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Glama

check_inventory_health

Validate infrastructure inventory files: confirm structure, environment variables, and referenced SSH keys or kubeconfigs are valid and readable, re-reading from disk to catch drift.

Instructions

Validate the inventory file: it parses and passes structural validation, every referenced environment variable (${VAR}) is set, and every SSH key / kubeconfig file it references exists and is readable. Re-reads the inventory file from disk on every call, so it reflects the file's current state — including drift since the server started — not just what's currently loaded in memory. Read-only, takes no arguments.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
grafanaYes
proxmoxYes
routersYes
serversYes
problemsNo
switchesYes
timestampYes
kubernetesYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.8.1

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations present, the description carries the full burden and does so: it declares read-only behavior, that it takes no arguments, and that it re-reads from disk on every call so results reflect drift since server start rather than cached state. That freshness guarantee is exactly the kind of behavioral trait structured fields cannot convey.

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 sentences, front-loaded with the validation scope, followed by the freshness guarantee and read-only statement. Every clause adds distinct information with no repetition of the name or schema.

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?

For a zero-argument, read-only validation tool with an output schema covering return values, the description supplies everything an agent needs: what is validated, that it is safe to call freely, and why the result may differ from loaded state. Nothing material is missing.

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

Parameters4/5

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

There are zero parameters, so the baseline is 4. The description confirms 'takes no arguments', which is consistent with the empty schema and removes any ambiguity about hidden inputs.

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?

States a specific verb (validate) and resource (the inventory file), then enumerates exactly what is checked: parse/structural validation, ${VAR} environment variables being set, and SSH key/kubeconfig file existence and readability. This is clearly distinguishable from siblings like cisco_inventory or list_servers, which list rather than validate.

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?

Gives clear context for use — validating the inventory file's on-disk state — and the drift note tells the agent when this result is more trustworthy than in-memory state. It stops short of naming an alternative tool or an explicit 'use this before doing X' rule, so it is clear context without exclusions.

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