"MCP servers that support SSE connections" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Your agent verifies its change before commit; PR review that knows what depends on your change.
An MCP server that compiles ASN.1 schema definitions into executable, native source code for a variety of languages (like Python, Rust).
**Can AI actually read your page?** ChatGPT, Perplexity, Claude and Google's AI Overviews fetch pages very differently from your browser — no JavaScript, tight timeouts, and a robots.txt rulebook of their own. Lekta fetches a URL exactly the way they do and grades what survives, **A+ to F**. This is the technical half of **AEO** (answer engine optimization) and **GEO** (generative engine optimization): before a model can cite you, it has to be able to fetch you, parse you, and find one sentence worth quoting. **The loop this server was built for:** `Audit https://mysite.com/pricing with Lekta, apply the fixes it lists, audit it again, and show me the difference.` Your agent gets a graded verdict, a ranked fix plan with the exact markup to paste, and a diff that proves the change landed. Repeat until A+. **Four layers, 100 points:** **Access** 25 — do the ~17 AI crawler tokens get past robots.txt? **Indexability** 25 — how much content survives without JavaScript? **Answerability** 30 — is there a single quotable sentence an engine can lift? **Recency** 20 — can a model tell when this page was last true? **What this is not:** a rank tracker. Lekta will not tell you how often ChatGPT mentions your brand. It tells you whether your page can be read and quoted when it does — the part you can actually fix. **No black box.** Every finding cites its basis — an RFC, a vendor doc, or a dated measurement we ran. The engine is versioned with a public changelog: a score never moves without a published shift table. **Tools:** `lekta_audit` (fresh fetch) · `lekta_report` (cached read) · `lekta_fix_plan` (ranked, paste-ready) · `lekta_diff` (before/after) · `lekta_my_sites` Listing tools is open. Tool calls need a free key from lekta.dev/en/panel/api — send `Authorization: Bearer lekta_…` or `x-api-key`. Cached reads, fix plans and diffs cost nothing; only fresh fetches count against the daily limit. **Topics:** AEO · GEO · AI SEO · LLM SEO · answer engine optimization · generative engine optimization · AI crawler access (GPTBot, ClaudeBot, PerplexityBot, Google-Extended) · JavaScript-free indexability · structured data · content freshness
Check that your AI is being logical. Free tool that mathematically catches contradictions in agent reasoning. No account needed. Also offers paid guardrails that converts natural language to formal verification proofs, that anyone can check succinctly.
Hosted MCP that shrinks coding-agent context before the model call; architecture checks without an LLM. Zero data retention.
An MCP server that gives your AI access to the source code and docs of all public github repos
Hosted out-of-band AST symbolic deconstruction and Gate 7 runtime circuit breaker for autonomous AI agents. Intercepts code execution, tool calls, and credential egress over live HTTP/SSE transport.
Stop your AI agents from writing sloppy TypeScript. A toolkit that teaches coding agents like Claude Code, Codex, Cursor, Amp, and more to ship production-ready code in half the time, at half the cost. Docs are available at https://convention.sh/docs
Tell your assistant you're about to launch. It checks the app your builder synced to GitHub, tells you what would break for real users, hands you the fix, and re-checks after the sync. Point it at any public repo first (nothing to install, nothing to grant) and see exactly what comes back before you connect the one that matters.
Check npm/PyPI packages exist and that a function or method exists in an exact package version.
- mcpOAuthco.policyforge
Generate, audit, and maintain legal policies that match what your code actually does.
Alcheon is an MCP server that gives AI coding agents access to a curated library of 100+ analyzed real-world design systems, letting them recommend reference sites, synthesize design briefs with spacing and color tokens, and generate section-level UI guidance — so AI-built interfaces draw from actual design DNA instead of generic defaults.
Audits MCP tool definitions for patterns that make models call tools wrong or mis-fill args.
