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originchain-ai

@originchain/telemetry-mcp

Official

log_volume

Retrieve total log volume and distinct pattern count from rollup aggregates to monitor telemetry health.

Instructions

Total log volume and distinct pattern count, instant from the rollup.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden and does disclose a useful behavioral trait: the result is instant and derived from a rollup, implying low latency and potentially pre-aggregated data. But it omits operational details like time-range semantics, freshness of the rollup, and exact output structure. It adds meaningful context but leaves gaps.

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?

The description is a single efficient sentence that front-loads the core outputs and then adds the performance characteristic. Every word contributes value, and no embellishment or repetition exists. This is ideal for a parameter-less tool.

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

Completeness3/5

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

The tool has no output schema, so the description must convey enough for an agent to interpret results. It states what metrics are returned and that they come from a rollup, but does not clarify the time range, units (log lines versus bytes), or whether the rollup is current or windowed. It is adequate for a simple metric but not fully self-sufficient.

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 tool has zero parameters and an empty schema, so the description need not explain parameter behavior. The baseline for zero-parameter tools is 4, and the description's mention of the outputs is helpful without being redundant.

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 clearly identifies the resource: total log volume and distinct pattern count, and the delivery mode (instant from rollup). It lacks an explicit verb, but 'Total' implies 'returns the total,' and this distinguishes it from sibling tools that return patterns, events, errors, or traces. It falls short of a 5 because it does not explicitly contrast with any sibling.

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

Usage Guidelines3/5

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

The phrase 'instant from the rollup' implies this is the fast, precomputed option for overall volume metrics, giving some contextual guidance. However, it never states when to use this tool versus alternatives like top_patterns, recent_events, or sql_query, nor does it mention any exclusions. The usage guidance is implied rather than explicit.

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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