Skip to main content
Glama
Sign In
azure-query-mcp

azure-query-mcp

by kapetanios55
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

check_ingestion_health

Read-onlyIdempotent

Detect log ingestion problems in a workspace by checking per-table last-seen times, 24h volume changes, stale tables, and ingestion latency.

Instructions

Check whether log data is still arriving: per-table last-seen time, 24h volume versus the previous daily average, stale tables, volume drops or spikes, and ingestion latency over the last hour.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "workspaceId": "<workspaceId>"
    },
    "name": "check_ingestion_health"
  }
}

Input Schema

NameRequiredDescriptionDefault
timespanNoISO 8601 duration back from now, such as P7D. Health is measured against now, so intervals are not accepted. Default P7D.P7D
workspaceIdYesWorkspace ID (GUID) or the workspace's full ARM resource ID; list_workspaces returns both.
staleAfterHoursNoFlag a table as Stale when no data arrived for this many hours.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.5.0

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive, so the safety profile is covered; the description adds substantive behavioral context by listing the specific signals computed and the comparison baselines (24h volume vs previous daily average, 1h latency window). It does not mention relative cost or whether the scan is expensive over long timespans, which is the main remaining gap.

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?

A single front-loaded sentence with the core purpose first and the enumerated signals after the colon. No redundant restatement of the name and no filler.

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?

There is no output schema, so the description carries the burden of describing returns, and it does so well by enumerating the health signals. What it does not clarify is whether results are aggregate-only or per-table rows, and how staleAfterHours interacts with timespan; those are minor for a read-only diagnostic.

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?

Schema description coverage is 100%, so all three parameters (timespan, workspaceId, staleAfterHours) are fully documented in the schema itself. The description adds no parameter-level detail beyond the schema, so the baseline of 3 applies; the 'over the last hour' latency window is the only hint at fixed-vs-configurable windows.

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

Purpose4/5

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

The description states a specific verb and resource ('check whether log data is still arriving') and enumerates exactly what is measured: per-table last-seen time, 24h volume vs prior daily average, stale tables, drops/spikes, and 1h ingestion latency. It is unambiguous against siblings like query_workspace or list_incidents, which do not perform diagnostic health assessment, though it never names those alternatives.

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

Usage Guidelines2/5

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

There is no explicit guidance on when to call this versus query_workspace or describe_table, and no stated preconditions (e.g., 'call before starting an investigation'). Usage is only implied by the diagnostic framing; an agent must infer the scenario itself.

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

Deploy Server

Other Tools