a2asearch-mcp
A2ASearch MCP-Server
10.752 KI-Agenten, MCP-Server, CLI-Tools und Agenten-Skills — in Sekunden durchsuchbar von Claude, Cursor oder jedem beliebigen MCP-Client.
Die einzige einheitliche Suchmaschine für das KI-Agenten-Ökosystem. Ein Paket. Jeder wichtige MCP-Server, jedes CLI-Tool, jeder Coding-Agent und jedes Agenten-Skill — indexiert und durchsuchbar.
npx a2asearch-mcp -- playwright
# → Finds playwright MCP server, CLI tools, related agentsWarum A2ASearch?
Das MCP-/Agenten-Ökosystem explodiert. Es gibt mittlerweile Tausende von Tools auf GitHub, npm und in verschiedenen Verzeichnissen — und keinen zentralen Ort, um sie zu finden. A2ASearch indexiert sie alle:
Wonach Sie suchen | A2ASearch hat es |
MCP-Server für Claude/Cursor | ✅ 2.000+ indexiert |
CLI-Tools für LLM-Workflows | ✅ |
KI-Coding-Agenten (Codex, Claude Code, etc.) | ✅ |
Agenten-Skills & Plugins | ✅ |
A2A-Protokoll-Agenten | ✅ |
Keine Authentifizierung erforderlich. Kein API-Key. Kostenlos.
Related MCP server: claude-oracle-mcp
Installation
Claude Desktop
Hinzufügen zu ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"a2asearch": {
"command": "npx",
"args": ["-y", "a2asearch-mcp"]
}
}
}Cursor
Hinzufügen zu .cursor/mcp.json:
{
"mcpServers": {
"a2asearch": {
"command": "npx",
"args": ["-y", "a2asearch-mcp"]
}
}
}Windsurf
Hinzufügen zu ~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": {
"a2asearch": {
"command": "npx",
"args": ["-y", "a2asearch-mcp"]
}
}
}Cline / Continue / OpenClaw
Das gleiche Format — fügen Sie es Ihrer MCP-Konfigurationsdatei hinzu:
{
"mcpServers": {
"a2asearch": {
"command": "npx",
"args": ["-y", "a2asearch-mcp"]
}
}
}MCP-Tools
search_agents
Durchsuchen Sie das gesamte Verzeichnis nach Schlüsselwörtern.
"Find MCP servers for browser automation"
"Search for AI agents that can do web research"
"Find CLI tools for working with LLMs"get_agent
Erhalten Sie vollständige Details zu einem bestimmten Agenten, einschließlich README, Fähigkeiten und Sternen.
"Get details for playwright"
"Tell me about claude-code"
"Show me the mem0 agent"list_agents
Durchstöbern Sie die Top-Agenten nach Typ und Sortierreihenfolge.
"List the top MCP servers by stars"
"Show me the newest AI coding agents"
"What are the top agent skills?"Beispiel-Prompts
Fragen Sie nach der Installation Ihren KI-Assistenten:
„Suche nach MCP-Servern, die mir bei der Arbeit mit Datenbanken helfen können“
„Was sind derzeit die beliebtesten KI-Coding-Agenten?“
„Finde Agenten-Skills für das Web-Browsing“
„Gibt es einen MCP-Server für Notion?“
„Erhalte vollständige Details zum ollama-Agenten“
CLI-Nutzung
Sie benötigen den MCP-Server nicht? Nutzen Sie die CLI direkt:
# 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 --newOder ohne Installation:
npx a2asearch-mcp -- playwright
npx a2asearch-mcp -- --type mcp database
npx a2asearch-mcp -- --get ollamaAgententypen
Typ |
| Beschreibung |
MCP-Server |
| Model Context Protocol-Server |
CLI-Tool |
| Terminal-Tools für LLM-Workflows |
KI-Coding-Agent |
| Autonome Coding-Agenten |
Agenten-Skill |
| Plugins und Skills für KI-Assistenten |
A2A-Agent |
| Agent-to-Agent-Protokoll-Agenten |
KI-Tool |
| Allgemeine KI-gestützte Tools |
REST-API
Der MCP-Server umschließt die kostenlose A2ASearch REST API. Nutzen Sie diese direkt, falls bevorzugt:
# 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"Keine Authentifizierung erforderlich.
Ein Tool einreichen
Sie kennen ein Tool, das fehlt? Reichen Sie es ein →
Der Index wird von der Community gepflegt. Einreichungen werden innerhalb von 24–48 Stunden geprüft und hinzugefügt.
Links
🌐 a2asearch.ai — durchsuchen Sie das vollständige Verzeichnis
📦 npm
🐛 Issues
Lizenz
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.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
3 tool updates
v1.0.0- First observed
get_agent - First observed
list_agents - First observed
search_agents
TDQS
Scored across 3 tools
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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