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Service map

service_map
Read-onlyIdempotent

Infer which services call which from sampled Graylog traces, showing traffic, error rates, p50/p95 latency, and entry points without configuration.

Instructions

Which service calls which, inferred from sampled traces (no configuration): edges with traffic, error rate and p50/p95 latency, plus entry points.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoOptional Lucene filter for scope
rangeNoRelative range ending now (or at to_time): '15m', '2h', '1d', '1h30m'1h
sampleNoNumber of traces to sample
streamsNoStream titles or ids to search in; all streams when omitted
to_timeNoAbsolute end, same formats as from_time; default now
instanceNoGraylog instance (environment) from list_instances, e.g. 'staging' or 'prod'; the default instance when omitted
from_timeNoAbsolute start: ISO 8601 or 'YYYY-MM-DD HH:MM' in the instance timezone; overrides range

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and non-destructive behavior, so the safety profile is covered. The description adds genuinely useful context beyond that: results are statistically inferred from sampling rather than from configuration, which tells the agent output is approximate.

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 dense sentence that front-loads the core meaning ('which service calls which') and then enumerates outputs. No wasted words, nothing under- or over-specified.

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?

No output schema exists, but the description compensates by naming the returned content (edges with traffic, error rate, p50/p95 latency, entry points). Combined with full schema coverage and rich annotations, an agent has enough to call it correctly; only minor gaps like result size limits remain.

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 every parameter (query, range, sample, streams, time bounds, instance) is already documented in the schema. The description alludes to sampling but adds no syntax or format detail beyond what the schema provides, so the baseline of 3 applies.

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?

States a specific resource (service dependency map) and how it's derived ('inferred from sampled traces'), plus the output shape (edges with traffic, error rate, p50/p95 latency, entry points). It clearly reads as a topology/dependency tool, distinct from trace_request or root_cause, though it never names siblings explicitly.

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 parenthetical '(no configuration)' implies the tool is the right choice when no instrumentation/config exists, which is useful implied context. However, there is no explicit when-to-use vs when-not guidance and no reference to alternatives such as trace_request or root_cause.

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