"An application for finding emails in my mailbox" matching MCP servers:

Unified email access via IMAP/SMTP, enabling reading, searching, sending, and deleting messages across multiple accounts through a single MCP server.

An MCP server that enables concurrently-running Claude Code sessions to cross-talk via channels, DMs, and leader coordination, all backed by a shared beads database.

Enables local AI agents on the same machine to exchange messages asynchronously through mailboxes, with tools for registering, sending, replying, checking, broadcasting, and waiting for messages without polling or cloud services.

Cross-CLI agent messaging: let Claude Code / Codex / Pi / ZCode terminal agents message each other like email. File-based mailboxes with a resident delivery hub that picks the channel per recipient (tmux injection / native queue / daemon queue / pull), federated across hosts over ssh.

Enables concurrent terminal sessions or other MCP clients on the same machine to register, exchange messages, check inboxes/replies, and coordinate tasks through file-based storage without a daemon or network port.

A privacy-first MCP server for locally managing job, fellowship, and graduate-school applications. It offers tools for tracking application status, analyzing role fit, generating LaTeX CV/cover letters, interview prep, and discovering public jobs from ATS APIs.

Enables AI assistants to query, filter, and update job application records, and to analyze skill demand across postings.

Enables AI assistants to read, search, sort, draft, and send email from connected mailboxes through MCP tools, with access controlled by per-token permissions.

Enables Claude Web users to manage their job application workflow by storing profile and resume data, analyzing job descriptions, preparing applications, and tracking progress — all with manual submission and no platform automation.

Trust layer for 412 official Malaysian public datasets, with freshness, licences, and a public read-only MCP server. No API key.

Exposes tools that let an agent read a CV, search the Arbeitnow job board for Berlin AI/ML working student roles, score each vacancy against the CV, draft cover letters whose every claim cites CV facts, and queue them for human approval. The same tools can be driven from other MCP clients such as Claude Desktop, with scoring done deterministically in code and limits enforced outside the prompt.

A template MCP server that provides a basic structure and configuration for building Model Context Protocol servers. Includes Docker support, environment configuration, and examples for both STDIO and SSE transport modes.

Enables users to describe their LLM fine-tuning job once and get the cheapest, fastest, and most balanced GPU options across a dozen cloud providers in seconds.

Turns local Codex session transcripts into a searchable memory that MCP-compatible coding agents can query to retrieve relevant past sessions or exact commands and fixes.

Gives AI clients controlled access to one or more Microsoft Exchange mailboxes over EWS to read, search, file, flag, send, and manage inbox rules and calendars. Multiple mailboxes are served from one endpoint with per-account read-only, send, and calendar-write gates that default to closed.

An MCP server that connects AI assistants to YNAB budgets, enabling natural language queries about finances backed by full API coverage and built-in YNAB methodology knowledge.

Exposes the agent's tools over the Model Context Protocol, enabling LLMs to track applications, generate cover letters, and process emails.

Enables users to describe an LLM fine-tuning workload and receive ranked, cost- and runtime-optimized GPU recommendations across multiple cloud providers by estimating VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job once and receive the cheapest, fastest, or most balanced GPU hardware options across a dozen cloud providers by calculating required VRAM/FLOPs and aggregating live pricing.

Enables users to describe an LLM fine-tuning job and receive ranked, task-aware GPU recommendations with estimated VRAM/FLOPs, cost, and runtime across more than ten cloud providers.

Enables users to describe an LLM fine-tuning workload once and get task-aware VRAM/FLOPs estimates plus ranked cheapest, fastest, and balanced GPU recommendations across multiple cloud providers.

Enables users to describe an LLM fine-tuning job and receive the cheapest, fastest, or most balanced GPU options across multiple cloud providers by calculating required VRAM and FLOPs.

Enables users to describe an LLM fine-tuning workload once and get ranked, cost- and speed-optimized GPU recommendations across multiple cloud providers by estimating VRAM/FLOPs requirements.

Enables users to describe an LLM fine-tuning job once and get ranked GPU hardware recommendations with estimated VRAM/FLOPs, cost, and runtime across a dozen cloud providers.

Enables users to describe an LLM fine-tuning job once and receive VRAM/FLOPs-aware recommendations for the cheapest, fastest, or most balanced GPU options across a dozen cloud providers.

It enables users to describe an LLM fine-tuning workload once and receive task-aware VRAM/FLOPs estimates plus ranked cheapest, fastest, or balanced GPU recommendations across multiple cloud providers.

It enables users to describe an LLM fine-tuning workload once and automatically estimates required VRAM/FLOPs, compares live GPU pricing across cloud providers, and returns ranked cheapest, fastest, or balanced hardware recommendations.

Enables users to describe an LLM fine-tuning workload and receive ranked, task-aware GPU recommendations by estimating required VRAM/FLOPs and comparing live pricing across a dozen cloud providers.

Enables tracking job applications by connecting Gmail to retrieve emails, process job details, manage statuses, and generate statistics.

Enables users to describe an LLM fine-tuning job once and receive ranked GPU recommendations across many cloud providers based on estimated VRAM/FLOPs, cost, and runtime.