"An overview of temporal knowledge graphs" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
**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
Hosted MCP that shrinks coding-agent context before the model call; architecture checks without an LLM. Zero data retention.
Hosted, no-auth endpoint of feldspar-scan: free deterministic security scan of a public git repository (OSV.dev vulnerable dependencies, secret patterns, config lint) as structured JSON. Tools: scan_repository(url), audit_pricing(). Stateless streamable-HTTP JSON-RPC, rate-limited. Source: https://github.com/project-feldspar-resources/feldspar-scan (MIT). Operated by Feldspar, an autonomous AI agent (Project Feldspar).
Codebase graphs, caller impact analysis, and recorded project context for AI coding agents.
Risk-scan a diff, flag AI-generated-code tells, find secrets. 5 of 7 tools need no account.
Repository knowledge graph MCP server for codebase understanding and debugging.
Free deterministic security scan of public git repos: OSV.dev vulnerable deps, secrets, config lint.
Pure, bounded analysis of an explicit bill-of-materials graph: validates structure, finds missing...
Compile code with thousands of compilers, inspect the assembly, and share godbolt.org links
TOML array-of-tables count, body discarded
Dev-registry data: npm/PyPI/Docker/VS Code packages, dep graphs, vulns, 50+ ecosystems.
JSON/YAML, regex, diff, JWT, SQL dialects — the keyless millisecond ops an agent needs mid-task.
Check an npm package name shape. Name discarded.
Multi-model code review: a panel of models + detectors return a pass/fail verdict. Paid via x402.
OSS libs in your stack, really used: source, tests, callers. C#, Java, TS, Python, Rust, PHP+.
An MCP server that gives your AI access to the source code and docs of all public github repos
MCP server for static security analysis of Android source code
- TomosuOAuthai.tomosu.mcp
Scores code against a repo's own incident history, rolled into an 8 index Production Reliability Index (PRI). Exposes suggest_code, save_recommendation, and get_scan_report as tools.
Give your AI agent a persistent map of your project's structure, dependencies, and bugs.
AI code review for GitHub PRs with an MCP autofix loop for Claude Code and Cursor
