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jasamandehvary

kahlo-mcp

get_live_diff

Reports drift between an ArgoCD app's Git and live Kubernetes specs, ignoring metadata noise. Use it to detect out-of-band kubectl edits or verify pod specs match Git.

Instructions

Compare an ArgoCD application's desired (git) state against what is actually running in the cluster, resource by resource. Reports drift as added/removed/changed spec fields, ignoring routine status/metadata noise (resourceVersion, managedFields, status subresource, etc). This is the tool for "did someone kubectl edit/patch something out-of-band" or "is the live pod spec what git says it should be".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
appYes

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

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full transparency burden. It discloses that the tool reports drift as added/removed/changed spec fields and intentionally ignores resourceVersion, managedFields, and status subresource noise. This gives meaningful behavioral insight beyond the schema and implies a read-only comparison operation.

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?

Three sentences, all information-dense and front-loaded. The first sentence states the primary function, the second clarifies the exact nature of the output, and the third anchors the use case. No filler or redundant restatement of the tool name.

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 tool is simple (one required parameter), has an output schema, and the description covers what it does, what it reports, what it ignores, and when to use it. Nothing necessary for correct invocation or selection 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?

The schema has only an 'app' string with 0% description coverage. The tool description compensates by repeatedly framing the operation as comparing an ArgoCD application, making it clear that 'app' refers to the target ArgoCD application name. It does not belabor syntax, but with a single obvious parameter this is sufficient.

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 states a specific verb ('Compare') and resource ('an ArgoCD application's desired (git) state against what is actually running'), and precisely defines the output as drift in spec fields while ignoring metadata noise. This clearly distinguishes it from sibling tools like get_argocd_app_status or describe_resource, so an agent knows exactly what this tool is for.

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 gives explicit use-case guidance: 'This is the tool for' detecting out-of-band kubectl edits or checking if a live pod spec matches git. It does not name specific alternative tools or when-not-to-use conditions, but the intended context is clear enough for an agent to select it appropriately.

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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