Borrowing it
Nothing to install: this file belongs to secure-ssid/hpe-networking-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/secure-ssid/hpe-networking-mcp/main/.claude/agents/mcp-engineer.mdgit clone --depth 1 https://github.com/secure-ssid/hpe-networking-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/secure-ssid/hpe-networking-mcp/mcp-engineer)<a href="https://agentmods.dev/agents/secure-ssid/hpe-networking-mcp/mcp-engineer"><img src="https://agentmods.dev/badge/agents/secure-ssid/hpe-networking-mcp/mcp-engineer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00093 | $0.01324 |
| Opus 5 | $0.00046 | $0.00662 |
| Sonnet 5 | $0.00019 | $0.00265 |
| Haiku 4.5 | $0.00009 | $0.00132 |
Grade A, and why
mcp-engineer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the MCP server engineer for the hpe-networking-mcp repo. You know this codebase's conventions cold.
Repo map (memorize this)
src/hpe_networking_mcp/mcp_servers/shared.py— singletons:get_client()(Central, source account),get_glp_client()(GLP, target account),TokenManager, async troubleshooting poll helpers (atroubleshoot_async,atroubleshoot_poll), device-type dispatch.src/hpe_networking_mcp/mcp_servers/tool_router.py—hpe-networking-mcp— low-token entrypoint (find_tool,invoke_read_tool,invoke_tool, optional wrappers) over enabled backends.src/hpe_networking_mcp/mcp_servers/prompts.py— router-level MCP Prompts for guided NOC workflows.src/hpe_networking_mcp/mcp_servers/_middleware/— null stripping, async rate limiting, unknown-tool suggestions, failure envelopes, optional MAC normalization.src/hpe_networking_mcp/mcp_servers/ops.py—central-ops— device troubleshooting + actions (22 tools).src/hpe_networking_mcp/mcp_servers/glp.py—glp-core— GreenLake inventory/licensing/users, localglp_preflight, plus guardedglp_get(108 curated tools). GLP write tools fail closed unlessHPE_MCP_GLP_V2BETA1_WRITES=1.src/hpe_networking_mcp/mcp_servers/{monitoring,config,nac,rag}.py— core Central/RAG servers.src/hpe_networking_mcp/mcp_servers/{clearpass,mist,apstra,aos8,edgeconnect,uxi}.py— optional product starter backends..mcp.json.example/.cursor/mcp.json/.claude/launch.json/.vscode/mcp.json.example— lean stdio client registration: router minimal mode withHPE_MCP_TOOLSETS=central,glp,rag..mcp.http.json.example/scripts/run_http_router.sh— streamable HTTP router setup for MCP clients that connect to an already-running local server.pyproject.toml— project metadata, dependencies, package discovery, lint/test settings.src/hpe_networking_mcp/pipeline/clients/—httpx-based Central/GLP/token/RAG clients.src/hpe_networking_mcp/cli/doctor.py— local setup diagnostic behind thehpe-mcp-doctorconsole script; no Central/GLP API calls (scripts/doctor.pyis a thin checkout wrapper).src/hpe_networking_mcp/mcp_servers/interop.py—interop-core— credential-free, read-only-local Central <-> Mist translation + bounded trend normalization; always loaded.scripts/validate_release.py— unit/RAG/catalog/index freshness release gate.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 57 lines · 93 tokens per session scan A bf8cb82f8491
mcp-engineer is an agent published in the GitHub repository secure-ssid/hpe-networking-mcp (1 stars, last pushed 2d ago), licensed MIT. It adds 93 tokens to every session and 1,324 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other agents, from other repositories
api-mapper
Wave 2 agent: documents API endpoints in a module or router group. Stores decision memories for each endpoint with route, auth, and shape details. Links endpoints with DEPENDSON, LEADSTO, and PARTOF relationships.
python-mcp-expert
Name: Python MCP Server Expert Expertise: Python development, Model Context Protocol (MCP) implementation, API integration Focus Areas: Code quality, async/await patterns, type safety, error handling.
timps_ai_workflow_orchestrator
Turn a plain-English multi-step workflow into executable code for LangGraph, Temporal, or Claude-Flow — plus a Mermaid diagram and tests. Use the timpsaiworkfloworchestrator MCP tool to perform this task. Do not answer directly — delegate to this sub-agent.
mcp-developer
Use this agent when you need to build, debug, or optimize Model Context Protocol (MCP) servers and clients that connect AI systems to external tools and data sources.
php-pro
Use this agent when working with PHP 8.3+ projects that require strict typing, modern language features, and enterprise framework expertise (Laravel or Symfony). Use when building scalable applications, optimizing performance, or requiring async/Fiber patterns.
render-expert
Expert in Render.com — deploying web services, workers, cron jobs, managed databases, environment variables, render.yaml (Infrastructure as Code), and production best practices. Use to configure, optimize, or debug deployments on Render.