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shigechika

io.github.shigechika/junos-mcp

by shigechika

daily_brief

Run a morning health check across multiple Juniper devices in parallel. Detects alarms, interface down, syslog anomalies, and routing issues, providing a tiered summary of critical, warning, and ok status.

Instructions

Run a morning health check across multiple devices in parallel.

Checks per host (Phase 1):

  • show system alarms / show chassis alarms

  • show interfaces descriptions — a physical interface is flagged [IF_DOWN] only when it has a description (Admin=up, Link=down) and its Last flapped time is within since_hours. Undescribed unused ports and chronically-down ports are suppressed (loopback / mgmt / internal logical units are also excluded).

  • show log messages | last 200 — alert patterns within since_hours

  • dual-RE redundancy — an explicit routing-engine fault is flagged [RE_FAULT] (skipped on SRX chassis clusters, whose facts misreport RE status; a failed cluster node raises chassis alarms instead)

  • route_baseline (optional) — when > 0, a device whose inet.0 destination count differs from this value is flagged [ROUTE_BASELINE]. Scope with tags (e.g. tags=["main"], route_baseline=152), since full-table routers carry far more routes than access routers.

Syslog patterns watched: BGP state change away from Established, STP port role change, OSPF neighbor down, ARP address conflict, IF_DOWN.

since_hours defaults to 18 (≈ previous 15:00 for a 09:00 morning run). Tags default to none (all routers); pass tags=["main"] to limit scope.

Output tiers:

  • CRITICAL — connection failure

  • WARNING — at least one anomaly found

  • OK — clean

Returns a Markdown summary with anomaly details for CRITICAL/WARNING hosts and a collapsed OK list.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hostnamesNo
tagsNo
since_hoursNo
route_baselineNo
max_workersNo
config_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description exhaustively discloses behavioral traits: exact checks per host, syslog patterns, handling of dual-RE faults on SRX chassis clusters, route_baseline comparison logic, output tiering (CRITICAL/WARNING/OK), and Markdown summary format. With no annotations provided, the description fully compensates with rich behavioral details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with headings (Phase 1, Syslog patterns, Output tiers) and front-loaded with the core purpose. It is detailed but not unnecessarily verbose; every sentence contributes meaningful information. Slight length is justified by complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is complete for the tool's purpose, covering behavior, parameters, edge cases, and output format. The presence of an output schema further enriches the context, making the overall definition fully actionable for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds meaning for tags, since_hours, route_baseline, and max_workers by explaining defaults and usage (e.g., tags for scoping). However, hostnames and config_path are not explained, leaving some ambiguity. Given 0% schema coverage, more parameter detail would be beneficial.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Run a morning health check across multiple devices in parallel,' with a specific verb and resource. It distinguishes from sibling tools by detailing the exactly checks performed, making it unique among tools like health_check or run_show_command.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides implicit usage context through defaults (since_hours=18 for morning runs, tags=none for all routers) and scoping advice for route_baseline. However, it lacks explicit when-to-use or when-not-to-use guidance compared to sibling tools, leaving the agent to infer optimal scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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