"semantic-release" matching MCP servers:

A local stdio MCP server that exposes compact semantic observations from UI elements annotated with data-agent-* hints, designed to work alongside the official Playwright MCP for browser automation.

Semantix-Verify is an MCP server for semantic validation of AI/LLM outputs. It exposes a single tool, verify_text_intent(text, intent_description, threshold), which uses a local quantized NLI cross-encoder (INT8 ONNX) to return a 0.0–1.0 probability that the text satisfies the given intent — and, when it doesn't, a structured correction prompt for agent retry loops. Useful for building com

AI-to-AI economic marketplace with on-chain USDC escrow on Base L2. Agents browse skills, hire each other, manage jobs, release payments, and handle disputes via AI Judge. 15 MCP tools, reputation scoring.

Enables AI agents to fundraise for projects on the Base chain using milestone-based escrow management. It allows agents to create proposals, track funding progress, and generate payment release requests upon completing work.

Provides persistent semantic memory with semantic search, knowledge graphs, and Intelligence Packs for AI agents, enabling memory recall by meaning and session continuity.

A MCP server that bridges LLMs with GitHub repository management, enabling automated analysis of pull requests, issue management, tag creation, and release management through natural language.

Enables agentic workflows to execute allow-listed side effects with automatic reverse-order compensation on failure, durable SQLite journaling, and idempotent rollback via MCP tools.

Enables multi-party conditional escrow locking, autonomous proof-of-delivery release triggers, referral fee splits, and dispute freeze protection for agentic commerce and enterprise settlement workflows.

Autonomous financial policy engine and budget guard for AI agent fleets. It verifies payment payloads, velocity caps, escrow release terms, and slippage inflation, returning risk verdicts and signed attestations.

Enables AI agents and marketers to search creators, draft campaign briefs, send collaboration offers, verify deliverables, and release escrowed payments against a shared data layer. Enforces budget ceilings, rate caps, human-approval checkpoints for high-value actions, and an append-only audit trail.

Enables coordination of concurrent Claude Code agent sessions by providing file claiming (claim/release/check) and automatic PreToolUse enforcement to block edits on files owned by other sessions.

Provides structured semantic context for JavaScript/TypeScript codebases, enabling coding agents to navigate, review, and change code with explicit uncertainty.

Enables estimating LLM hallucination and confabulation by clustering sampled responses into semantic equivalence classes and computing semantic entropy. It provides a plug-and-play validation filter for agent pipelines using the MCP protocol.

Enables LLM agents to make fast semantic decisions by choosing one of 2–16 options with probabilities, via tools for single and batch classification.

Enables MCP clients to observe Windows accessibility trees and deltas, generate fail-closed semantic action plans for external authorized executors, and control isolated, agent-owned Chrome tabs without accessing personal tabs.

Enables AI agents to synthesize and update hierarchical episodic and semantic user profile memory graphs through MCP, using deterministic zero-dependency Python processing with JSON Schema validation.

Provides a coherent memory layer for LLM agents that models beliefs as nodes in a constraint network to detect and surface semantic contradictions. It uses local embeddings and a hybrid geometric-logical energy model to identify conflicting information that requires review.

Enables AI agents to access and act on web, desktop, and mobile interfaces via semantic accessibility trees, using MCP tools for querying, navigating, and executing deterministic UI actions without relying on screenshots or pixel coordinates.

An AI engineering team for embedded firmware, lab automation, and hardware drivers. It uses 53 specialized experts and a datasheet-to-release pipeline to produce auditable, hardware-aware code.

Agentic AI instruction encoding. 86.8% smaller than JSON. Inference-free decode by table lookup. 342 opcodes, 26 namespaces, three conformant SDKs. Confirmed over LoRa mesh radio. Apache 2.0 with express patent grant.