An MCP server that enables AI assistants to analyze schematics, inspect PCBs, trace connections, validate designs, and generate embedded code for KiCad projects.
An MCP server that provides AI trading agents with persistent, outcome-weighted memory to learn from historical performance and detect behavioral biases. It enables agents to automatically adjust strategies and optimize position sizing based on context-aware recall of past trade outcomes.
An MCP server that exposes tools for sub-agent style reasoning across multiple LLM providers, enabling delegation of prompts to various models and running critique loops, debates, red-teaming, and answer ranking.
Enables LLMs to access a user's personal writing context—voice, style, opinions, expertise, projects, and communication patterns—via curated markdown files, helping the LLM match the user's voice when generating written content.
Universal work attestation for autonomous
agents. Register any AI agent or machine
with persistent cryptographic identity,
attest completed work with tamper-evident
on-chain records, and query trust scores.
The reputation layer for the agent economy.
3 MCP tools over SSE. Settled on Solana.
Enables assistants to read-only explore and analyze Grafana dashboards and data sources, execute queries against Prometheus and Azure Log Analytics, and run guided investigation workflows.
Context+ is a semantic intelligence server that transforms codebases into searchable, hierarchical feature graphs using RAG, Tree-sitter AST, and spectral clustering. It provides tools for deep code discovery, blast radius analysis, and memory graph management for high-accuracy engineering tasks.
Provides a local SQLite-backed code context knowledge base with MCP tools for storing and querying code facts, call graphs, semantic info, evidence, and business mappings, plus versioned snapshot publishing and incremental sync.
Provides tools for file system operations and text generation using OpenAI-compatible models via the Model Context Protocol, supporting both stdio and HTTP transports.
An MCP server for managing contextual data as markdown files with metadata, enabling agents to save, retrieve, search, and delete contexts using simple CRUD operations.
Unified MCP orchestration layer that consolidates multiple MCPs into a single interface with semantic tool discovery, code-mode execution, scheduling, and intelligent caching to reduce token usage by 97% and eliminate choice paralysis.
A local MCP server for managing engineering context across Components, Repos, Tasks, and Governance entities. It enables capturing reusable context and composing it per-task with typed relationships and cross-cutting guidelines.
Model Context Protocol server for the NetLoc8 IP geolocation API. Gives AI assistants access to country/city lookup, timezone reconciliation, and IP utilities.
MCP server for technical SEO audits, powered by the detail.web engine. Run a site audit straight from your AI agent to get a health score, issues across 8 categories, and a GEO sub-score.