Skip to main content
Glama

get_account_health

Get a health summary of your LLM infrastructure with key metrics like call volume, cost, error rate, latency, and budget usage, returning a status indicator.

Instructions

Get a health summary of your LLM infrastructure in one call. Fetches 4 existing endpoints in parallel (aggregate_calls / get_percentiles / get_llm_budget / list_audit_log) and compresses them into one response. Returns { window, totals: {calls, costUsd, errorRate (percent 0-100)}, latency: {p50, p95, p99 (ms)}, budget: {used, limit, percentUsed (0-100)}, recentEvents: count, summary: 'ok' | 'warn' | 'critical' }. critical = errorRate>=10% / budget>=90% / p95>=10s; warn = >=3% / >=70% / >=3s. Example phrasing: "how is our LLM infra doing right now?" — answered in one prompt. Pure read aggregator (no new backend endpoint); individual endpoint failures return partial results (one axis timing out does not block the summary).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
windowNoObservation window ('1h' / '24h' / '7d', default '24h')24h
Behavior5/5

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

With no annotations, the description fully discloses behavior: it is a pure read aggregator that fetches endpoints in parallel, returns partial results if one endpoint fails, and defines clear thresholds for 'critical' and 'warn' states. This exceeds expectations for transparency.

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 detailed but well-structured, starting with purpose, then return type, thresholds, example, and behavior. It could be slightly more concise, but every sentence adds value.

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?

Without an output schema, the description fully explains the return structure (including exact field names and types) and behavior on partial failures. This is complete for the tool's complexity.

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 coverage is 100% for the single parameter 'window', and the schema already includes a description. The tool description does not add additional meaning beyond the schema, so a baseline score of 3 is appropriate.

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 it gets a health summary of LLM infrastructure by fetching 4 existing endpoints in parallel, distinguishing it from sibling tools like aggregate_calls, get_percentiles, etc. The verb 'get' and resource 'account health' are specific and unambiguous.

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 an example query ('how is our LLM infra doing right now?') and explains the tool returns a compressed summary, implying it should be used for quick health checks. However, it lacks explicit when-not-to-use guidance or direct comparisons to alternatives.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/argosvix/mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server