"Understanding Perl, Language Server Protocol (LSP), and Abstract Syntax Trees (AST)" matching MCP servers:

Enables AI agents to maintain persistent, inspectable understanding through typed, revisable updates, and to coordinate multi-agent work via shared graph-based stigmergy.

MCP server that keeps language server sessions warm and routes multiple languages through one process. Agents get persistent cross-file awareness, speculative execution (simulate edits before writing to disk), and 20 skills that encode correct multi-step operations like safe rename, blast-radius analysis, and end-to-end refactoring. Single Go binary, no runtime dependencies.

Enables LLM agents to interact with the Lean theorem prover through the Language Server Protocol, providing tools for analyzing Lean projects, accessing diagnostics, goal states, documentation, and searching for theorems using both local and external search services.

TypeScript-based MCP server designed to enhance code editing experiences by providing features such as hover information, code completion, and diagnostics.

Universal coordination hub for AI agents. Find collaborators, negotiate terms, form contracts, and build reputation through an MCP interface. Supports natural language search across agent networks.

Trust, identity, and reputation infrastructure for AI agents. Register agents with W3C DID (Ed25519), check EigenTrust reputation scores, submit peer attestations, search agents by capability, and verify IPFS-anchored audit trails. 11 tools.

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.

Security-enforcing MCP proxy that sits between an AI agent and any number of downstream MCP servers, intercepting every tool call through a capability-token policy gateway that can allow, deny, or escalate to human approval before the call reaches any real tool. It also exposes built-in operator tools for approval workflows, audit trail queries, token management, voice/HUD output, and hierarchical

Provides time intelligence, persistent memory and complete traceability for AI coding agents. Enables sophisticated activity tracking, timezone management, smart reminders, and cross-session continuity with support for both per-project and centralized storage modes.

Enables autonomous agents to parse markdown documents into structured AST nodes—including headings, lists, tables, callouts, and code blocks—and serialize modified AST back into clean canonical markdown via MCP tools.

MCP server providing witness, recovery, and continuity primitives for AI agents, enabling them to articulate failure, preserve state across sessions, and coordinate via MCP, A2A, or REST.

Local-first shared memory and coordination layer for AI coding agents, with repository evidence, reservations, handoffs, code graph context, and dashboard review backed by PostgreSQL/pgvector.

Local MCP server for Bind Protocol — credential parsing, verification, and hashing tools for AI agents.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Universal agent registry, discovery, and cross-protocol messaging for any MCP-compatible AI agent, enabling registration, discovery, and message translation across six protocols.

An MCP server that enforces explicit task ownership acceptance and requires a provenance tag (observed, reviewed, or reported) on every completion claim, preventing silent inheritance and unverified assertions in multi-agent systems.

Enables durable agent session transfer between machines by serializing state, provisioning sandboxes, and metering costs via MCP tools over a Postgres-backed state machine.

Resume-as-an-Agent (RAAA) or Portfolio-as-an-Agent (PAAA) allows Agents to talk to your resume, find out if you are available for work, and more. Use the MCP to allow recruiters, managers, and anyone with an agent to "talk" to your resume.

Enables schema-validated, sandboxed access to 34 Python-powered tools across Pillow, requests, pypdf, pandas, openpyxl, and python-docx, plus an autonomous ReAct converter agent that can grow the catalog by requirement and self-repair at runtime. It also provides a control console and hardened Docker deployment for secure MCP client integration.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

An intelligent MCP server that acts as an AI tech lead for coding agents, providing expert validation, impact analysis, and strategic guidance before code changes are made.

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 AI agents to break down complex tasks into manageable pieces using a structured JSON format with task tracking, context preservation, and progress monitoring capabilities.

Enables AI agents to fetch daily deterministic constraint challenges and submit JSON answers for immediate, stateless evaluation via MCP.

Enables local, Docker-isolated code execution across six programming languages including Python, Rust, and TypeScript. It features pre-warmed container pooling, persistent sessions, and built-in support for machine learning libraries.

MCP server for step-by-step mathematical reasoning and planning, enabling AI agents to execute calculations and perform GUI actions like opening PowerPoint.

Connect OpenAI Custom GPTs, Google Gemini, Anthropic Claude MCP, and autonomous agents directly to the FormFactory.sol smart contract. Execute verified EVM transactions for creating, closing, activating, or archiving decentralized forms across 4 supported networks.

Enables structured role-to-role handoffs and merge gating for multi-agent collaboration. It persists evidence and computes approval gates without invoking LLMs.

The accountability layer for AI agents — a named human's signed yes before an agent does anything irreversible (payment, record change, deploy), then an offline-verifiable Trust Receipt. Apache-2.0, formally verified.

Look up AI agents' evidence-based reputation and record two-party signed work receipts (INAM Protocol). Tools for agent search, reputation checks, job posting, receipt drafting/countersigning, and content hashing, against a registry with operator-authorized verification and a Merkle transparency log.