AgentRank vs Smithery vs Glama vs MCP.so: Comparing Every MCP Directory (2026)
There are now six major places to find MCP servers. They differ dramatically in coverage, ranking methodology, API access, and what they're actually for. This is a direct, data-driven comparison of AgentRank, Smithery.ai, Glama.ai, MCP.so, mcp-get.com, and the official MCP registry — what each does well, what it doesn't, and which one to use for what.
Side-by-side comparison
The table below covers the six platforms across five dimensions. "Scored" means the platform publishes a composite quality score per tool — not just a view count or star count. "Open data" means the underlying index or scoring methodology is publicly documented.
| Platform | Coverage | Update freq. | Scoring | API? | Open data? |
|---|---|---|---|---|---|
| AgentRank agentrank-ai.com | 25,000+ | Nightly | Composite (5 signals) | Yes | Yes |
| Smithery.ai smithery.ai | 4,000+ | Continuous | Usage-based | Yes | No |
| Glama.ai glama.ai | Curated subset | As reviewed | Manual review + security scan | Yes | No |
| MCP.so mcp.so | 3,000+ | Community-driven | None (alphabetical) | No | No |
| mcp-get.com mcp-get.com | ~200 curated | Manual PR process | None (flat list) | No | Yes |
| Official MCP Registry registry.modelcontextprotocol.io | ~87 | Manual additions | None (curated) | No | No |
Coverage figures as of March 2026. "Scored" = platform publishes a composite quality metric beyond raw popularity.
Coverage and completeness
The coverage gap between the largest and smallest platforms is roughly 300x. AgentRank indexes 25,000+ repositories. The official MCP Registry lists around 87. These numbers reflect fundamentally different philosophies, not one platform being lazy.
Breadth matters when you're exploring. If you're building an agent workflow and want to know whether an MCP server exists for a particular tool or API, a 25,000-repo index will find things a curated list of 200 will miss. The tradeoff is signal-to-noise: broad indexes need better ranking to stay useful.
Curation matters when you're deploying. If you're selecting a server for a production workflow, evidence of review — security scanning, code inspection, human judgment — has real value even if the list is shorter.
Most developers need both. A broad index for initial discovery, followed by quality signals to filter the candidates down.
Scoring and ranking methodology
Composite quality score (AgentRank)
AgentRank is the only platform in this comparison that computes a transparent composite quality score per tool. Five GitHub signals are weighted and combined into a 0–100 number: freshness (25%), issue health (25%), inbound dependents (25%), stars (15%), and contributors (10%).
The weights reflect a specific opinion: maintenance signals (freshness, issue health) should outweigh popularity signals (stars) because they predict future reliability better than historical fame. A server with 50 stars and a 95% issue close rate and weekly commits scores higher than a 3,000-star repo that hasn't been touched in 14 months.
The methodology is publicly documented. The weights are transparent. Every tool in the index is scored by the same formula — no editorial exceptions.
Usage-based ranking (Smithery.ai)
Smithery surfaces tools by installation and usage signals — how many times a server has been deployed or run through its platform. This is a reasonable proxy for real-world adoption, but it measures Smithery-specific usage, not ecosystem-wide usage. A server with high GitHub activity and widespread real-world use but low Smithery install counts will rank poorly. The feedback loop amplifies tools that are already popular on the platform.
Manual review + security scan (Glama.ai)
Glama applies a different filter: human review and automated security scanning before inclusion. What the platform provides is a security clearance signal, not a quality comparison. All tools in Glama's list passed the security bar; there's no composite score for comparing two tools that both passed.
No ranking (MCP.so, mcp-get.com, Official Registry)
MCP.so, mcp-get.com, and the official MCP Registry provide flat lists organized by category or alphabetically. There is no signal for comparing two tools within a category. The choice falls entirely on the developer.
API availability
As AI agents themselves become the primary consumers of tool directories, API access becomes critical. An agent that needs to select an MCP server should be able to query a directory programmatically, not scrape a webpage.
AgentRank API
REST API providing access to the full scored index. Query by name, category, score threshold, or keyword. Returns rank, score breakdown, GitHub metadata, and last-crawled data. Suitable for automated tool selection in agentic workflows.
Public REST API · JSON responses · No key required for basic access
Smithery.ai API
Smithery provides an API for accessing its server registry, primarily oriented toward deployment operations rather than discovery. The API supports listing, searching, and triggering hosted server runs. Requires authentication.
REST API · Auth required · Deployment-focused
Glama.ai API
Glama offers API access to its curated directory. Coverage is limited to the manually reviewed subset, but tools returned have passed security vetting. Useful as a secondary validation layer after initial discovery via a broader index.
REST API · Auth required · Curated subset only
MCP.so — No public API
MCP.so is a web directory only. There is no documented public API. Discovery must happen through the browser interface or by scraping. Not viable for agentic workflows.
No API · Browse-only
mcp-get.com — CLI only
mcp-get is a command-line package manager. It provides programmatic installation via CLI, not a browseable API. The underlying package list is a JSON file in a public GitHub repository — queryable but not structured for discovery queries.
CLI interface · No REST API · JSON package list on GitHub
Official Registry — No public API
The official MCP Registry at registry.modelcontextprotocol.io does not publish a documented REST API for third-party consumption. The registry is designed for vendors seeking official listing, not for developer discovery queries.
No API · Vendor submission process only
Platform deep dives
AgentRank
25,000+ tools · Nightly scoring · APIAgentRank indexes the full GitHub ecosystem nightly and scores every tool on five transparent signals. The goal is to answer the question a developer actually has: "Among the dozen MCP servers that do X, which one is most likely to still be maintained six months from now?"
The score is designed to surface active projects over historically popular ones. Freshness (25%) and issue health (25%) together account for half the score — the same signals that predict whether you'll get a response if you file a bug. Inbound dependents (25%) measure real ecosystem adoption, not just interest. Stars (15%) and contributors (10%) fill out the rest.
The index is fully open: score methodology is documented, signal weights are public, and the REST API is accessible without authentication for basic queries. Tools can also claim their listing and add context to their entry.
- Largest coverage in the category: 25,000+ repositories
- Transparent composite quality score (0–100)
- Nightly crawl — scores reflect commits from yesterday, not last year
- Public REST API for agentic tool selection
- Score methodology documented and reproducible
Limitations: GitHub-only signals for now. npm, PyPI, and Docker Hub adoption data are not yet incorporated. Tools not on GitHub (private/proprietary) are not indexed.
Smithery.ai
4,000+ tools · Hosted deployment · APISmithery occupies a distinct niche: it's not just a directory, it's a hosting platform. You can deploy and run MCP servers directly through Smithery without managing your own infrastructure. For developers who want zero-configuration MCP access, this is a significant differentiator.
The directory is a byproduct of the hosting business: Smithery lists servers that can be deployed on its platform. Coverage (4,000+) is solid for the most popular servers but incomplete relative to the full ecosystem. Ranking is based on Smithery-platform usage signals — servers with more Smithery installs rank higher, regardless of quality or maintenance outside the platform.
- Hosted server deployment — run MCP servers without infrastructure
- One-click install into compatible clients
- API for programmatic access to the registry
- Growing ecosystem of platform-specific tooling
Limitations: Coverage limited to Smithery-compatible deployments. Ranking reflects platform usage, not objective quality signals. Maintenance activity and issue health are not factored into visibility.
Glama.ai
Curated · Security-reviewed · APIGlama applies security scanning and manual review before listing any server. For organizations with strict security requirements — enterprises, regulated industries, teams where installing an unvetted tool is a compliance issue — this is genuinely valuable. It means every listed tool has been looked at by a human and run through automated security checks.
The limitation is inherent to the model: manual review doesn't scale to the speed at which the MCP ecosystem grows. Hundreds of new repositories appear each week; Glama's reviewed list will always represent a small fraction of what's available.
- Security scanning and manual review for every listed tool
- API access to the curated directory
- Strong signal for security-sensitive selection decisions
Limitations: Coverage is limited by manual review capacity. No composite quality score for comparing tools within the vetted set. Ecosystem coverage will always lag broad automated indexes.
MCP.so
3,000+ tools · Community directoryMCP.so is a community-run web directory for browsing MCP servers by category. The site aggregates submissions from the community and provides a reasonable browsing interface. There is no composite quality score, no freshness indicator per tool, and no programmatic API. It functions as a search-and-browse tool, not a ranked index.
Coverage at 3,000+ is meaningful but incomplete. MCP.so captures many mainstream servers but misses a large portion of the ecosystem that hasn't been manually submitted. Community-driven means coverage depends on who happens to submit their server.
- Clean browsing interface organized by category
- Community submissions keep popular servers visible
- No login required for browsing
Limitations: No quality scoring. No freshness signals. No API. Coverage depends on manual community submissions rather than automated crawling. Not viable for agentic or programmatic use cases.
mcp-get.com
~200 curated · CLI package managermcp-get is a different product category entirely — it's a package manager, not a discovery tool. The command-line interface lets you install MCP servers directly into Claude Desktop and other compatible clients with a single command:
npx @michaellatman/mcp-get@latest install @modelcontextprotocol/server-github The underlying registry is a curated JSON file (~200 entries) maintained via GitHub PRs. There is no quality scoring, no freshness data, and no discovery UI. mcp-get is most useful as an installation mechanism after you've already decided which server to use.
- One-command installation into Claude Desktop and compatible clients
- Open source package list (JSON on GitHub)
- No manual configuration file editing required
Limitations: Not a discovery tool. 200-entry curated list misses the vast majority of the ecosystem. No scoring, no API, no freshness signals. Intended to complement directories, not replace them.
Official MCP Registry
~87 tools · Certification layerThe official registry at registry.modelcontextprotocol.io is not a developer discovery tool. It's a certification layer — vendors submit MCP servers for official recognition by the MCP team, and the list is curated by Anthropic. At ~87 entries, it covers a tiny fraction of the ecosystem.
What it does represent is a credibility floor. Anything listed here has explicit endorsement from the team that maintains the MCP protocol. For vendors building commercial MCP servers, official listing is a meaningful signal. For developers searching for tools, this list alone will rarely be sufficient.
- Official Anthropic endorsement for listed tools
- High bar for inclusion — meaningful credibility signal
- Best starting point for understanding the reference implementation landscape
Limitations: ~87 entries. No quality scoring within the set. No API for programmatic access. Designed for vendor certification, not developer discovery.
The verdict: which to use
"Does an MCP server exist for X?" — Broad discovery
Use AgentRank first. 25,000+ indexed repos means you'll find niche tools that smaller directories miss. The quality score helps filter results immediately. Smithery.ai is a reasonable secondary option with a polished UI if you're also open to hosted deployment.
"Which of these five candidates is the best?" — Quality comparison
Use AgentRank. It's the only platform with a transparent composite score across all candidates. Sorting by score and examining the signal breakdown (freshness, issue health, dependents) surfaces which tool will likely still be maintained when you need support. No other platform in this comparison provides this signal.
"I need a vetted, security-reviewed option" — Enterprise/regulated
Filter through Glama.ai as a secondary step. Discover candidates with AgentRank's broad index, then cross-check against Glama's reviewed list. Anything that appears in both has both quality signals and security vetting — the strongest possible filter for production selection.
"I want to install a server without editing config files" — Friction-free setup
Use mcp-get.com for installation after you've selected a tool. Find the server you want via AgentRank or another directory, then use mcp-get to install it into Claude Desktop in one command. The two tools are complementary.
"I'm building an agent that selects MCP tools programmatically" — Agentic use
Use the AgentRank API. It's the only platform in this comparison offering open REST access to a scored, 25,000-repository index without requiring authentication for basic queries. Smithery's API covers its subset but is deployment-oriented rather than discovery-oriented.
"I'm building an MCP server and want maximum visibility" — Submitters
Target multiple platforms. Submit to the official MCP Registry for credibility. List on Smithery.ai for deployment reach. Claim your listing on AgentRank so your tool appears in the scored index with the description and context you want. All three together maximize your discovery surface across different developer segments.
Browse the AgentRank index: 25,000+ MCP servers ranked by composite quality score — updated nightly.
API access: Query the index programmatically for agentic tool selection. Full score breakdowns, metadata, and category filtering. See the API docs.
Methodology: Freshness 25%, issue health 25%, inbound dependents 25%, stars 15%, contributors 10%. Full methodology documented here.
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