"Information on agent swarm architecture" matching MCP servers:

MCP server that diagnoses ML model regressions by correlating drift reports, eval runs, and deploy logs, providing evidence-cited incident reports through a set of investigation tools.

Screens AI agent tool calls before execution and tool results before the agent reads them, acting as an MCP proxy and hooks adapter with shadow, enforce, and strict policy modes.

Provides real-time system metrics and information through a Model Context Protocol interface, enabling access to CPU usage, memory statistics, disk information, network status, and running processes.

MCP server exposing AI agent observability tools: list, inspect, and deterministically replay recorded agent execution traces for debugging failures.

Exposes rate-limit and usage data for AI coding agent CLIs as MCP tools, enabling querying of usage snapshots and reset times.

An MCP server that provides append-only, tamper-evident local receipts for AI agent actions, capturing command executions, outputs, and handoff evidence.

Local-first observability for AI agent stacks. Provides MCP tools for agents to check health, shadow configs, and burnout metrics proactively.

Tripwire detection for autonomous AI agents. Plants honeypot files, MCP tripwire tools, and API decoy endpoints to log agent scope creep and unauthorized tool use with full forensic context.

Authorization, human approval, audit trail and budgets for Spring AI agents and MCP servers. Spring Boot starter, on Maven Central.

A read-only MCP server that exposes local coding-agent session logs as three tools for introspection of recent work, debugging tool failures, and tracking token usage and estimated cost without parsing log files.

Provides a local MCP interface to query captured, PII-scrubbed prompt telemetry from coding agents and browser chats, enabling time summaries, prompt search, and activity analysis without data leaving the machine.

Provides cost analysis of AI agent usage by previewing scrubbed metadata before upload and analyzing spend, cache-hit rates, and waste recovery through a local or remote server.

Enables LLM agents to discover and invoke guarded web search, read-only SQLite queries, and sandboxed file read/write tools over the Model Context Protocol. It adds schema validation, rate limiting, output sanitization, request-ID logging, and Prometheus metrics at the protocol boundary.

MCP server for AI agent observability, providing trace and span logging, search, latency/tokens/cost metrics, and anomaly detection using an in-memory buffer.

Enables AI agents to manage prompts and sessions via the ai-agent-logs API, with tools for health checks, login, listing/creating prompts, and retrieving sessions.

Enables analyzing coding-agent session transcripts to measure tool efficiency, detect loops, track test outcomes, and report costs, with MCP tools for listing sessions, analyzing sessions, finding loops, and generating cost reports.

Tracks AI agent token usage and spending in real time, with budget alerts, per-task cost breakdown, and a visual dashboard.

Cryptographic accountability for AI agents. Ed25519-signed receipts for every MCP tool call. Constraints, chains, AI judgment, invoicing, and local dashboard included.

agent-bom is an open security scanner and self-hosted control plane for AI, MCP, and cloud infrastructure. The default scan profile in MCP server mode exposes 8 MCP tools. Additional profiles provide inventory, findings, compliance, graph, and runtime workflows. See the published Glama release for its packaged version.

Enables agents to report and query performance metrics to build a community-driven quality database for MCP tools. This server helps agents discover and select the most reliable tools based on success rates and user-reported quality scores.

Enables recording and querying MCP tool call traces, including latency, failures, and token costs, per agent run.

Enables local-first observability for MCP agent workflows by recording tool calls, prompts, latency, and token usage in a local SQLite database, and provides tools for inspecting, comparing, exporting, and generating dashboards from traces.

Enables AI agents and their hosts to record actions, tool calls, and human approvals in an append-only, hash-chained log; verify tamper evidence; mask secrets; retrieve recent events; and export audit-ready JSON or CSV on self-hosted infrastructure.

MCP server that provides structured audit logging for AI agent repair tasks via tools to start, record, end, query, and export event traces, with JSONL persistence and SDK integration.

Analyzes AI agent traces to diagnose failures, score performance, and provide actionable fixes using tools like judge_trace and trace_breakdown.

Hash-chained HMAC-signed audit log MCP for A2A (agent-to-agent) calls. Every tool-call, agent-handoff, decision gets a tamper-evident signed record.

MCP server for monitoring, diagnosing, and auto-correcting observability issues across platform services, integrating with Sentry and PostgreSQL for error tracking and billing pipeline health.

Tracks and analyzes AI agent tool calls with event logging, dashboard, per-tool and per-agent analytics, and error monitoring.

Enables AI agents to work with business-document data through MCP tool calls constrained by a registry, data tiers, human approval for writes, and a tamper-evident audit log. Ships with saved evals and a dashboard to prove and inspect those controls.

A security checkpoint for AI agents that intercepts and polices tool calls with allow/block/ask verdicts, taint tracking, and audit logging.