The mcp-server-mcpindex helps agents discover, compare, install, and pre-flight other MCP servers from within the agent loop, backed by an index of 3,500+ MCP servers. Here's what you can do:
Recommend MCP servers for a task (recommend_mcp_for_task): Describe a task in natural language and get the top 3 ranked MCP server recommendations with reasoning, install commands, and quality scores.
Search the MCP registry (search_mcp_servers): Perform keyword and semantic search across the full MCP server registry, with optional category filtering (e.g. database, browser, github) and configurable result limits.
Get exact install commands (get_install_command): Retrieve a ready-to-paste install command or JSON config for a specific MCP server and target client (Claude Desktop, Claude Code, Cursor, Gemini CLI, Cline, or Zed).
Compare multiple servers (compare_servers): Get a side-by-side comparison of 2–5 MCP servers, including quality scores, install paths, transport types, and environment variables.
Check tool trust before invocation (check_tool_trust): Get an advisory trust verdict (ALLOW / DENY / REVIEW / UNVERIFIED) for a specific tool on a specific server before calling it, helping agents implement pre-invocation safety screens.
Assess an entire server's trustworthiness (assess_server): Get an aggregated pre-flight trust verdict across all tools on a given MCP server, useful for deciding whether a server is worth integrating.
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-server-mcpindex Find an MCP server for web scraping"
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.
An MCP server for finding MCP servers, plus advisory trust verdicts agent frameworks can call before invoking a tool - from mcpindex.ai.
A drop-in MCP server that lets your agent discover, compare, install, and pre-flight other MCP servers from inside the agent loop. Backed by mcpindex.ai - the agent-native MCP server index of the official registry (live count at mcpindex.ai/stats), screened and drift-monitored daily.
Live site · npx mcp-server-mcpindex · Remote MCP · Install gate · Docs · Trust
npm install -g mcp-server-mcpindexThis is the directory / advisory client (recommend, search, trust). It does not install the in-path drift gate — that is curl -fsSL https://mcpindex.ai/install.sh | sh.
Requires Node 20+. Speaks both protocol eras on stdio: the 2026-07-28 revision (server/discover, per-request _meta envelope) and the initialize handshake every current client uses (2025-11-25 down to 2024-10-07), selected per connection.
Prefer not to install anything? mcpindex is also a hosted remote MCP server. Point any client that supports remote MCP (Claude connectors, Cursor, etc.) at:
https://mcpindex.ai/api/mcpStreamable HTTP, no credentials. Same six tools as the npm package.
claude mcp add --scope user mcpindex -- npx -y mcp-server-mcpindex@latestgemini mcp add -s user mcpindex npx -y mcp-server-mcpindex@latestRelated MCP server: filesystem-mcp
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"mcpindex": {
"command": "npx",
"args": ["-y", "mcp-server-mcpindex@latest"]
}
}
}
@latestkeeps you current: this is the advisory discovery server (not the in-path drift gate), so it carries no version pin —npxfetches the newest on your next host restart, no manual upgrade step.
Restart Claude Desktop. Then ask:
"Find me an MCP server that can read PDFs and write the contents to S3."
Claude calls recommend_mcp_for_task and returns the top 3 ranked servers with install commands.
Add to .cursor/mcp.json:
{
"mcpServers": {
"mcpindex": {
"command": "npx",
"args": ["-y", "mcp-server-mcpindex@latest"]
}
}
}Add to your Cline settings:
npx -y mcp-server-mcpindex@latestTool | What it does |
| Pass a natural-language task. Returns top 3 servers with reasoning, install commands, quality scores. |
| Keyword + semantic search across the full registry. Optional category filter. |
| Get the exact install JSON/CLI for a server + client (Claude Desktop, Claude Code, Cursor, Gemini CLI, Cline, Zed). |
| Side-by-side comparison of 2-5 servers - quality scores, install paths, env vars. |
| Pre-invocation advisory verdict for a specific tool on a server. Fail-CLOSED: returns UNVERIFIED when no verdict on file. |
| Aggregated pre-flight verdict across all tools on a server. Same shape as |
check_tool_trust is the directory client integration surface (not the in-path mcpindex-gate). It lets agent frameworks (Composio, Mastra, LangChain, DSPy, raw LLM-tool-call loops) ask for an advisory screen verdict before dispatching a call. At v1 you will see REVIEW or UNVERIFIED — not a safety clearance.
Using Mastra? The sibling package
@mcp-index/mastraships this exact screen as a ready-madebeforeToolCallhook -npm i @mcp-index/mastra, no wiring required.
{
"directive": "ALLOW" | "DENY" | "REVIEW" | "UNVERIFIED",
"status": "EVALUATED" | "PARTIAL" | "STALE" | "ERROR",
"granularity": "description-level" | null, // scope of a PARTIAL screen
"dimensions": [
{ "id": "tool_safety", "verdict": "PASS", "severity": "INFO" }
],
"expires_at": "2026-06-30T00:00:00Z",
"honest_limits": [
"conformance_monitored_not_enforced",
"calibrated_false_v1",
"advisory_deployment"
],
"verdict_contract_version": "1.0.0",
"server_id": "github",
"tool_name": "create_pull_request",
"source_url": "https://mcpindex.ai/api/v1/trust/tool/github/create_pull_request",
"fetched_at": "2026-05-28T18:42:11.118Z"
}The free-tier verdict ships directives + dimensions + freshness. Evidence quotes, LLM rationale, and chain history are paid-tier surfaces and intentionally omitted here.
Every v1 verdict ships with these three caveats, and your gate SHOULD surface them on every dispatch decision:
conformance_monitored_not_enforced - publishers self-declare; mcpindex monitors drift but does not block at the network layer.
calibrated_false_v1 - dimension severities are not yet calibrated against real-world incident data.
advisory_deployment - the verdict is advisory; the agent (or human reviewing the agent) is the decision-maker.
History anchoring: OTS Bitcoin-anchored history; Bitcoin-finalized at N=6 confirmations (~1 hr); pending in ~10 min. Sub-window precision asserted, not proven.
import { Client } from '@modelcontextprotocol/sdk/client/index.js';
import { StdioClientTransport } from '@modelcontextprotocol/sdk/client/stdio.js';
const mcpindex = new Client({ name: 'gate', version: '1.0.0' }, { capabilities: {} });
await mcpindex.connect(new StdioClientTransport({
command: 'npx', args: ['-y', 'mcp-server-mcpindex@latest'],
}));
// gateToolCall wraps any agent tool dispatch. Plug it in front of
// the LangChain / DSPy / Mastra / Composio tool-call hook.
async function gateToolCall({ serverId, toolName, invoke, askHuman }) {
const res = await mcpindex.callTool({
name: 'check_tool_trust',
arguments: { server_id: serverId, tool_name: toolName },
});
const verdict = JSON.parse(res.content[0].text);
// Pin the v1 caveats in the audit log no matter what.
audit.log({ verdict, caveats: verdict.honest_limits });
switch (verdict.directive) {
case 'REVIEW':
// Fail-CLOSED to human. Do NOT auto-execute on REVIEW.
// At v1 this is the common screened outcome (semantic-only).
return askHuman({ verdict, action: `${serverId}/${toolName}` });
case 'UNVERIFIED':
// No verdict on file (or upstream unreachable). Fail-CLOSED.
// Recommend human review. Do NOT fail-open to invoke().
return askHuman({
verdict,
action: `${serverId}/${toolName}`,
note: 'No trust verdict on file. Human review required before first use.',
});
case 'ALLOW':
// Reserved in the contract — not produced by the v1 public screen.
// Keep the branch for future conformance-earned ALLOW; do not expect it today.
return invoke();
case 'DENY':
// Reserved in the contract — not produced by the v1 public screen.
throw new Error(
`mcpindex denied ${serverId}/${toolName}: ${JSON.stringify(verdict.dimensions)}`,
);
default:
// Unknown directive. Fail-CLOSED.
return askHuman({ verdict, action: `${serverId}/${toolName}` });
}
}If the verdict endpoint is unreachable, returns 404, times out, returns malformed JSON, or has no verdict on file yet for that server, check_tool_trust returns directive: "UNVERIFIED" + status: "ERROR". It never silently coerces to ALLOW. Your gate code SHOULD treat UNVERIFIED as "human review required", never as "looks fine, ship it."
status is telemetry about screen completeness, distinct from the directive trust decision: EVALUATED (full screen), PARTIAL (only part of the surface, e.g. description-level — see granularity), STALE (verdict past its freshness window), ERROR (unreachable / no verdict on file). A PARTIAL screen is never reported as EVALUATED.
This is tested. See test/trust.test.mjs.
The trust client is also exported as a plain ES module:
import { checkToolTrust, assessServer } from 'mcp-server-mcpindex/src/trust.mjs';
const verdict = await checkToolTrust({
serverId: 'github',
toolName: 'create_pull_request',
});
if (verdict.directive !== 'ALLOW') {
// Hand to a human, log, or block.
}By default, calls go to https://mcpindex.ai. Override with MCPINDEX_API_BASE=... if you self-host.
The free tier is rate-limited to 60 req/min/IP. Paid keys are coming for higher throughput and the full evidence-bearing verdict (evidence quotes, LLM rationale, chain history).
Three ways to bring mcpindex into an agent, for different surfaces:
Package | Install | What it does |
|
| Directory + advisory screen as an MCP server: find servers by task, and |
|
| The same advisory screen wired into Mastra as a |
|
| In-path drift gate: |
Advisory screen vs drift gate: this package and @mcp-index/mastra ask mcpindex "has this tool been vetted?" (a network verdict). @mcp-index/sdk asks a different question locally: "did this tool's contract change since I pinned it?" Complementary, and none depends on another.
MIT.
Website: mcpindex.ai
Methodology: mcpindex.ai/methodology
Screen a server: mcpindex.ai/screen
Source: github.com/mcpindex-ai
Unofficial. Not affiliated with Anthropic.
Aggregated pre-flight trust assessment across all tools on an MCP server. Same verdict shape as check_tool_trust. Use for "is THIS server worth integrating?" decisions. v1 advisory; conformance monitored not enforced; verdicts may be UNVERIFIED if not yet probed.
| Name | Required | Description | Default |
|---|---|---|---|
| server_id | Yes | Server slug to assess. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description discloses behavioral traits: advisory nature, conformance monitoring not enforced, and potential UNVERIFIED verdicts. This adds valuable context beyond a simple read operation, though no side effects or auth needs are mentioned.
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?
Three concise sentences without redundancy: purpose, use, and behavioral notes. Front-loaded with the core action, 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?
The description covers purpose, usage guidance, verdict shape, and advisory nature. For a simple one-parameter tool with no output schema, it provides sufficient context, though explicit mention of return format would be slightly better.
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 single parameter 'server_id' is described in the schema as 'Server slug to assess.' The description does not add new meaning beyond usage context. With 100% schema coverage, a baseline 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 performs an aggregated pre-flight trust assessment across all tools on a server, using the specific verb 'assess' and resource 'server'. It distinguishes from sibling 'check_tool_trust' by noting aggregation, and clarifies the verdict shape is identical.
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?
Explicitly states the use case: 'is THIS server worth integrating?' decisions. It also provides context on when to be cautious with 'v1 advisory; conformance monitored not enforced; verdicts may be UNVERIFIED if not yet probed', guiding appropriate reliance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Pre-invocation advisory screen for a specific tool on an MCP server. Returns an advisory verdict object (directive ALLOW | DENY | REVIEW | UNVERIFIED, dimensions, freshness). At v1 the public screen produces REVIEW or UNVERIFIED only — ALLOW/DENY are reserved. Not the in-path gate (mcpindex-gate). Agents SHOULD treat UNVERIFIED as "human review required", never as ALLOW.
| Name | Required | Description | Default |
|---|---|---|---|
| server_id | Yes | Server slug (e.g. "github", "filesystem"). Same id used by search_mcp_servers. | |
| tool_name | Yes | Tool name as exposed by the server (e.g. "create_pull_request"). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It accurately describes behavior: returns advisory verdict with specific fields, v1 only produces REVIEW or UNVERIFIED. Does not mention side effects, but none expected. Could add authentication requirements, but sufficient for a read-only check.
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?
Three sentences, no fluff. Front-loads purpose, then constraints, then usage guideline. Every sentence 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?
For a simple two-parameter tool with no output schema, the description covers purpose, return value, version limitations, and usage advice. No gaps for correct 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 coverage is 100% (both parameters described in schema). Description adds no extra meaning beyond what schema provides. Baseline 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 purpose: a pre-invocation advisory screen for a specific tool. It uses specific verb 'check' and resource 'tool trust', and distinguishes from siblings like assess_server and search_mcp_servers by focusing on a single tool's trust level.
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?
Explicitly states when to use (pre-invocation advisory) and when not (not the in-path gate). Provides clear directive: treat UNVERIFIED as human review required. Distinguishes from sibling tools effectively.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Side-by-side comparison of 2-5 MCP servers - quality scores, install paths, transport types, env vars.
| Name | Required | Description | Default |
|---|---|---|---|
| slugs | Yes | Server slugs to compare. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the output content (quality scores, install paths, etc.) but does not mention any side effects, auth requirements, or that it is a read-only operation. It assumes a non-destructive nature, but this is not explicit.
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?
One sentence encapsulates the core functionality without any wasted words. It is front-loaded with the key action 'Side-by-side comparison' and details the compared attributes.
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 one-parameter tool with no output schema, the description is relatively complete by listing the compared attributes. However, it does not mention output format, ordering, or whether results are aggregated, which would enhance completeness.
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 100% for the single parameter 'slugs', with min/max items already defined. The description does not add semantic detail beyond what the schema provides, so it meets the baseline.
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 compares 2-5 MCP servers side-by-side, listing specific attributes (quality scores, install paths, etc.). This distinguishes it from siblings like assess_server (single server) and recommend_mcp_for_task (recommendation).
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 use for comparing multiple servers but does not explicitly state when to use it versus alternatives or any prerequisites. It could benefit from guidance on when to choose compare_servers over assess_server or search_mcp_servers.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the exact install command for a given MCP server and client. Returns a JSON block ready to paste into the client config.
| Name | Required | Description | Default |
|---|---|---|---|
| client | Yes | Target client. | |
| server_slug | Yes | Slug of the server (from search_mcp_servers or recommend_mcp_for_task results). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description discloses the tool returns a JSON command ready to paste. It does not mention any side effects or authentication, but for a retrieval tool this is sufficiently transparent.
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 wasted words, perfectly front-loaded with the action and output.
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 two well-described parameters and no output schema, the description is complete: it explains what it does and what it returns.
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 100% and both parameters have descriptions. The description adds marginal value beyond the schema, so baseline 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 retrieves the install command for a given server and client, and specifies the output format (JSON block). This distinguishes it from sibling tools like search or recommend.
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 by the description, but there is no explicit guidance on when to use this tool versus alternatives, nor conditions to avoid usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recommend the best MCP servers for a natural-language task. Returns top 3 ranked picks with reasoning, install commands, and quality scores. Use this when the user asks for the right MCP server for a task they want to do.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | Natural-language description of the task, e.g. "read PDFs and write to S3" or "search GitHub and open a PR". |
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 output structure: 'Returns top 3 ranked picks with reasoning, install commands, and quality scores.' This is sufficient for a read-only recommendation tool. It could mention ranking criteria or data sources for greater transparency, but the current description is clear.
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: the first states purpose and output, the second gives usage guidance. It is front-loaded with key information and contains no superfluous words.
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, no output schema), the description adequately covers what the tool does and returns. It mentions the top 3 picks, reasoning, install commands, and quality scores. It could be enhanced by referencing sibling tools for alternative uses, but it is sufficiently 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 100% coverage with a description for the 'task' parameter. The tool description does not add additional examples or constraints beyond the schema's example. Thus, per the baseline, a 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 purpose: 'Recommend the best MCP servers for a natural-language task.' It specifies the verb (recommend), resource (MCP servers), and context (natural-language task). This distinguishes it from sibling tools like search_mcp_servers (which lists servers) and assess_server (which evaluates a single server).
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 explicitly says when to use the tool: 'Use this when the user asks for the right MCP server for a task they want to do.' It lacks explicit when-not-to-use or alternatives, but the context is clear enough for most agents.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Keyword + semantic search across the full MCP server registry. Use when the user knows what tool category they want but not which server.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 10, max 50). | |
| query | Yes | Search query. | |
| category | No | Optional category filter (e.g. database, browser, github, productivity). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description bears full responsibility for behavioral disclosure. It only states the search capability, missing details on read-only nature, authentication, rate limits, or any side effects. Minimal transparency beyond basic function.
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, front-loaded with the action and purpose. Every word adds value. No redundancy or unnecessary 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?
For a simple search tool with 3 parameters and no output schema, the description is adequate but has gaps. It does not explain result format, pagination, or search behavior beyond 'keyword + semantic'. Could be more complete given lack of annotations and output 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?
Schema coverage is 100%, so baseline is 3. The description does not add meaning beyond the schema; it mentions keyword + semantic search but that is already implicit. No additional parameter guidance.
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 clearly states the tool performs keyword + semantic search across the MCP server registry, with a specific use case ('when the user knows what tool category they want but not which server'). This distinguishes it from sibling tools like assess_server or get_install_command.
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?
Provides explicit context for when to use the tool, but does not mention when not to use it or list alternatives. The guidance is clear and useful, but lacks exclusion criteria.
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.
v0.3.7assess_servercheck_tool_trustcompare_serversget_install_commandrecommend_mcp_for_tasksearch_mcp_serversScored across 6 tools
Each tool targets a clearly distinct purpose: search, recommend, compare, install, and two levels of trust assessment (server-level and tool-level). Even the trust tools are well-differentiated by scope.
All tool names follow a consistent verb_noun pattern using snake_case: assess_server, check_tool_trust, compare_servers, get_install_command, recommend_mcp_for_task, search_mcp_servers.
With 6 tools covering search, recommendation, comparison, installation, and trust, the number is well-scoped for a server registry/helper. No tools feel redundant or unnecessary.
The set covers the core workflow of finding, evaluating, and installing MCP servers. A minor gap is the lack of a tool to retrieve full metadata for a single server (e.g., description, version), but search and comparison partially fill that need.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Search and install 4,000+ security-scanned MCP servers from inside any MCP-aware AI client.
Search and get install details on MCP servers, right from your agent -- a unified marketplace index.
Search, vet & assemble MCP servers from your agent: verified tools, risk labels, and trust scores.
Capability registry for the agentic economy. Semantic search over verified MCP server listings.
