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netops-field-notes

by labaccessnow

extract_device_facts

Extract hostname, device role, IPv4 addresses, subnets, and VLANs from router or switch configs in EdgeOS, MikroTik, OPNsense, FastPath, or Cisco IOS formats.

Instructions

Pull the facts out of a device config: hostname, inferred role, every IPv4 address with its subnet, the subnets it sits on, and its VLANs. EdgeOS config.boot, MikroTik export, OPNsense config.xml, EdgeSwitch/FastPath, and Cisco IOS-style text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
configYes
deviceNo
vendorNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations provided, the description carries the full behavioral burden. It usefully discloses the accepted input formats and the extraction outputs, but it does not mention failure behavior, handling of unsupported formats, or whether the operation is purely local/read-only. The supported formats add some transparency, but richer caveats about parsing limitations would improve it.

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

Conciseness5/5

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

Two tight sentences with no filler. The main action and outputs are front-loaded, and the config format list is compactly appended. Every sentence adds meaningful information.

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

Completeness4/5

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

Given that an output schema exists and the input schema is simple, the description covers the key invocation context: what to pass and what to expect. It misses only guidance on the optional 'device' and 'vendor' parameters and edge cases, but these are minor gaps relative to the provided schema and output schema.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for all three parameters. It clearly implies that 'config' is the device configuration text and hints at vendor formats, but it never explains the 'device' parameter and does not explicitly connect 'vendor' to parsing behavior. Two of three parameters remain effectively undocumented.

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 uses a specific verb ('Pull') and resource ('device config'), then enumerates the exact facts extracted: hostname, inferred role, IPv4 addresses with subnets, subnets, and VLANs. It also names the supported config formats, which clearly distinguishes this parsing/extraction tool from siblings like explain_config_diff or check_config_compliance.

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

Usage Guidelines4/5

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

The description provides clear context by listing the supported device config formats (EdgeOS, MikroTik, OPNsense, EdgeSwitch/FastPath, Cisco IOS-style), making it apparent when this tool applies. It does not explicitly state when to avoid it or name alternatives, but the sibling tool names are distinct enough that an agent can infer the appropriate use case.

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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