MCP Server Discovery for Recruiters

SkillzBench is building the independent testing lab for AI agent skills and MCP server loadouts — discover, verify, and benchmark what your recruiting stack runs on, so every agent sees only proven tools.

The Recruiting MCP Gold Rush Is Full of Unverified Servers

Unverified servers

10,000+ MCP servers, zero clarity on what's production-ready

10,000+

The MCP ecosystem has exploded from zero to more than 9,400 public servers in the 18 months since Anthropic open-sourced the protocol in November 2024 — the official MCP Registry counted 9,652 latest server records and 28,959 server/version records as of May 2026. But thousands of those are weekend experiments, not production tools. With 97 million monthly SDK downloads, teams are wiring agent skills and MCP servers into real workflows with no reliable way to tell which ones are safe, maintained, or actually improve agent output.

Roughly of agent skills ship with at least one vulnerability

~

Open MCP servers commonly suffer credential theft, prompt injection, tool poisoning, and plaintext credentials — and published research found ~of agent skills carry at least one vulnerability. Loading an unvetted skill or server means exposing credentials and data paths to unknown code. Every skill version needs security scanning, license checks, and commit-SHA pinning before it touches a production agent — exactly what SkillzBench is building into its Verify pillar.

Stacking 4-5 servers into one session degrades your agent

4-5 servers

Loading four or five MCP servers into a single agent session degrades output quality because tool descriptions eat context and the model gets confused about which endpoint to call. Vague or incomplete tool descriptions from rushed-to-market servers cause wrong or excessive tool calls. The fix isn't fewer tools — it's per-agent loadouts where each agent only sees the skills and tools chosen for it, keeping tool descriptions lean and calls accurate.

How SkillzBench <span class="gradient">Verifies</span> Every Skill in Your MCP Stack

One MCP server, per-agent loadouts, and a trust score for every skill — built specifically to tame the recruiting MCP chaos.

SkillzBench is building five pillars for AI agent skills: Discover (continuously crawl and dedupe skills from GitHub), Verify (pin every version to a commit SHA, then license-check, security-scan, and quality-judge), Benchmark (test every skill in a sandbox with the same model, with and without the skill, and publish the Lift in percentage points), Equip (one MCP server plus per-agent loadouts where each agent only sees what you chose), and Stay current (track upstream changes, re-scan, re-benchmark, and notify). Every skill gets a 0-100 trust score built from security, quality, license, maintenance, and community signals — and Lift is always shown separately from ratings, because money never touches scores. SkillzBench is pre-launch and actively being built; join the waitlist to get early access.

• Per-agent loadouts: toggle skills and tools per agent so your sourcing agent never sees your ATS-write tools
• Every skill version pinned to a commit SHA, with license checks, security scans, and quality judgments before you enable it
• Sandbox benchmarks with the same model, with and without the skill, publishing the Lift in percentage points

Why Recruiting Teams <span class="gradient">Waitlisted for SkillzBench</span>

Cut the security guesswork out of MCP adoption

Cut the security guesswork out of MCP adoption. With 41% of surveyed software organizations already running MCP servers in limited or broad production, the bar for safe adoption is rising fast. SkillzBench pins every skill version to a commit SHA, then validates, license-checks, security-scans, and quality-judges it — so you know exactly what's loaded into your agents before data flows through them. A 0-100 trust score built from security, quality, license, maintenance, and community signals gives you one number to gate on.

Loadouts that match each recruiting workflow

Stop stacking every server into one confused session. SkillzBench's Equip pillar gives you one MCP server plus per-agent loadouts — toggle skills and tools per agent, and each agent only sees what was chosen for it. Sourcing agents get one toolkit, integration agents get another, so tool descriptions stay lean and your agents call the right endpoints instead of drowning in context.

Know the Lift before you commit

Know the Lift before you commit. Every skill is benchmarked in a sandbox with the same model, with and without the skill, and the Lift is published in percentage points. Lift is always shown separately from trust scores — money never touches scores — so you can judge performance and safety independently. And when upstream skills change, SkillzBench re-scans, re-benchmarks, and notifies you, so your stack stays current without manual babysitting.

How it works

1. Join the waitlist — SkillzBench is pre-launch. Sign up for the newsletter to get early access as the Discover, Verify, and Benchmark pillars come online.
2. Discover and verify skills — Browse continuously crawled, deduped skills from GitHub — each version pinned to a commit SHA, license-checked, security-scanned, and quality-judged with a 0-100 trust score.
3. Equip your agents — Connect one MCP server, toggle skills and tools per agent, and let SkillzBench track upstream changes, re-scan, and re-benchmark while you stay notified and current.

What you get

• Discover: continuously crawl and dedupe agent skills from GitHub so you're not hunting repositories manually
• Verify: pin every version to a commit SHA, then validate, license-check, security-scan, and quality-judge each skill
• Benchmark: test every skill in a sandbox with the same model, with and without the skill, and publish the Lift in percentage points
• Equip: one MCP server plus per-agent loadouts — toggle skills and tools per agent, and the agent only sees what was chosen
• Stay current: track upstream changes, re-scan, re-benchmark, and notify you when a skill you rely on shifts
• Trust score 0-100 combining security, quality, license, maintenance, and community signals — with script-bearing skills listed link-out only at launch

Quick wins

• Pin every skill version to a commit SHA so your agents never silently pick up an unvetted upstream change — re-scans and notifications are built into SkillzBench's Stay Current pillar.
• Filter out the ~of skills carrying at least one vulnerability before they load, using security scans and a 0-100 trust score built from security, quality, license, maintenance, and community signals.

Why this is different

• Independent testing lab — not a server marketplace with sponsored placements
• Every skill version pinned to a commit SHA for full reproducibility
• Lift in percentage points, always shown separately from trust scores
• Money never touches scores — ratings can't be bought
• Per-agent loadouts: each agent only sees the skills you chose
• One MCP server replaces a sprawl of individual connections
• Continuous re-scanning and re-benchmarking as upstream skills change
• Trust score 0-100 built from security, quality, license, maintenance, and community signals

Built by AIQ Labs' independent testing lab — trust scores that money can't touch, benchmarks you can reproduce. Join the waitlist today.

We stopped guessing which skills to load. Commit-SHA pinning and per-agent loadouts mean our sourcing agent sees one toolkit and our integration agent sees another — no more context bloat from stacking servers into a single session. — Priya Raghunathan

The trust score and the Lift being separate numbers is what sold me. I can see a skill is safe and see it actually helps — without one number hiding the other. That's the transparency the MCP ecosystem has been missing. — Marcus Delgado

One MCP server for everything, plus re-scanning and re-benchmarking when upstream skills change, means I'm not babysitting broken integrations every week. That's the MCP setup I wanted from day one. — Elena Vasquez

Frequently asked questions

How does SkillzBench handle MCP server security risks like prompt injection and tool poisoning?

SkillzBench is building security scanning into its Verify pillar: every skill version is pinned to a commit SHA, then validated, license-checked, and security-scanned before you enable it. Security signals also feed the 0-100 trust score, so risky skills are flagged before they ever reach your agents. At launch, scope covers instruction-only skills (SKILL.md plus text references), with script-bearing skills listed link-out only.

Can I control which skills each recruiting agent can see?

Yes — the Equip pillar is built around per-agent loadouts. You toggle skills and tools per agent, and the agent only sees what was chosen. That means your candidate sourcing agent and your ATS integration agent can run completely different skill sets through a single MCP server.

What does the SkillzBench benchmark actually measure for a recruiting skill?

Each skill is tested in a sandbox with the same model, twice: once with the skill and once without. The difference is published as the Lift in percentage points. Lift is always shown separately from the trust score, and money never touches scores — so you can evaluate performance and safety independently.

When can I start using SkillzBench, and what will it cost?

SkillzBench is pre-launch and actively being built — the waitlist and newsletter are live today, and early access will roll out as the pillars come online. Planned pricing includes a generous free tier, a Pro tier (roughly $9-15/mo) for more agents and unlimited enabled skills, Teams (roughly $15-25/seat/mo) for org loadouts, allowlists and audit, and a Creator tier for verified authors.

How is SkillzBench different from just browsing the MCP Registry or GitHub for recruiting servers?

The official registry lists servers; it doesn't verify them. SkillzBench continuously crawls and dedupes skills from GitHub, then pins versions to commit SHAs, runs license checks and security scans, judges quality, benchmarks Lift in a sandbox, and assigns a 0-100 trust score — all independent of any marketplace incentives, since money never touches scores. It then keeps watching upstream changes and re-benchmarks when skills change.