a2asearch-mcp
The A2ASearch MCP server provides unified search and discovery across a directory of 10,000+ AI tools, agents, and servers. It offers three core capabilities:
Search agents (
search_agents): Query the directory by keyword to find AI agents, MCP servers, CLI tools, agent skills, and A2A agents — with optional filtering by type and configurable result limits (1–20).Get agent details (
get_agent): Retrieve full information for a specific agent by its slug (e.g.playwright,ollama), including its README, capabilities, stars, forks, programming languages, and GitHub URL.List/browse agents (
list_agents): Browse agents by category with optional type filtering, sortable by popularity (stars) or recency (newest additions).
It requires no authentication or API keys, works with Claude Desktop, Cursor, Windsurf, and other MCP clients, and is free to use.
Provides search capabilities for AI agents, MCP servers, CLI tools, and agent skills within Windsurf (Codeium's IDE), enabling developers to discover and integrate AI tools into their coding workflow.
Provides access to GitHub-hosted AI agents and tools, including star ratings, forks, and repository information, enabling users to discover and evaluate open-source AI projects.
Enables searching for MCP servers and AI tools that integrate with Notion, allowing users to find agents and tools that can work with Notion's API and functionality.
Enables discovery and installation of AI agents and MCP servers available through npm, providing access to thousands of tools in the JavaScript/Node.js ecosystem for AI workflows.
Provides detailed information about the Ollama agent including its capabilities, README, and technical specifications, enabling users to understand and potentially integrate with Ollama's local LLM management system.
A2ASearch MCP Server
10,752 AI agents, MCP servers, CLI tools and agent skills — searchable in seconds from Claude, Cursor, or any MCP client.
The only unified search engine for the AI agent ecosystem. One package. Every major MCP server, CLI tool, coding agent, and agent skill — indexed and searchable.
npx a2asearch-mcp -- playwright
# → Finds playwright MCP server, CLI tools, related agentsWhy A2ASearch?
The MCP/agent ecosystem is exploding. There are now thousands of tools across GitHub, npm, and various registries — and no single place to find them. A2ASearch indexes them all:
What you're looking for | A2ASearch has it |
MCP servers for Claude/Cursor | ✅ 2,000+ indexed |
CLI tools for LLM workflows | ✅ |
AI coding agents (Codex, Claude Code, etc.) | ✅ |
Agent skills & plugins | ✅ |
A2A protocol agents | ✅ |
No auth needed. No API key. Free.
Related MCP server: claude-oracle-mcp
Installation
Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"a2asearch": {
"command": "npx",
"args": ["-y", "a2asearch-mcp"]
}
}
}Cursor
Add to .cursor/mcp.json:
{
"mcpServers": {
"a2asearch": {
"command": "npx",
"args": ["-y", "a2asearch-mcp"]
}
}
}Windsurf
Add to ~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": {
"a2asearch": {
"command": "npx",
"args": ["-y", "a2asearch-mcp"]
}
}
}Cline / Continue / OpenClaw
Same format — add to your MCP config file:
{
"mcpServers": {
"a2asearch": {
"command": "npx",
"args": ["-y", "a2asearch-mcp"]
}
}
}MCP Tools
search_agents
Search across the full directory by keyword.
"Find MCP servers for browser automation"
"Search for AI agents that can do web research"
"Find CLI tools for working with LLMs"get_agent
Get full details for a specific agent including README, capabilities, stars.
"Get details for playwright"
"Tell me about claude-code"
"Show me the mem0 agent"list_agents
Browse top agents by type and sort order.
"List the top MCP servers by stars"
"Show me the newest AI coding agents"
"What are the top agent skills?"Example Prompts
Once installed, ask your AI assistant:
"Search for MCP servers that can help me work with databases"
"What are the most popular AI coding agents right now?"
"Find agent skills for web browsing"
"Is there an MCP server for Notion?"
"Get full details on the ollama agent"
CLI Usage
Don't need the MCP server? Use the CLI directly:
# Install globally
npm install -g a2asearch-mcp
# Search by keyword
a2asearch playwright
a2asearch "web scraping"
a2asearch database
# Filter by type
a2asearch --type mcp database
a2asearch --type skill web-browsing
a2asearch --type cli llm
a2asearch --type agent coding
# Top agents by stars
a2asearch --type mcp --top
a2asearch --type agent --top
# Get full details
a2asearch --get ollama
a2asearch --get playwright
# Newest additions
a2asearch --newOr without installing:
npx a2asearch-mcp -- playwright
npx a2asearch-mcp -- --type mcp database
npx a2asearch-mcp -- --get ollamaAgent Types
Type |
| Description |
MCP Server |
| Model Context Protocol servers |
CLI Tool |
| Terminal tools for LLM workflows |
AI Coding Agent |
| Autonomous coding agents |
Agent Skill |
| Plugins and skills for AI assistants |
A2A Agent |
| Agent-to-Agent protocol agents |
AI Tool |
| General AI-powered tools |
REST API
The MCP server wraps the free A2ASearch REST API. Use it directly if you prefer:
# Search
curl "https://a2asearch.ai/api/v1/agents?q=playwright"
# Get by slug
curl "https://a2asearch.ai/api/v1/agent/playwright"
# List by type
curl "https://a2asearch.ai/api/v1/agents?type=MCP+Server&sort=stars"No authentication required.
Submit a Tool
Know a tool that's missing? Submit it →
The index is community-maintained. Submissions are reviewed and added within 24–48 hours.
Links
🌐 a2asearch.ai — browse the full directory
📦 npm
🐛 Issues
License
MIT
Available Tools
3 toolsget_agentB
Get full details for a specific agent by its slug (name as kebab-case). Returns description, README, capabilities, stars, forks, languages and more.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Agent slug — e.g. 'playwright', 'ollama', 'claude-code', 'mem0' |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return data includes 'description, README, capabilities, stars, forks, languages and more,' which adds some context about output. However, it doesn't cover critical aspects like whether this is a read-only operation, error handling for invalid slugs, rate limits, or authentication needs, leaving significant gaps for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by details on return values. It's efficient with two sentences and no redundant information, though it could be slightly more structured by separating usage context from output details. Overall, it's appropriately sized 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?
Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is moderately complete. It covers the purpose and output data, but lacks behavioral details like error cases or operational constraints. Without annotations or an output schema, it should provide more context on what 'full details' entail and potential limitations, making it adequate but with noticeable gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'slug' parameter well-documented as 'Agent slug — e.g. 'playwright', 'ollama', 'claude-code', 'mem0'.' The description adds minimal value by restating that the slug is 'name as kebab-case' and used to identify the agent, but doesn't provide additional semantics beyond what the schema already covers. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get full details for a specific agent by its slug.' It specifies the verb ('Get'), resource ('agent'), and identifier method ('by its slug'), making the action clear. However, it doesn't explicitly differentiate from sibling tools like 'list_agents' or 'search_agents', which likely handle multiple agents rather than a single one.
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 detailed information about a specific agent, as indicated by 'specific agent by its slug.' However, it doesn't provide explicit guidance on when to use this tool versus alternatives like 'list_agents' or 'search_agents,' nor does it mention any prerequisites or exclusions. The context is clear but lacks comparative direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_agentsB
List agents from A2ASearch, optionally filtered by type. Use this to browse top agents by category.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Filter by type (optional — omit for all types) | |
| sort | No | Sort order: 'stars' for most popular, 'new' for recently added | stars |
| limit | No | Number of results (1-20, default 10) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'browse top agents by category,' which hints at a read-only operation, but doesn't explicitly state safety (e.g., non-destructive), rate limits, authentication needs, or what the output looks like (e.g., pagination, format). For a tool with no annotations, this leaves significant gaps in understanding its behavior.
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 with zero waste. The first sentence states the core purpose, and the second provides usage guidance. It's front-loaded and efficiently structured, making it easy 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?
Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and hints at usage, but lacks details on behavioral traits (e.g., safety, output format) that are crucial since annotations are absent. It meets minimum viability but has clear gaps in 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 100%, with all parameters well-documented in the schema (type, sort, limit). The description adds minimal value beyond the schema, mentioning 'optionally filtered by type' and 'browse top agents by category,' which loosely relates to the 'type' and 'sort' parameters but doesn't provide additional syntax or usage details. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'List agents from A2ASearch, optionally filtered by type.' It specifies the verb ('List'), resource ('agents'), and source ('A2ASearch'), and mentions optional filtering. However, it doesn't explicitly differentiate from sibling tools like 'search_agents' beyond implying this is for browsing rather than searching.
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 some usage context: 'Use this to browse top agents by category.' This implies it's for general browsing rather than targeted searches, but it doesn't explicitly state when to use this vs. 'search_agents' or 'get_agent', nor does it mention any prerequisites or exclusions. The guidance is helpful but incomplete.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_agentsA
Search the A2ASearch directory for AI agents, MCP servers, CLI tools and agent skills. Returns name, description, type, stars, GitHub URL and capabilities for each result.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query — e.g. 'database', 'browser automation', 'memory' | |
| type | No | Filter by agent type (optional) | |
| limit | No | Number of results to return (1-20, default 10) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the search behavior and return format, but lacks details on rate limits, authentication needs, pagination, or error handling. It adds basic context but does not fully compensate for the absence of annotations, leaving gaps in behavioral understanding.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence and adds return details in the second. Both sentences earn their place by providing essential information without redundancy or fluff, making it highly efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (search with filters), no annotations, and no output schema, the description is reasonably complete. It covers the purpose, scope, and return format, but could improve by addressing behavioral aspects like rate limits or error cases. It's adequate but has minor gaps in full contextual coverage.
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 the schema already documents all parameters thoroughly. The description does not add any additional meaning beyond what the schema provides (e.g., it doesn't explain parameter interactions or provide examples beyond the schema's descriptions). Baseline score of 3 is appropriate as the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search'), the target resource ('A2ASearch directory for AI agents, MCP servers, CLI tools and agent skills'), and the return format ('Returns name, description, type, stars, GitHub URL and capabilities for each result'). It distinguishes from sibling tools like 'get_agent' (likely fetches a single agent) and 'list_agents' (likely lists all without search) by specifying search functionality with filtering.
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 searching the directory with a query and optional filters, but does not explicitly state when to use this tool versus alternatives like 'list_agents' (e.g., for browsing vs. targeted search) or 'get_agent' (e.g., for specific agent details). It provides some context but lacks explicit guidance on exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
Each tool has a clearly distinct purpose: get_agent retrieves detailed information for a specific agent, list_agents provides a filtered browse of agents by category, and search_agents performs a broader search across the directory. There is no overlap in functionality, making tool selection unambiguous.
All tool names follow a consistent verb_noun pattern (get_agent, list_agents, search_agents) with clear, descriptive verbs and pluralization where appropriate. This uniformity aids in predictability and readability.
With 3 tools, the count is slightly low but reasonable for the server's purpose of searching and browsing agents. It covers core operations (get, list, search), though additional tools like update or delete might be expected if the domain included management capabilities.
The tool set provides comprehensive coverage for searching and browsing agents, with no obvious gaps in this domain. However, it lacks CRUD operations (e.g., create, update, delete), which might be expected if the server also supported agent management, but based on the descriptions, the focus appears to be on discovery.
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