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Fixter

by dev.fixter.mcp

get_trace

Return every span in a single trace (up to 10000), plus a trace summary.

Prefer correlate when investigating a trace — it returns the same spans plus correlated logs and metric exemplars in one call. Use get_trace only when you need the span tree alone and want to skip the log/exemplar lookup.

Spans come ordered by (timestamp, spanId) ascending; each carries parentSpanId so you can rebuild the tree. The summary gives root operation, span count, error count, total duration, and start time at a glance.

Returns spans' core fields by default; pass verbose=true to include their attributes (flattened in, plus a resource object). Long string values are capped. For raw columns or custom selection use run_sql.

Returns: traceId, traceUrl, rootOperation, spanCount, errorCount, totalDurationNanos, startTime, spans[], queryStats. traceUrl is a shareable Fixter UI link for this trace — attach it when citing the trace as evidence to the user.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
traceIdYesLowercase hex trace id
verboseNoReturn full spans incl. attributes and resource. Default false.

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, the description carries full burden and excels: it discloses span ordering, parentSpanId for tree rebuilding, summary fields, default vs verbose behavior, string capping, and the shareable traceUrl. It also explains how to use traceUrl as evidence, adding actionable context.

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 front-loaded with the core purpose, then provides usage guidance and behavioral details. Every sentence contributes distinct information; no filler or repetition. The length is justified by the tool's complexity.

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?

Given the absence of annotations and output schema, the description is remarkably complete: it specifies return fields, ordering, default behavior, verbose options, and when to use alternatives. It fully compensates for missing structured metadata.

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?

Schema coverage is 100%, but the description adds value beyond the schema: it details that verbose includes 'attributes (flattened in, plus a `resource` object)' and notes that long string values are capped. This enriches parameter understanding.

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 opens with a specific verb and resource: 'Return every span in a single trace (up to 10000), plus a trace summary.' It clearly states the tool's function and distinguishes it from the sibling 'correlate' by noting it returns only spans without logs/exemplars.

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

Usage Guidelines5/5

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

Explicit guidance is given: 'Prefer correlate when investigating a trace... Use get_trace only when you need the span tree alone and want to skip the log/exemplar lookup.' This names the preferred alternative and specifies when to choose this tool instead.

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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TDQS

A3.8/5.0
Disambiguation2/5

Several tool pairs are near-duplicates, including three deprecated aliases (add_investigation_alert_channel vs add_alert_channel, list_investigation_alert_channels vs list_alert_channels, remove_investigation_alert_channel vs remove_alert_channel) that muddy the surface. Additionally, suppress_signal and create_ignore_rule both suppress alerting via different mechanisms, which could cause misselection despite detailed descriptions.

Naming Consistency4/5

The vast majority of tools follow a clear verb_noun snake_case pattern (create_api_test, list_issues, set_alert_rule_status). A few bare-noun tools (logs, spans, metrics) and the standalone verb correlate break the pattern slightly, but overall the naming is highly consistent and predictable.

Tool Count1/5

With 52 tools, this is on the extreme end of the calibration scale. Even accounting for the broad scope of an observability platform, the count is excessive and includes several deprecated redundancies that inflate it further.

Completeness5/5

The toolset provides comprehensive CRUD/lifecycle coverage across all major domains: alert rules (create, read, update, delete, status, delivery, preview), API tests (create, read, update, delete, run history, credentials), ignore rules and suppressions, issues with digest config, investigations with claim/read, channels, credentials, and rich query tools (logs, spans, metrics, SQL, traces, correlation). No obvious dead ends or missing core operations.

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