Provides academic paper search with access to full PDF text via the arXiv API.
Enables web search using DuckDuckGo, allowing retrieval of real-time information from the internet.
Allows analysis of YouTube videos by extracting transcripts and performing content analysis.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Playground search for the latest AI research papers"
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.
Research and analysis toolkit with real-time data access via Model Context Protocol (MCP).
Live Server: https://mcp-playground.fastmcp.app/mcp
git clone <your-repo>
cd mcp_arena
uv sync --extra local
uv run streamlit run app.pyRelated MCP server: Sibyl
Option 1: Remote Server (Recommended)
Add to claude_desktop_config.json:
{
"mcpServers": {
"mcp-playground": {
"type": "http",
"url": "https://mcp-playground.fastmcp.app/mcp"
}
}
}Option 2: Local Development
Add to claude_desktop_config.json:
{
"mcpServers": {
"mcp-playground-local": {
"command": "uv",
"args": ["run", "python", "mcp_server.py"]
}
}
}Note: Run this command from your mcp_arena directory
Cmd/Ctrl + Shift + P → "MCP: Add Server"
Choose "Add remote HTTP server"
URL: https://mcp-playground.fastmcp.app/mcp
Program tab → "Edit mcp.json"
Add configuration:
{
"mcp-arena": {
"url": "https://mcp-playground.fastmcp.app/mcp",
}
}web_search(query) - DuckDuckGo web search
analyze_url(url) - Extract and analyze webpage content
arxiv_search(query) - Academic paper search with full PDF text
get_stock_overview(symbol) - Real-time stock, crypto, and market data
analyze_canadian_economy() - Economic indicators and analysis
analyze_youtube_url(url) - Video transcription and analysis
get_weather(location) - Weather forecasts by city or coordinates
get_tide_info(location) - Canadian coastal tide times
get_toronto_crime(neighbourhood, crime_type) - Crime statistics by area
list_toronto_neighbourhoods() - Available neighborhoods
# Core dependencies
uv sync
# With Streamlit UI
uv sync --extra local
# With development tools
uv sync --extra dev# Streamlit UI
uv run streamlit run app.py
# MCP server for Claude Desktop
uv run python mcp_server.py
# HTTP mode testing
uv run python -m src.server http 8000YOUTUBE_MAX_TOKENS=24000 - Transcript processing limit
WEBSHARE_PROXIES="ip:port:user:pass,..." - Proxies for YouTube cloud deployment
MCP_RETRY_MAX_ATTEMPTS=3 - Auto-retry failed tool calls
MCP_RETRY_TYPE_COERCION=true - Auto-fix type mismatches
Tools in src/tools/ modules use @mcp.tool(description="...") decorator with automatic schema generation from Python type hints.
Push to main branch
FastMCP Cloud auto-deploys from GitHub
Get auth token from dashboard
Configure clients with deployment URL
Clone repository
uv sync --extra local
uv run streamlit run app.py
DEPLOYMENT.md - Deployment guide
CLAUDE.md - Development notes
Canadian economic analysis and overview
| Name | Required | Description | Default |
|---|---|---|---|
| focus | No | overview |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must fully disclose behavior. It provides no information about side effects, network usage, computational cost, or auth requirements. The tool's behavior is entirely opaque.
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 (one short phrase), but it does not earn its place because it provides insufficient information. It is under-specified rather than efficiently clear.
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 ability to describe behavior and parameters, the description is severely incomplete. No output schema exists, no parameter guidance, no behavioral hints. An agent would struggle to use this 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?
Schema description coverage is 0% and the tool description adds no meaning to the single parameter 'focus'. Possible values or semantics like 'overview' are not explained, leaving the agent to guess.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's domain ('Canadian economic analysis and overview') but lacks a specific verb and resource. It does not differentiate from siblings that are clearly different domains (e.g., weather, stock), but fails to specify what kind of analysis is performed (e.g., data retrieval, report generation). Thus, it is vague but not misleading.
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. There is no description of context, prerequisites, or exclusions. The agent receives no help in deciding when this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Comprehensive analysis of webpage content optimized for LLM understanding
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description lacks behavioral details such as whether it fetches the web page, extracts content, or summarizes. With no annotations, the description should disclose traits like network usage or rate limits but does not.
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?
While short, the description is under-specified and lacks essential details, making it not genuinely concise but rather incomplete.
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 single parameter and no output schema, the description should explain what 'analysis' means (e.g., keywords, summary) but does not. The agent cannot determine the tool's output or behavior.
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 0% coverage and the description provides no information about the 'url' parameter—no format, examples, or constraints. This is a critical 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 it analyzes webpage content, with the verb 'analyze' and resource 'URL'. It distinguishes from sibling tools like 'analyze_youtube_url' and 'analyze_canadian_economy' which are domain-specific.
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 its siblings. There are no exclusions or context about when it is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get YouTube video transcript and analyze content with optional specific questions
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| question | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It only mentions fetching and analyzing without details on side effects, permissions, or limitations (e.g., rate limits, video length constraints).
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?
A single, well-formed sentence that front-loads the core function. No redundant information, 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?
Despite conciseness, the tool has no output schema and no annotations. The description omits important context such as return format, error handling, prerequisites, or usage limits making it incomplete for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It indirectly explains 'url' as YouTube video and 'question' as optional query, but lacks format details or constraints (e.g., URL validity). Adds some meaning but not comprehensive.
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 a YouTube video transcript and analyzes it, with optional specific questions. This distinguishes it from sibling tools like 'analyze_url' (general URL analysis) and 'analyze_canadian_economy' (specific domain).
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 explicit guidance on when to use this tool versus alternatives. It is implied for YouTube transcript analysis but lacks 'when not to use' or mention of sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search academic papers on arXiv with full text extraction
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| max_results | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only says 'full text extraction' without explaining behavior (e.g., returns abstracts or full text, rate limits, pagination).
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?
Single, front-loaded sentence with no filler. 'Search academic papers on arXiv with full text extraction' is efficient and clear.
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 search tool with no output schema, the description is too minimal. It lacks details on return format, error conditions, or how 'full text extraction' works.
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%, but description does not add meaning to parameters. It doesn't explain query format or max_results usage beyond what schema shows.
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 it searches academic papers on arXiv, a specific verb+resource. It also mentions 'full text extraction', distinguishing it from sibling tools like web_search. No ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives like web_search. No context for best use cases or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get comprehensive stock, crypto, and market data
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only says 'get...data' without disclosing read-only nature, rate limits, data freshness, or potential side effects. Minimal behavioral info.
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?
Single sentence is concise but lacks essential information. For a one-parameter tool, conciseness is acceptable but structure could be improved by front-loading key details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, no annotations, and vague description, the tool definition fails to inform the agent about return format or data type. Incomplete for effective selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has one required parameter 'symbol' with 0% description coverage. The description does not explain what 'symbol' represents (e.g., stock ticker, crypto code) or expected format. No additional meaning added.
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?
Description states 'Get comprehensive stock, crypto, and market data' which is vague; 'comprehensive' is subjective and doesn't specify what data fields are included. It does not clearly differentiate from siblings like 'analyze_canadian_economy' or 'get_weather'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives (e.g., 'analyze_canadian_economy' for focused economic data). No mention of prerequisites or exclusions. The description provides no usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get Canadian coastal tide times and heights
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | ||
| location | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and the description does not disclose any behavioral traits such as output format, data sources, or limitations. For a tool with no annotations, this is insufficient.
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 with no unnecessary words. However, it is too brief and could be restructured to include more details without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no output schema and two parameters, the description should explain what data is returned (e.g., timing, heights, units) and how to use parameters. It fails to do so, leaving agents with incomplete 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%, yet the description adds no information about parameters (e.g., that location is required, date is optional, or acceptable formats). The description does not compensate for the lack of schema descriptions.
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 action ('Get') and resource ('Canadian coastal tide times and heights'), which is specific and distinguishes it from all sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines are provided. There is no indication when to use this tool versus alternatives, nor any context about required prerequisites or excluded cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get Toronto neighbourhood crime statistics and trends
| Name | Required | Description | Default |
|---|---|---|---|
| crime_type | No | assault | |
| neighbourhood | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits, but it only gives a vague phrase ('statistics and trends'). No mention of safety, rate limits, or output characteristics.
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?
While short, the description is too brief for a tool with 2 parameters and no annotations, sacrificing necessary 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?
Given the tool's simplicity (2 params, no output schema), the description lacks essential details about parameter meaning, return format, and usage constraints.
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 adds no information about the parameters—neighbourhood is required, crime_type has a default, but no explanation of valid values or behavior.
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 its purpose: retrieving Toronto neighborhood crime statistics and trends. It distinguishes itself from sibling tools like weather or stock data.
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, nor any context about prerequisites or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get current weather and 7-day forecast for any location or your current location
| Name | Required | Description | Default |
|---|---|---|---|
| location | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility for behavioral traits. It does not mention read-only nature, error handling, rate limits, or any side effects. Only the output type (current weather and forecast) is hinted, but not formalized.
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, focused sentence that efficiently conveys the tool's purpose without extraneous information. It is front-loaded and every word adds 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?
Given the tool's low complexity (one parameter, no output schema), the description provides the basic intent but omits crucial context like return format, error cases, units, or limitations, making it insufficient for reliable invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning beyond the schema by explaining that the location can be 'any location' or 'your current location' (null). However, it lacks format details (e.g., city name, coordinates) and examples, leaving some ambiguity.
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 verb 'get' and the resource 'current weather and 7-day forecast', specifying the scope 'for any location or your current location'. It effectively distinguishes the tool from its siblings, none of which focus on weather.
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, nor any exclusions or prerequisites. The agent must infer its use from the description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all available Toronto neighbourhoods for crime data
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It correctly implies a read-only operation ('list') but does not explicitly disclose safety or lack of side effects. For a simple listing tool, this is adequate but not thorough.
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 with no wasted words. It front-loads the action and resource, making it efficient for an AI agent to parse.
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?
While the tool is simple and has an output schema (so return values need not be explained), the description lacks context about how this tool fits into workflows (e.g., its output is needed to use 'get_toronto_crime'). It is minimally 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?
There are no parameters (0 params, 100% schema coverage), so the baseline is 4. The description adds no additional parameter meaning, but none is necessary.
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 verb 'list' and the resource 'all available Toronto neighbourhoods for crime data', making the tool's purpose unambiguous. It distinguishes itself from sibling tools like 'get_toronto_crime' by focusing on neighbourhoods rather than crime data.
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 or when not to use it. It does not indicate that listing neighbourhoods is typically a prerequisite for querying crime data in 'get_toronto_crime', nor does it mention any alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search web for current information using DuckDuckGo
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| max_results | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only states the tool searches the web, but does not disclose any behavioral traits such as rate limits, API restrictions, safety, or result format. This is insufficient for a search tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the key information. It is not verbose, though it could add more context without becoming bloated.
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 search tool with 2 parameters and no output schema, the description is minimal. It does not describe what the results contain (e.g., snippets, URLs), pagination, or any limitations. The agent lacks enough context to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. The description adds no meaning beyond the schema for parameters 'query' and 'max_results'. It does not explain what the query should contain or how max_results affects results.
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 verb 'search', resource 'web', and purpose 'current information' using a specific engine (DuckDuckGo). This distinguishes it from siblings like arxiv_search (specific to arxiv) and analyze_youtube_url (specific to YouTube).
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 for general web searches, but does not explicitly state when to use this tool versus alternatives like arxiv_search or analyze_url. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0analyze_canadian_economyanalyze_urlanalyze_youtube_urlarxiv_searchget_stock_overviewget_tide_infoget_toronto_crimeget_weatherlist_toronto_neighbourhoodsweb_searchEach tool targets a distinct domain or function: Canadian economy, URL analysis, YouTube, arXiv, stocks, tides, Toronto crime, weather, neighborhoods, and web search. There is no overlap or ambiguity among them.
All tool names follow a consistent verb_noun pattern (e.g., analyze_canadian_economy, get_weather, list_toronto_neighbourhoods, web_search). The naming is predictable and uniform.
The server has 10 tools, which is a reasonable number for a general-purpose playground. Each tool covers a distinct area without being overwhelming or sparse.
The tool set covers a broad range of information retrieval tasks (web, academic, financial, weather, local data). Minor gaps exist (e.g., no image analysis, no file operations), but for a playground server, the coverage is sufficient.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
AI research on companies and industries — one MCP tool per research domain.
Web search, scraping, RAG answers with citations, and translation as MCP tools.
Live AI-native web search with citations. One tool for every MCP client. Flat per-request pricing.
Real SEC, 13F, insider, congress & macro data your AI agent can cite. Hosted MCP, 24 tools.
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