"Model Context Protocol (MCP) Servers Repository" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Verify before the agent pays. Screwtape is an independent check on agent work before a payment goes out: invoices, duplicate invoices and reconciliation, with a signed receipt for your auditor. It is independent of your agent's own model and vendor, with no stake in your agent passing. - judge: pass, fail or retry verdict on one agent output, with a score, reasons and a next_step. Optional deterministic checks: required fields, types, formats, sums, regex. - check_duplicate_invoice (/v1/duplicate): opt-in duplicate-invoice check using keyed fingerprints. - consensus: scores agreement across 2 to 16 agent answers and flags false consensus. - Advisory mode: recommends approve, review or return; a person decides. - Receipts: Ed25519-signed. The receipt proves what was checked, when, and the verdict. Verify via POST /v1/receipts/verify or offline; export via GET /v1/receipts/export. - Judged content isn't stored unless you opt in. Free key: 100 calls/day. Or $0.005 USDC per call via x402 on Base or Solana, no key needed. MCP: https://screwtape.ai/mcp Docs: https://screwtape.ai/docs/ Icon: https://screwtape.ai/icon-512.png Tags: verification, evaluation, llm-as-judge, invoices, receipts, x402
HefestoAI checks AI-generated code before you ship it.
Load testing and synthetic monitoring platform: test with Playwright, Browser Bot, or Protocol Bots.
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.
Official Mifori Agent Trust Protocol. Performs real-time latency, JSON schema compliance, and synthetic IQ audits on target AI bots using x402 USDC micro-payments on Base network before high-value tool execution.
Simulate cloud infrastructure and estimate cost before deploying or changing infrastructure.
Receipts for what your AI coding agent says it verified. Short-lived API checks, keys kept out of the model
Free, read-only context retention review for RAG and agent workflows. Supply sanitized text with stable source IDs and a token budget; receive a source-linked context pack, token estimates, and exact excerpt-retention results. No web fetching, account access, model calls or payments. Checks text selection, not factual truth or semantic completeness. No API key required.
Run, debug, and triage tests from your IDE using natural language, no dashboard switching, no manual data transfers. The TestMu AI (formerly LambdaTest) MCP Server is a single remote server exposing four tool suites: HyperExecute — analyze your project, generate YAML configs and test runner commands, then monitor jobs and sessions. Automation — pull a TestID's details plus command, network, and console logs into one chat for instant root-cause analysis. Includes mobile app upload. SmartUI — explain pixel, layout, DOM, and perceptual changes in a visual regression run, with context-aware React/HTML/CSS fixes. Accessibility — audit any public URL or a local React app against WCAG and get ready-to-apply remediation steps. Connects over https://mcp.lambdatest.com/mcp using OAuth 2.1 — no API keys in your config. One-click install in Cursor; works with Claude, GitHub Copilot, Cline, and any MCP client. Tests execute on the TestMu AI cloud: 3,000+ browsers and 10,000+ real devices.
Check what your AI says against your policy and records before it reaches a customer. Deterministic, no model calls, no signup.
LLM & RAG evals: hallucination, eval contracts, semantic tests, tokens & model info. Free, no key.
Benchmark your own prompt on 100+ LLMs with real API calls; pick the cheapest model that passes.
Independent AI model test scores on 5 tasks: inbox, scheduling, receipts, finance, support replies.
Agentic code review, no signup to try: reality gates + frontier-model review, with veto.
The world's first named AI prompt quality score. Score, optimize, and compare LLM prompts before they hit any model. Free tier available. Built on PEEM, RAGAS, G-Eval, and MT-Bench frameworks. x402-native on Base.
Observed facts on public MCP servers: protocol checks, tool changes, signed evidence. No verdicts.
Synthetic checks, nightly regression replay and model-drift alerts for AI agents
PQS scores any prompt before the model runs. 8 dimensions. 5 frameworks. Pre-flight, not post-hoc.
AI integrity standards, benchmarks, and EU AI Act-aligned Tier 0 model certification
Unpaid qualification-gated operational_context canary. Staging data. No wallet.
Give AI assistants the context behind client website feedback. Read comments, screenshots, replies, element details, and developer briefs; organize priorities, update statuses, and export feedback from authorized projects. Coding agents with repository access can investigate issues and prepare fixes for review. Connect through Streamable HTTP and browser OAuth using a Simple Commenter account.
MCP Spec Compliance MCP — audits any MCP server.json against the official Model Context Protocol
## 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
Run AI agent evaluations on your own model keys, and publish runs others can review and re-run.
Check public MCP reachability, protocol handshake, auth and tool schemas before connecting.
Translation QA: automated checks, AI evaluation, linguistic review, and visual in-context testing.
MCP server for building and testing AI agents with multi-model experimentation and insights.
