ChannelTerm is an open-source MCP server that enables humans and AI agents to share live serial and SSH terminal sessions for embedded systems development and debugging. It allows AI agents to read device output, execute commands, assist with GDB debugging, and transfer files while keeping developers in control.

Enables AI agents and assistants to inspect live system telemetry, check TCP port connectivity, audit environment variables for leaked secrets, analyze and summarize log files, and list Docker containers — all with token-efficient, compressed output.

Enables AI clients to build, solve and inspect LS-DYNA analyses, either by driving Ansys Mechanical through PyMechanical or by submitting and monitoring standalone keyword-deck runs. It also reads and audits solver output files such as glstat, binout, d3plot and ASCII histories, with results comparison and deck checks.

Enables MCP-capable assistants such as Codex and Claude to delegate bounded text work — summaries, structured extraction, classification, rewrites, code drafts and log digestion — to locally hosted Ollama models, with conservative routing that keeps small or sensitive tasks with the caller and private-result artifacts for sensitive output.

A task-aware transparent proxy that wraps stdio MCP servers (such as git, filesystem, and terminal) and compresses large tool outputs field-by-field using declarative YAML profiles, passing narrow intent queries through untouched. Discarded data is stored in SQLite and can be losslessly retrieved via an injected tool, either in full, by line range, or by grep, while errors are always preserved and failures fall back to the raw result.

Enables MCP clients to inspect local Git repositories through five focused, read-only tools for repository trees, literal text search, numbered file reads, bounded git diffs, and project summaries while confining paths, skipping secret-like and binary files, and enforcing output limits.

Exposes MCP tools that compute longevity and metabolic metrics (autophagy index, GKI, MAGE), cardio-renal biomarkers (BUN/creatinine ratio, cardiac output and TPR, nocturnal BP dipping), chronobiology estimates (DLMO, melanopic lux, caffeine clearance), plus neuro-recovery and biomechanics scores. Runs as a zero-dependency Python JSON-RPC 2.0 stdio server so any MCP-compatible client can query these physiological domain calculators.

Enables a model to write short Python programs that call a wrapped MCP server's tools as async functions, rather than invoking them one at a time. The program runs in a locked-down Docker sandbox, and every tool call passes through a host-side bridge that enforces an allowlist, schema-validated arguments, call budgets, timeouts and output caps.

Enables AI agents to read WeChat Official Account articles by fetching mp.weixin.qq.com links from the user's own machine and network and converting them into clean Markdown with title, account, author, and publish-time metadata. Provides both single-article and batch-reading tools (up to 20 URLs) with optional image handling, character limits, and JSON output.

Enables AI assistants to search a university library's catalog by keyword, title, author, subject, or ISBN, and to check each copy's real-time availability, floor location, and expected return date. Also provides recommended reading lists, new arrivals, and search-term suggestions, with raw holdings data parsed into readable output.

Independent pass/fail/retry judge and false-consensus check for agent outputs, with signed receipts.

Turn assistant output into ready-to-send action links and files, with no sign-in.

Deterministic runtime safety for AI agents: scan PII, gate tool actions, verify LLM output.