"Files formatted in JSON" matching MCP servers:

tooltrim reduces the tokens agents spend re-reading bloated tool results. Run it as an MCP server exposing compress and expand_tool_output, or as a gateway in front of any upstream MCP server: it re-exposes the upstream tools unchanged and shrinks each result (HTML/JSON/logs/tables) before it reaches the model, keeping the relevant content only.

Provides blocking await tools to poll commands, URLs, or files until a condition is met, eliminating the need for sleep loops in agent workflows.

Enables Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

Allows two AI coding agents on different machines to securely pair and share files, context, and conventions through an end-to-end encrypted peer-to-peer channel with human-in-the-loop consent.

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.

Enables ChatGPT and Codex to securely manage files in Nextcloud via OAuth-protected MCP tools, with tenant isolation and root-bound access.

Enables AI agents to control legacy web applications running in Microsoft Edge's IE Mode via MCP, using Selenium WebDriver and IEDriverServer for browser automation.

Enables a primary agent to offload bounded, high-volume analysis of explicitly selected workspace files to an auxiliary LLM through a single read-only tool, returning text, Markdown, JSON, or unified-diff output with token usage while retaining all planning, verification, and edits.

Enables Codex to manage a local Claude Code companion through MCP, implementing a dual-agent workflow where Codex handles reasoning and review while Claude Code performs engineering tasks.

A Model Context Protocol server that connects to Mattermost, allowing AI models to monitor and process messages from specific teams and channels in real-time via SSE or Standard I/O transport modes.

Cuts AI token costs by running user code in a secure sandbox, so data never enters the context window, enabling efficient data processing with MCP protocol.

A Machine Comprehension Protocol server that enables AI assistants to interact with Kestra workflows through natural language, supporting operations like flow management, executions, backfills, and other Kestra features.

Exposes ReadyAgents' local workflow engine and built-in tools over MCP stdio, enabling MCP clients to define, run, inspect, and approve YAML/JSON agent workflows.

Enables AI agents to persist and share durable memory — appending session logs with checkpoints and handoffs, claiming and completing tasks, and recording decision records — all stored as plain Markdown and JSONL files in a git repository. Everything written is full-text searchable, and the server speaks MCP so MCP-aware clients can plug in directly with zero vendor lock-in.

Enables AI agents to optimize context by pruning large JSON payloads, enforcing token budgets, and handling x402 micropayments via Base blockchain.

Enables registering custom agent tools via a minimal JSON-RPC over stdio implementation, with zero external dependencies, to expose them to AI agents like Claude, Cursor, and Gemini.

Enables deterministic multi-agent debate cross-examination and consensus arbitration through the Model Context Protocol, with structured JSON output and zero external dependencies.

Enables coding agents in different editors and MCP clients to share messages, project context, notes, task ownership, handoffs, and advisory file claims through a local SQLite-backed coordination server. Supports MCP stdio, MCP Streamable HTTP, a JSON HTTP API, and a local CLI for multi-project agent collaboration.

Enables searching and retrieving cancer-genomics projects, cases, and genomic data files — including TCGA, TARGET, and CPTAC — via keyless, open-access metadata tools. Supports filtering by primary site, project, or data category, and can route plain-English questions to the right tool automatically.

Enables AI agents to interact with web browsers using natural language, featuring automated browsing, form filling, vision-based element detection, and structured JSON responses for systematic browser control.