"Sort Content by Number of Downloads" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Coding agents in multi-service codebases routinely rebuild existing helpers, trust stale type definitions, and modify API contracts without knowing who consumes them. Carrick solves this by indexing your entire TypeScript ecosystem across service and repository boundaries. By integrating deeply with the TypeScript compiler, Carrick traces every route, type, and cross-service call while recording function behaviour so agents search by intent rather than name. Delivered via MCP for AI agents and LSP for IDEs, Carrick ensures models see existing endpoints and utilities before generating new code. The scanner is source-available and runs from your CLI or CI pipeline.
Risk-scan a diff, flag AI-generated-code tells, find secrets. 5 of 7 tools need no account.
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.
**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
Free deterministic security scan of public git repos: OSV.dev vulnerable deps, secrets, config lint.
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).
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
Multi-model code review: a panel of models + detectors return a pass/fail verdict. Paid via x402.
TOML array-of-tables count, body discarded
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
Web scraping, code review, content gen, sentiment. Zero Core Tools.
## Skill Catalog The library contains 42 public skills organized by Rails development concern. | Category | Examples | |----------|----------| | Planning | `create-prd`, `generate-tasks`, `plan-tickets` | | Testing | `plan-tests`, `write-tests`, `test-service`, `triage-bug` | | Code quality | `code-review`, `respond-to-review`, `security-check`, `refactor-code` | | Architecture and DDD | `define-domain-language`, `review-domain-boundaries`, `model-domain`, `review-architecture` | | Rails imple
Search your team's Storybook components by text or image, so agents reuse what exists.
Permission-aware onboarding MCP server: answers about a codebase, filtered by the caller's role.
Give your AI agent a persistent map of your project's structure, dependencies, and bugs.
Hunt zero-days by talking to binaries. 40+ tools. Hosted, OAuth + SSO, invite: hi@byteray.ai
- MegaLensOAuthai.megalens
Code review by AI models from different companies, usually two. Shows where they agree and disagree.
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.
