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jagadeesh52423

Graylog MCP Server

get_log_histogram

Retrieve a time-based histogram of log messages, showing message counts over intervals. Use filters, queries, and time ranges to analyze log volume trends.

Instructions

Get a time-based histogram of log messages. Shows message counts over time intervals.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoEnd time for absolute range (ISO string or timestamp)
fromNoStart time for absolute range (ISO string or timestamp)
queryNoQuery to filter messages
filtersNoField filters (e.g. {"env": "production", "level": 3})
metricsNoMetrics to calculate per time bucket. Default: ['count']. Use with valueField for numeric aggregations.
intervalNoTime interval for buckets (e.g., '1m', '5m', '1h', 'auto'). Default: 'auto'
streamIdsNoOptional stream IDs to scope the search. Use 'list_streams' to get available stream IDs.
timeRangeNoTime range (e.g., '1h', '2d', '30m') or use from/to for absolute range
exactMatchNoIf true (default), wraps the query in quotes for exact match.
valueFieldNoNumeric field for sum/avg/min/max calculations (e.g., 'latencies_request'). Required when metrics include non-count metrics.
Behavior2/5

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

With no annotations, the description must carry the full behavioral burden. It only states that it shows message counts over time, without disclosing details like whether it returns only counts or can calculate sum/avg/min/max (which the schema hints at), how intervals are determined, or any limitations. The behavioral transparency is minimal.

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 extremely concise, using only two short sentences that focus on the core purpose. It is front-loaded and has no filler. However, it is perhaps too terse for a tool with 10 parameters, though the schema handles parameter details, so the brevity is acceptable.

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

Completeness2/5

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

The tool has no output schema and no annotations, and the description is minimal. It lacks context about the returned histogram structure, query semantics, or when to use it among many similar sibling tools. For a complex tool with 10 optional params, the description is incomplete.

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 the baseline is 3. The description adds no parameter-specific meaning beyond the schema's own property descriptions. It does not clarify relationships between parameters (e.g., when valueField is required) beyond what is already in the schema.

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 states the tool gets a time-based histogram of log messages and shows counts over intervals. The verb 'get' and resource 'time-based histogram' are specific. It does not explicitly differentiate from similar sibling tools like get_field_time_aggregation, but the name and description convey the core purpose.

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?

No guidance is provided on when to use this tool versus alternatives such as get_field_aggregation or debug_histogram_query. There are no explicit when-to-use instructions or exclusions, leaving the agent to infer usage from the name alone.

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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