sibling tools like get_sections or get_summary, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites. The purpose implies usage for coordinate retrieval, but there is no direct context to help an agent decide between this and other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_linksARead-onlyIdempotent
Get the links contained within a Wikipedia article.
Returns a dictionary with the article title and list of links.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| links | Yes | |
| title | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this as a safe read-only operation (readOnlyHint, idempotentHint, destructiveHint). The description adds the return format (dictionary with title and links), but discloses no additional behavioral traits such as error handling, pagination, or behavior for nonexistent articles. It does not contradict annotations.
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 sentences with no redundant words, directly stating the function and the return value. It is appropriately sized and front-loaded.
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?
The tool is simple, has an output schema, and comprehensive annotations. The description covers the core function and return structure, which is sufficient for the tool's simplicity. It does not mention edge cases, but those are not necessary given the context and schema.
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 has a single required parameter 'title' with 0% schema description coverage. The description implicitly refers to the article title but provides no explicit parameter semantics, format, or examples. For a single obvious parameter, the meaning is inferable from the tool's purpose, but the description does not compensate for the lack of schema description.
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 identifies the action 'Get' and the resource 'links contained within a Wikipedia article', which distinguishes it from sibling tools like get_sections or get_summary. The verb and resource are specific and unambiguous.
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 explicit guidance on when to use this tool versus alternatives. It implies usage by stating what it does, but lacks any stated criteria, prerequisites, or exclusions that would help an agent choose this tool over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_sectionsARead-onlyIdempotent
Get the sections of a Wikipedia article.
Returns a dictionary with the article title and list of sections.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| title | Yes | |
| sections | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a safe read-only operation. The description adds the return structure (a dictionary with title and sections), which is helpful but largely covered by the output schema. No additional behaviors like error handling are disclosed, so a mid-range score is appropriate.
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 short sentences, front-loaded with the action, and contains zero fluff. It is appropriately sized.
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 simplicity, one parameter, annotations covering safety, and an output schema, the description is complete enough for an agent to use it. It clearly defines the operation and result shape.
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 provides no description for the 'title' parameter (0% coverage), but the description clarifies it's the Wikipedia article title. This adds essential meaning, though no further constraints or format details are given.
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 retrieves sections of a Wikipedia article. It uses a specific verb and resource, distinguishing it from siblings like get_article and get_summary.
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 a clear use case (get sections of a Wikipedia article) and implicitly indicates when to use it. It does not explicitly mention alternatives or when not to use, but the context is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_summaryARead-onlyIdempotent
Get a summary of a Wikipedia article.
Returns a dictionary with the title and summary string. On error, includes an error message instead of a summary.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| error | No | |
| title | Yes | |
| summary | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only and idempotent behavior, allowing the description to focus on additional context. It discloses the return structure (dictionary with title and summary) and error behavior (error message instead of summary), which goes beyond the annotations.
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 short sentences with no wasted words. The description is front-loaded with the primary action and immediately provides the essential return and error 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 tool's simplicity, one self-explanatory parameter, an output schema, and strong annotations, the description is complete. It covers the key behavior (returns summary) and edge case (error message), so the agent has enough information to use the tool 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?
With 1 parameter and 0% schema coverage, the description does not directly explain the 'title' parameter. However, the name 'title' is self-explanatory in the Wikipedia context, and the description implicitly references it as the article title. This is adequate but not exceptional, so 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool gets a summary of a Wikipedia article, with a specific verb and resource. It distinguishes from siblings like get_article (full article) and get_sections (sections) by focusing specifically on the summary.
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 naming the summary function, but provides no explicit guidance on when to choose this over similar sibling tools like summarize_article_for_query or extract_key_facts. There is no mention of alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_wikipediaARead-onlyIdempotent
Search Wikipedia for articles matching a query.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of results to return (1-500). | |
| query | Yes | The search term to look up on Wikipedia. |
Output Schema
| Name | Required | Description |
|---|---|---|
| count | No | |
| query | Yes | |
| status | Yes | |
| message | No | |
| results | No | |
| language | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, covering the safety profile. The description adds no additional behavioral context such as result ranking, pagination, or network behavior. It merely restates what could be inferred from the tool name and annotations, providing no extra 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 concise sentence that efficiently conveys the core purpose. There is zero verbosity and all words add value.
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 simple search tool, the description combined with rich annotations and a complete input schema is sufficient. The output schema exists, so return values are accounted for, and the safety and world assumptions are covered by annotations. No additional context is necessary for an agent to select and invoke the 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?
Schema description coverage is 100%, so both 'query' and 'limit' are fully documented. The description adds no parameter-level meaning beyond what the schema already provides, so 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Search Wikipedia for articles matching a query.' This uses a specific verb ('search') and a clear resource ('Wikipedia') and distinguishes it from sibling tools like get_article or get_summary, which retrieve specific content rather than perform a search.
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 (when you need to find articles by query) but provides no explicit guidance on when to choose this over alternatives, nor does it mention exclusions or prerequisites. It does not differentiate from sibling tools like get_sections or get_links, which are alternatives for other purposes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
summarize_article_for_queryARead-onlyIdempotent
Get a summary of a Wikipedia article tailored to a specific query.
The summary is a snippet around the query within the article text or summary. The max_length parameter controls the length of the snippet.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| title | Yes | ||
| max_length | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| query | Yes | |
| title | Yes | |
| summary | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: the summary is a snippet around the query within the article text or summary, and max_length controls snippet length. This goes beyond the annotations without contradicting them.
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 sentences, efficiently front-loaded with the main purpose, followed by a concise explanation of the snippet and max_length. Every sentence adds value with no fluff.
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?
With an output schema present and strong annotations (readOnly, idempotent), the description covers the core behavior and key parameter. It does not explain edge cases like query-not-found behavior, but this is not essential given the schema and annotations. The tool is well-specified for an agent to select and invoke 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?
Schema description coverage is 0%, so the description must compensate. It explicitly explains max_length ('controls the length of the snippet') and implicitly explains query ('tailored to a specific query') and title (the article title). This adds meaning beyond the bare parameter names, though it does not elaborate on constraints or defaults for title and query.
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 a specific verb and resource: 'Get a summary of a Wikipedia article tailored to a specific query.' This distinguishes it from generic summary tools like get_summary and from section-specific summarize_article_section, by emphasizing query-tailored output.
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 clearly implies when to use the tool: when a query-focused summary is needed. However, it does not explicitly mention alternatives or exclusions, such as 'Use get_summary for generic article summaries.' The context is clear but lacks explicit comparison with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
summarize_article_sectionBRead-onlyIdempotent
Get a summary of a specific section of a Wikipedia article.
Returns a dictionary containing the section summary or an error.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | ||
| max_length | No | ||
| section_title | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| title | Yes | |
| summary | Yes | |
| section_title | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds the return behavior ('Returns a dictionary containing the section summary or an error'), which goes beyond the annotations. It doesn't mention max_length effects, but the annotation coverage lowers the required burden.
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 concise sentences, front-loaded with the primary purpose, and no filler or redundancy.
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?
The tool has annotations and an output schema, and the description states purpose and return type. However, it lacks parameter semantics, usage guidelines versus siblings, and any mention of max_length or error handling nuances, leaving meaningful gaps for a 3-parameter 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?
Schema coverage is 0% and the description provides no explanation for the parameters (title, section_title, max_length). It does not compensate for the missing schema descriptions, leaving parameter semantics entirely to the agent's inference from names.
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 uses a specific verb ('Get') and resource ('a specific section of a Wikipedia article'), clearly distinguishing it from siblings like get_summary (whole article summary) and get_sections (list sections). It also states the return type.
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 such as get_summary or summarize_article_for_query. The usage is only implied by the phrase 'specific section', with no explicit when/when-not or sibling differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
test_wikipedia_connectivityARead-onlyIdempotent
Provide diagnostics for Wikipedia API connectivity.
Returns the base API URL, language, site information, and response time in milliseconds. If connectivity fails, status will be 'failed' with error details.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| url | Yes | |
| error | No | |
| server | No | |
| status | Yes | |
| language | Yes | |
| site_name | No | |
| error_type | No | |
| response_time_ms | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, and the description adds valuable behavior details: return fields (base API URL, language, site info, response time) and failure handling (status='failed' with error details). This complements the annotations without contradicting them.
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 sentences and front-loaded with the primary purpose. The first sentence states what the tool does, and the second sentence details the output and failure behavior. Every word contributes meaning; there is no redundancy.
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 simple tool with no parameters and an output-schema present, the description is fully sufficient. It explains the return values and failure status, covering all necessary contextual information for an agent to invoke the tool appropriately.
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 zero parameters and the schema coverage is 100%, so the description has no parameters to explain. The baseline for zero-parameter tools is 4, and the description appropriately refrains from inventing unnecessary parameter details.
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: 'Provide diagnostics for Wikipedia API connectivity.' It uses a specific verb ('provide diagnostics') and a well-defined resource ('Wikipedia API connectivity'), making it distinct from sibling tools that retrieve content or perform searches.
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 its usage as a connectivity diagnostic tool but does not explicitly state when to use it versus alternatives. Since no sibling tool performs diagnostics, the usage context is inferred rather than directly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
wikipedia_extract_key_factsARead-onlyIdempotent
Extract key facts from a Wikipedia article, optionally focused on a topic.
Returns a dictionary containing a list of facts.
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | ||
| title | Yes | ||
| topic_within_article | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| facts | Yes | |
| title | Yes | |
| topic_within_article | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive behavior, so the safety profile is clear. The description adds value by explicitly stating the return type ('a dictionary containing a list of facts') and the optional topic focus, which are behavioral details not covered by annotations.
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 only two sentences, with the core purpose front-loaded in the first sentence and the return type in the second. Every word earns its place, and there is no fluff or redundancy.
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?
The tool is simple (3 straightforward parameters, with an output schema present and strong annotations), so the description covers the essential behavior and return type. Minor gaps include lack of parameter details and no explicit comparison to similar tools, but these do not undermine usability given the low complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for all parameters. While it explains 'topic_within_article' indirectly ('focused on a topic'), it does not explain 'count' or the exact role of 'title'. Given 3 parameters and no schema descriptions, this leaves a significant meaning gap.
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 function with a specific verb ('Extract') and resource ('key facts from a Wikipedia article'), and includes the optional topic focus. This distinguishes it from sibling tools like get_summary or get_article, which produce different outputs.
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 appropriate usage ('optionally focused on a topic') but does not explicitly mention when not to use it or contrast it with alternatives. It conveys that this is for extracting facts rather than summaries or full articles, which provides clear contextual guidance without explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
wikipedia_get_articleARead-onlyIdempotent
Get the full content of a Wikipedia article.
Returns a dictionary containing article details or an error message if retrieval fails.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| url | No | |
| text | No | |
| error | No | |
| links | No | |
| title | Yes | |
| exists | Yes | |
| pageid | No | |
| summary | No | |
| sections | No | |
| categories | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds value by specifying the return type (dictionary) and error handling (error message if retrieval fails), which is useful context beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no redundant information, and the main purpose is front-loaded. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple parameter set and strong annotations, the description is largely sufficient. It covers the primary outcome and failure mode. An output schema exists, so detailed return value documentation is unnecessary.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not elaborate on the title parameter. While the parameter name is self-explanatory, the description fails to add meaning about formatting, disambiguation, or other nuances, leaving the agent to infer.
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 retrieves the full content of a Wikipedia article, using a specific verb and resource. It distinguishes from sibling tools like get_summary or get_sections by emphasizing 'full content'.
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 the tool should be used when full article content is needed, but it does not explicitly mention alternatives or exclusion criteria. The presence of multiple similar sibling tools (e.g., get_article, get_summary) makes this a missed opportunity for clearer guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
wikipedia_get_coordinatesARead-onlyIdempotent
Get the coordinates of a Wikipedia article.
Returns a dictionary containing coordinate information.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| error | No | |
| title | Yes | |
| exists | Yes | |
| pageid | No | |
| message | No | |
| coordinates | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (read-only, idempotent, non-destructive), lowering the bar for description. The description adds only that a dictionary is returned, which is also documented by the output schema. No behavioral details like error handling are provided, but none contradict the annotations.
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 short sentences with no filler. It front-loads the core action and states the return type without unnecessary detail.
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?
This is a simple one-parameter tool with a strong output schema and clear annotations. The description fully covers the tool's purpose and basic behavior, and the existing schema/annotations cover the rest. Nothing essential is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. The only parameter, 'title', is self-explanatory and the description's reference to 'Wikipedia article' adds context that the title is the article's title. However, no additional detail or format is given, making this adequate but not exceptional.
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 'Get the coordinates of a Wikipedia article' with a specific verb and resource, making the tool's purpose unambiguous. However, it does not distinguish itself from the sibling tool 'get_coordinates' which appears to perform the same function.
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?
There is no guidance about when to use this tool or how it differs from alternatives. Given the presence of many sibling tools including what looks like a duplicate, the lack of usage context is a notable gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
wikipedia_get_linksARead-onlyIdempotent
Get the links contained within a Wikipedia article.
Returns a dictionary with the article title and list of links.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| links | Yes | |
| title | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds the return structure (dictionary with article title and list of links), which is not covered by the annotations. This provides useful context beyond the structured data.
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 sentences, front-loaded with the main action and followed by the return format. Every word earns its place, and there is no redundancy or filler.
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 simple getter nature, strong annotations, and existence of an output schema, the description is sufficient. It covers the primary action and return type. It does not address edge cases or errors, but these are less critical for a read-only tool with clear annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description does not explicitly define the 'title' parameter. However, it is strongly implied that the parameter refers to the Wikipedia article title, given the phrasing 'within a Wikipedia article.' The description partially compensates but could be more explicit.
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 function: 'Get the links contained within a Wikipedia article.' This is a specific verb and resource, and it differentiates from sibling tools like get_article or get_summary. The mention of a return value further clarifies its purpose.
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 when to use the tool (when you need links from an article) but does not explicitly contrast it with alternatives like get_article or get_summary. No exclusions or prerequisites are mentioned, only the core action.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
wikipedia_get_sectionsBRead-onlyIdempotent
Get the sections of a Wikipedia article.
Returns a dictionary with the article title and list of sections.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| title | Yes | |
| sections | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a safe, read-only, idempotent operation. The description adds that it returns a dictionary with title and sections, but this is minor and the output schema likely covers return value details. No additional behavioral context (e.g., error handling, edge cases) is provided.
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 sentences, front-loaded with the action, and contains no fluff. Every sentence adds information about what the tool does or returns.
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 simple tool with one parameter, strong annotations, and an output schema, the description is sufficiently complete. It states the core functionality and return type, though it could improve by adding usage context relative to siblings.
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?
With 0% schema description coverage, the description should compensate by explaining the 'title' parameter, but it does not. It simply repeats the word 'title' without adding meaning about format, exactness, or disambiguation.
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 retrieves the sections of a Wikipedia article, which is a specific verb+resource. It differentiates from most sibling tools (e.g., get_summary, get_article), though it does not distinguish from the similar sibling 'get_sections'.
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 such as get_article or get_summary. The description only states what the tool does, with no contextual hints or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
wikipedia_get_summaryARead-onlyIdempotent
Get a summary of a Wikipedia article.
Returns a dictionary with the title and summary string. On error, includes an error message instead of a summary.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| error | No | |
| title | Yes | |
| summary | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context beyond annotations by specifying the return format ('dictionary with the title and summary string') and error behavior ('includes an error message instead of a summary'). This goes beyond what the annotations convey.
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, front-loaded with the primary purpose. Every sentence earns its place, and there is no redundant or extraneous content.
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 simple tool with one parameter and strong annotations, the description adequately covers the main behavior, return content, and error handling. It does not mention language edition or filtering options, but these are not necessary for a summary operation. The absence of an explicit output schema in the context is mitigated by the description's mention of the return structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not compensate. The 'title' parameter is not explained; the description only refers to 'a Wikipedia article,' leaving the agent to infer that 'title' is the article title. While the parameter name is self-explanatory, the description adds no additional semantic detail.
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 uses a specific verb and resource: 'Get a summary of a Wikipedia article.' It clearly states what the tool does and aligns with the tool name. While there are siblings like 'get_summary', the description is unambiguous enough to distinguish this tool as the Wikipedia-specific summary retriever.
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 when a summary is needed but offers no explicit guidance on when to prefer this over alternatives like get_article or get_sections. It does not mention exclusions or provide comparisons, so usage context is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
wikipedia_search_wikipediaBRead-onlyIdempotent
Search Wikipedia for articles matching a query.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of results to return (1-500). | |
| query | Yes | The search term to look up on Wikipedia. |
Output Schema
| Name | Required | Description |
|---|---|---|
| count | No | |
| query | Yes | |
| status | Yes | |
| message | No | |
| results | No | |
| language | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, which already cover the safety profile. The description adds no additional behavioral context beyond the basic search action, such as result format, pagination, or API limitations. It does not contradict the annotations.
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 concise sentence, 'Search Wikipedia for articles matching a query,' which front-loads the verb and resource without unnecessary words. It is appropriately sized for the tool's simplicity.
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?
The tool has a complete input schema, output schema, and safety annotations, so the description does not need to explain return values or safety. However, the description is incomplete in that it does not address the relationship to the sibling tool 'search_wikipedia', which appears to be nearly identical, leaving the agent unsure which to 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 fully describes both parameters with 100% coverage (query and limit with descriptions). The tool description does not add any additional meaning beyond the schema, so the baseline score of 3 is appropriate.
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 searches Wikipedia for articles matching a query, using a specific verb and resource. However, it does not differentiate from the sibling tool 'search_wikipedia', which appears to have an identical purpose.
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 'search_wikipedia' or other Wikipedia tools. There is no mention of exclusions, use cases, or relationships to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
wikipedia_summarize_article_for_queryARead-onlyIdempotent
Get a summary of a Wikipedia article tailored to a specific query.
The summary is a snippet around the query within the article text or summary. The max_length parameter controls the length of the snippet.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| title | Yes | ||
| max_length | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| query | Yes | |
| title | Yes | |
| summary | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only and idempotent behavior. The description adds useful context: the output is a snippet around the query, and max_length controls snippet length. No contradictions with annotations.
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 short sentences front-load the purpose and add one functional detail. Every sentence earns its place with no redundancy.
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 read-only tool with output schema and good annotations, the description covers purpose, snippet mechanics, and the length control. It doesn't address edge cases like missing articles, but that is not critical given the annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates by explaining max_length explicitly and clarifying that title identifies the article and query determines the snippet location. This goes beyond the raw 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 tool's function: 'Get a summary of a Wikipedia article tailored to a specific query.' This specifies a concrete verb, resource, and distinguishes it from siblings like get_summary by emphasizing query-tailored output.
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 when to use the tool: when a query-specific summary or snippet is needed. It explains the snippet behavior but does not explicitly name alternatives or exclusions, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
wikipedia_summarize_article_sectionBRead-onlyIdempotent
Get a summary of a specific section of a Wikipedia article.
Returns a dictionary containing the section summary or an error.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | ||
| max_length | No | ||
| section_title | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| title | Yes | |
| summary | Yes | |
| section_title | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds that it returns a dictionary or an error, which is useful but minimal. It does not disclose other behavioral traits beyond the return format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, two sentences, with the main purpose front-loaded. Every sentence provides relevant information without unnecessary filler.
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?
The tool is simple and has an output schema, but the description lacks usage guidance and parameter detail. It does not clarify differences from nearly identical sibling tools like 'summarize_article_section', leaving some ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain any parameters. While title and section_title are self-evident, max_length is left ambiguous, and the description fails to compensate for the lack of schema documentation.
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 gets a summary of a specific section of a Wikipedia article, using a specific verb and resource. It distinguishes from siblings like get_summary (whole article) and get_sections (list sections) by explicitly mentioning 'specific section'.
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 any exclusions or alternative tools for different scenarios, leaving the agent without context for appropriate selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
wikipedia_test_wikipedia_connectivityARead-onlyIdempotent
Provide diagnostics for Wikipedia API connectivity.
Returns the base API URL, language, site information, and response time in milliseconds. If connectivity fails, status will be 'failed' with error details.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| url | Yes | |
| error | No | |
| server | No | |
| status | Yes | |
| language | Yes | |
| site_name | No | |
| error_type | No | |
| response_time_ms | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond annotations by specifying the return payload (base API URL, language, site information, response time) and the failure behavior (status 'failed' with error details). This is useful supplementary information, especially given the readOnly and idempotent annotations already present.
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 sentences, front-loaded with the main purpose, and every word adds value. It avoids redundancy and is appropriately sized for the tool's simplicity.
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?
The tool is simple (no parameters) and the description covers its function, return value, and failure mode. An output schema is present, so the description does not need to detail return structure further, making the description complete for this context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With zero parameters, the description does not need to explain parameter semantics. The schema already covers all parameters (none), and the description adds no unnecessary parameter detail, meeting the baseline for a zero-parameter tool.
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: 'Provide diagnostics for Wikipedia API connectivity.' This uses a specific verb (provide) and resource (connectivity diagnostics), and it is clearly distinct from sibling tools that fetch content or search Wikipedia.
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 gives clear context that this tool is for checking API connectivity, which implies when to use it. However, it does not explicitly name alternatives or state when not to use it, though sibling tools are obviously for different purposes.
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.
22 tool updates
v2.0.1- First observed
extract_key_facts - First observed
get_article - First observed
get_coordinates - First observed
get_links - First observed
get_related_topics - First observed
get_sections - First observed
get_summary - First observed
search_wikipedia - First observed
summarize_article_for_query - First observed
summarize_article_section - First observed
test_wikipedia_connectivity - First observed
wikipedia_extract_key_facts - First observed
wikipedia_get_article - First observed
wikipedia_get_coordinates - First observed
wikipedia_get_links - First observed
wikipedia_get_related_topics - First observed
wikipedia_get_sections - First observed
wikipedia_get_summary - First observed
wikipedia_search_wikipedia - First observed
wikipedia_summarize_article_for_query - First observed
wikipedia_summarize_article_section - First observed
wikipedia_test_wikipedia_connectivity
TDQS
Scored across 22 tools
Every tool has a duplicate with the 'wikipedia_' prefix, making it impossible to tell them apart. The descriptions are identical, so agents cannot distinguish between e.g. get_sections and wikipedia_get_sections, leading to high misselection risk.
The original tools use a consistent verb_noun pattern (e.g., get_article, search_wikipedia), but the addition of 'wikipedia_' prefixed duplicates mixes conventions and creates redundant, awkward names like wikipedia_search_wikipedia. This inconsistency makes the API feel disorganized.
At 22 tools, the server is heavily over-inflated; each of the 11 unique operations is exposed twice. This doubles the cognitive load and suggests poor API design, even though the underlying feature count is reasonable.
For a read-only Wikipedia API, the unique tools cover the core operations well: search, retrieval, summaries, sections, links, coordinates, and related topics. Minor advanced features like page categories or edit history are missing, but the surface is largely complete for typical use.
Maintenance
Related MCP Connectors
Driflyte MCP server which lets AI assistants query topic-specific knowledge from web and GitHub.
MCP server for langchain documentation, generated by doc2mcp.
An MCP server that gives your AI access to the source code and docs of all public github repos
Related MCP Servers
- AlicenseBqualityBmaintenanceA Model Context Protocol server that retrieves information from Wikipedia to provide context to LLMs, allowing users to search articles, get summaries, full content, sections, and links from Wikipedia.222,952 PyPI295MIT
- FlicenseBqualityCmaintenanceA MCP server that allows you to search and retrieve content on any wiki site using MediaWiki with LLMs 🤖. wikipedia.org, fandom.com, wiki.gg and more sites using Mediawiki are supported!227-
- FlicenseNot gradedqualityDmaintenanceAn MCP server that retrieves and provides Wikipedia content for requested topics, enabling easy access to Wikipedia information directly through the Model Control Protocol.1-
- AlicenseBqualityCmaintenanceAn MCP server that enables LLMs to search, summarize, and retrieve detailed information from Wikipedia across multiple languages. It supports automated fact-checking by allowing models to proactively verify factual claims using Wikipedia's database.52MIT