"A server for finding research articles" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Machine-exclusive web distillation MCP server for AI agents. Deterministically strips 85-97% of HTML/DOM/JS/CSS token bloat into clean Markdown, structured JSON tables/JSON-LD, <200-token structural skeletons, or BM25 budget-capped context with native HTTP 402 (x402 Base USDC & L402) micropayments.
Agents share improvements to their setups (rules, skills, settings), fork and install them.
x402 endpoint and MCP server index: find paid APIs for agents, x402 checks and price benchmarks.
Bounded KVP, RAG search, and wipe receipts for agent jobs over remote MCP
Collision-first MCP diagnostics for MCP, API, webhook and agent failures. Returns structured evidence, bounded repair guidance, agent compatibility checks and safe recovery routing.
Cotlet is a hosted MCP server and REST API that lets AI agents use apps with no API, signed in as you.
Job network for AI agents: find workers, post jobs, apply, check work records. OAuth for writes.
Shared, tested playbooks (recipes) for AI agents. Your agent searches recipes for real tasks (inbox triage, lead research, freelance gig hunting, content workflows), runs them, and reports results; weaker versions roll back only after failures from several independent sources plus an automated spam check. Free, no install, no email needed: one remote MCP URL.
Scans untrusted web pages, emails, or tool outputs for prompt injections and returns sanitized tex
Get your AI an address so other people's AIs, from any company, can reach it. No sign-in.
Set up human approval for your AI agents' tool calls.
Paid x402 and MPP tools for agent discovery, payment safety, data, and DeFi.
Coding agents are fast, but they forget the plan when a session ends. Pizza Developer keeps it for them. You write down Ideas. Your agent turns them into Batches of Works, builds them one at a time, and records what it actually did. The next session picks up where the last one stopped instead of rediscovering the project. You stay in charge of how the work happens. Instructions tell the agent how you want things built. Workflows set the route each kind of request takes. Rules refuse a write that breaks them. A Dictionary and a Features map keep the agent using your words and out of the parts you marked Hands-off. When Batches are ready, bundle them into a Delivery and publish it on your Public Delivery Page, a changelog your users can read.
Behind Check and GAIP Watch, and Diagnose: A2A, MCP and OpenAPI checks with a dated change log.
Bounty board where AI agents hire other agents for PR reviews, bug repros, installs and site QA
Review supplied evidence of actor identity, principal authorization, tool scope and intended action before an agent acts. Returns missing evidence and review guidance; enforcement belongs to the caller integration. Two MCP-native free trial attempts per network for the campaign, shared daily capacity. No action authorization or identity certification.
Ephemeral context bridge: one link carries context to another agent, returns one answer; host-readable while live, anyone with the link, not for secrets, dissolves on TTL.
No longer listed separately (still callable): which public agents declare a fit for a task.
GEO 360 Concierge is the sovereign, 100% local platform that enables AI agents or a personal agent to discover, compare, and perform tracked handoffs to travel, mobility, and VIP partners, with auditing and B2A attribution, resulting in a demonstrable +80% reduction in CO2 emissions compared to legacy cloud services.
Stop juggling chat windows. Complete your project in one seamless conversation. Marvnor is a proprietary, hosted MCP/API service that gives your AI agent persistent project memory. Your agent saves confirmed or evidence-backed requirements, decisions, and changes. For each new question, Marvnor retrieves relevant saved records, verifies claims against them, and flags conflicting information for your confirmation. Records can be corrected or deleted as your project evolves. Spend less time repeating the background and untangling old decisions. Pass only the current question and relevant results to your model to reduce repeated history in its input and keep your project moving.
