"Information about a Facebook page" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
An MCP server that compiles ASN.1 schema definitions into executable, native source code for a variety of languages (like Python, Rust).
Lint a SKILL.md for frontmatter, structure, secrets and size. All 6 tools free.
Risk-scan a diff, flag AI-generated-code tells, find secrets. 5 of 7 tools need no account.
Find what to fix on a public website, for search engines and for AI answers.
Per-harness pre-open verdict on a received repo's agent-control surface.
Production-safety audits for AI-generated code, with a fix for every finding.
**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
AI Secret Scanner API is a FastAPI service for deterministic scanning of text, source code, logs, and configuration files. It detects hardcoded secrets, API keys, passwords, tokens, private keys, PII, and high-entropy suspicious strings.
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).
DBRE-grade SQL analysis inside any MCP client. No connection. No install. Paste a query.
Dependency picker for coding agents: recommends, compares and scores GitHub repos with a verdict.
Generate and validate a .specs/ bundle for your repo, then hand it to your AI coding agent
15 deterministic AI agent and developer tools for code context pruning, token budgeting, hybrid search reranking, self-healing patches, AST analysis, circular dependency detection, root-cause isolation, schema compilation, math/financial solving, critical-path scheduling, A/B testing, memory diffs, API compatibility checks, boundary test generation, and Monte Carlo risk simulation.
Read-only scan of a public GitHub repo for leaked secrets, with a 0-100 grade.
Ask Void anything: answers with sources, plus code review, moon phases and meteor showers.
Runs your code against a contract; HELD or BROKE at the exact input. Deterministic. 0.10 USDC/call.
Search, inspect and inject full source modules (code + AI rules + MCP) pinned to a Git commit.
Rams is a design reviewer for UI code. The MCP server puts the hosted engine inside a coding agent: the agent passes files to the review_files tool and gets back a 0–100 score with file:line issues and concrete fixes — accessibility, color, typography, spacing, components, UX, motion, craft, and native SwiftUI. Same engine and scoring as the Rams GitHub App. 258 rules, published at rams.ai/rules. Free tier: 30 reviews/month.
Explain a regex in plain English and detect catastrophic backtracking risk.
Verify a vuln report's file/line/function claims against a real GitHub repo.
