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Fixter

by dev.fixter.mcp
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Correlate Telemetry

correlate
Read-onlyIdempotent

One-shot cross-signal pivot for a trace id.

Given a trace id, returns (all fields top-level, no nested summary object): rootOperation, spanCount, errorCount, totalDurationNanos, startTime — trace summary spans — the trace's spans, earliest first (up to 1000; spansTruncated says more exist) logs — the newest 1000 logs tagged with that traceId (no window limit; logsTruncated says more exist) exemplars — metric exemplars whose traceId matches, within the span window (up to 1000) windowFrom / windowTo — the derived scan window (earliest span - 5s / latest span end + 5s)

The window is derived from the trace's spans. If the trace is unknown, spans and exemplars are empty but logs are still returned if they carry the traceId. Exemplar filtering is window-bounded; log filtering is not.

Use this as the primary entry point when you have a trace id and want to see all correlated signals at once. Returns core fields by default; verbose=true flattens attributes in for both spans and logs (plus a resource object) and long string values are capped. Use run_sql for raw columns or custom selection. After reviewing the result, drill into individual signals with logs, spans, or metrics as needed.

Long-lived traces (scheduler ticks, batch jobs) can produce very large verbose responses even with the caps. Prefer verbose=false first; for error triage, the logs tool with traceId + level is a cheaper, targeted alternative. Pass maxStringChars to tighten string truncation per call.

Returns: traceId, traceUrl, rootOperation, spanCount, errorCount, totalDurationNanos, startTime, windowFrom, windowTo, spans[], logs[], exemplars[], queryStats. traceUrl opens this trace as a waterfall in the Fixter app, every span on its timeline with its attributes. When the user asks to see, open or visualize the trace, send them traceUrl rather than drawing the waterfall yourself; attach it whenever you cite the trace as evidence.

Input Schema

NameRequiredDescriptionDefault
traceIdYesLowercase hex trace id
verboseNoInclude the row's attributes (flattened) + a resource object. Default false.
maxStringCharsNoMax characters of any string value (message or attribute) before truncation, at most 4000. Omit to use the server default.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / maxStringChars / description
      Previous value: -"Max characters of any string value (message or attribute) before truncation. Omit to use the server default."New value: +"Max characters of any string value (message or attribute) before truncation, at most 4000. Omit to use the server default."
  2. Changed3 schema fields changed
    • addedInput schema / $schema
      Added value: +"https://json-schema.org/draft/2020-12/schema"
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / maxStringChars / format
      Added value: +"int32"
  3. Added
  4. Removed
  5. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already cover readOnly/idempotent/non-destructive, so the description does not need to restate safety. It adds real undisclosed behavior instead: how the scan window is derived (earliest span -5s / latest end +5s), that exemplar filtering is window-bounded while log filtering is not, and the 1000-row truncation flags. It stops short of describing cost/latency traits for the expensive verbose path, which keeps it off a 5.

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?

Front-loaded with the one-line purpose, then a field block, then behavioral notes — the structure lets an agent skim. It loses a point for the closing 'Returns:' list, which restates the field enumeration already given at the top and bloats the definition noticeably.

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?

No output schema exists, so the description carries the full return burden and does it: every returned field is named, truncation semantics are given per array, the empty-result edge case (unknown trace still returns tagged logs) is spelled out, and traceUrl usage guidance tells the agent what to do with evidence.

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%, so baseline is 3; the description earns above that by explaining what verbose actually changes (flattens attributes for spans and logs, adds a resource object, caps strings) and by advising verbose=false first with maxStringChars to tighten truncation per call. Marginal rather than decisive: the 4000-character ceiling lives in the schema, not here.

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?

States a specific verb and resource ('one-shot cross-signal pivot for a trace id') and immediately enumerates exactly what comes back — trace summary, spans, logs, exemplars, window. An agent can distinguish this from get_trace, spans, logs and run_sql without opening any schema.

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?

Explicitly names itself as 'the primary entry point when you have a trace id', then gives named alternatives with the conditions that select them: run_sql for raw columns, logs with traceId + level for cheaper error triage, and the drill-down tools after reviewing. That is when/when-not/alternatives in one place.

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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