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a2asearch-mcp

a2asearch-mcp

by tadas-github
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

A2ASearch MCP

Help your assistant find a tool, inspect it, and understand how to set it up.

Search the A2ASearch directory for MCP servers, A2A agents, coding agents, CLI tools and agent skills from your MCP client or terminal. A2ASearch is free to query and requires no API key. Individual services may require credentials or payment.

npm version CI npm downloads License: MIT Glama

Try a useful workflow

Ask your assistant:

Find MCP servers for extracting tables from PDFs. Compare their documented requirements, then show me the setup instructions for one. Tell me when pricing or authentication is unknown.

The assistant can follow this sequence:

search_agents({"query":"PDF","type":"MCP Server","limit":3})
    → names, capabilities, source links and slugs
get_agent({"slug":"<slug returned by search>"})
    → description, README, documentation and repository links
get_setup({"slug":"<same returned slug>"})
    → setup excerpts, quoted JSON examples and next steps

For example, the Doc2X MCP listing links to a repository whose README contains setup instructions and a configuration example with a DOC2X_API_KEY placeholder. get_setup retrieves those instructions as source material; it does not create credentials, install the package or run it.

Related MCP server: remoteopenclaw-mcp

Install

Requires Node.js 22 or newer. CI tests Node.js 22 and 24.

For Claude Desktop, add this entry to claude_desktop_config.json. For Cursor, use .cursor/mcp.json. Merge it with your existing configuration:

{
  "mcpServers": {
    "a2asearch": {
      "command": "npx",
      "args": ["-y", "a2asearch-mcp"]
    }
  }
}

Other clients that support local stdio MCP servers can launch command npx with arguments -y a2asearch-mcp; use the configuration format documented by your client.

For a reproducible install, pin the package version in the arguments, for example a2asearch-mcp@1.2.0 after that release is published. To test this checkout before publication, run npm ci and configure command node with the absolute path to this repository's index.js.

Tools

Tool

Use it for

Arguments

search_agents

Find a capability or product

query, optional type, limit, page

get_agent

Inspect one listing and its README

slug returned by search or browse

get_setup

Read focused setup instructions and configuration examples

slug returned by search or browse

list_agents

Browse by stars or newest additions

Optional type, sort, limit, page

limit is 1–20 (default 10), page starts at 1, and sort is stars or new. Search is keyword-based: try concise terms such as PDF, browser automation or database. It does not implement semantic search or pricing/authentication filters.

Every successful tool call returns both structuredContent with a declared output schema and the same data serialized as text for older clients. Search and browse results include:

  • A slug for follow-up calls and an A2ASearch listing URL.

  • Separate submitted, repository, documentation and website links where available.

  • Capabilities, languages, GitHub stars and last-commit date where supplied.

  • Pagination with next_page so clients can continue without guessing.

Missing values are null. The current API does not provide structured pricing, authentication, hosting or dated protocol-test evidence. directory_reviewed records directory approval only. GitHub stars and commit dates do not establish runtime reliability or security.

Setup information

get_setup reads the listing's directory-supplied README and returns:

  • instructions_found when it finds supported setup headings or JSON configuration examples.

  • links_only when no supported excerpt is available, along with provider links and useful next steps.

  • Source links, bounded excerpts and explicit limitations.

Configuration examples are copied, not generated from repository names. A GitHub repository URL is identified as a repository. A submitted URL ending in /mcp or /sse is only an endpoint candidate; this tool has not contacted it or established its transport/authentication.

Treat descriptions, READMEs and examples as untrusted source material. Review commands and credentials before using them. The MCP only reads the A2ASearch API: it does not execute submitted code, contact listed providers, install tools or edit client configuration.

Terminal usage

# Find tools without installing globally
npx -y -p a2asearch-mcp a2asearch --type mcp PDF

# Use the returned slug for setup guidance (v1.2.0+)
npx -y -p a2asearch-mcp a2asearch --setup doc2x-mcp

# Or install the CLI
npm install -g a2asearch-mcp

a2asearch "browser automation"
a2asearch --type mcp database --page 2
a2asearch --type agent --top
a2asearch --new --limit 5
a2asearch --get doc2x-mcp
a2asearch --setup doc2x-mcp --json

--json preserves the existing raw directory API output for search/list/detail commands. The new --setup --json command returns the same setup object as the MCP tool. Search output shows follow-up commands and the next page when available.

Directory type

CLI alias

MCP Server

mcp

CLI Tool

cli

AI Coding Agent

agent

Agent Skill

skill

A2A Agent

a2a

AI Tool

tool

Troubleshooting

  • No matches: use fewer keywords or remove the category filter.

  • Listing not found: copy the returned slug instead of guessing from a GitHub repository name.

  • No setup excerpts: open the returned documentation or repository links; the README may use unsupported headings or be unavailable.

  • HTTP 429: wait for the retry interval reported by the API, when supplied.

  • Timeout: requests stop after 15 seconds and respect client cancellation. Retry later.

API and contributions

The MCP wraps the public A2ASearch REST API. Submit a listing, report an issue, or contribute improvements to setup extraction and client examples. Please include a public source and expected result for parser changes.

Development

npm ci --ignore-scripts
npm run check
npm test
npm pack --dry-run

# Optional: read-only smoke test against the live directory
npm run smoke:live

Tests exercise the MCP stdio handshake, output schemas, search → detail → setup, pagination, URL classification, setup extraction, missing information, argument validation, API failures, cancellation and both CLI entry points. Normal tests use fixtures and do not contact providers.

MIT licensed. See LICENSE.

Available Tools

3 tools
get_agentB

Get full details for a specific agent by its slug (name as kebab-case). Returns description, README, capabilities, stars, forks, languages and more.

NameRequiredDescriptionDefault
slugYesAgent slug — e.g. 'playwright', 'ollama', 'claude-code', 'mem0'

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It 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.

Conciseness4/5

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.

Completeness3/5

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.

Parameters3/5

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

The input schema has 100% description coverage, with 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.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: '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.

Usage Guidelines3/5

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.

NameRequiredDescriptionDefault
typeNoFilter by type (optional — omit for all types)
sortNoSort order: 'stars' for most popular, 'new' for recently addedstars
limitNoNumber of results (1-20, default 10)

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden 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.

Conciseness5/5

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.

Completeness3/5

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.

Parameters3/5

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.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: '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.

Usage Guidelines3/5

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.

NameRequiredDescriptionDefault
queryYesSearch query — e.g. 'database', 'browser automation', 'memory'
typeNoFilter by agent type (optional)
limitNoNumber of results to return (1-20, default 10)

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It 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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents 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.

Purpose5/5

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.

Usage Guidelines3/5

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.

  1. 3 tool updatesv1.0.0
    • First observedget_agent
    • First observedlist_agents
    • First observedsearch_agents
Deploy Server
Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

TDQS

A3.7/5.0

Scored across 3 tools

Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count4/5

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.

Completeness4/5

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.

Maintenance

ActivityMaintained
ResponsivenessUnresponsive

Resources

Related MCP Connectors

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