Fixter

spans

Find individual spans matching filter criteria within a time range.

This is a DRILL-DOWN tool. For "where are errors / latency concentrated?" start with aggregate_spans, then use this to fetch example spans for a (service, operation).

Returns spans sorted by (timestamp, spanId) descending (newest first).

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

Defaults: from/to window open if none given; limit 100 (max 1000); all filters any. Common patterns:

Errored spans of an operation: statusCode="ERROR", name="http.client"
Slow spans: minDurationMs=500
Every span of a trace: traceId="..." (or use get_trace)

Returns: spans[], nextCursor (null on last page), queryStats.

Input Schema

TableJSON Schema

Name	Required	Description
`to`	No	End of window, ISO-8601 instant (exclusive)
`from`	No	Start of window, ISO-8601 instant (inclusive)
`kind`	No	Filter by kind: SERVER, CLIENT, PRODUCER, CONSUMER, INTERNAL
`name`	No	Filter by operation/span name (e.g. 'GET /things')
`limit`	No	Max spans, default 100, max 1000
`cursor`	No	Opaque cursor from a previous next_cursor
`service`	No	Filter by service name
`traceId`	No	Filter to a single trace id
`verbose`	No	Return full spans incl. attributes and resource. Default false.
`statusCode`	No	Filter by status: UNSET, OK, ERROR
`minDurationMs`	No	Only spans at least this many milliseconds long
`maxStringChars`	No	Max characters of any attribute string value before truncation. Omit to use the server default.

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 the full burden and does so thoroughly. It discloses sorting order, default response fields, verbose behavior, string truncation, default time windows, limit defaults, and filter semantics. It also outlines the return structure (spans, nextCursor, queryStats), exceeding typical expectations.

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 well-organized with clear sections and front-loaded purpose. Every sentence contributes useful information, including common patterns and return details. Despite its length, it avoids redundancy and remains structured and scannable.

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 tool's complexity (12 optional parameters, no output schema), the description is remarkably complete. It explains return values, default behaviors, filter semantics, and provides practical examples. It also covers when to use sibling tools, making it self-contained for effective selection and invocation.

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 description coverage is 100%, so baseline is 3. The description adds contextual value beyond the schema by illustrating parameter usage with examples (e.g., statusCode='ERROR', name='http.client', minDurationMs=500, traceId) and explaining default behaviors for limit, from/to, and verbose. It doesn't deeply elaborate every parameter, but the extra semantic context justifies a 4.

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 clearly states the tool's function with a specific verb and resource: 'Find individual spans matching filter criteria within a time range.' It also distinguishes itself from siblings by explicitly positioning as a drill-down tool and referencing aggregate_spans and run_sql.

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?

Provides explicit guidance on when to use this tool versus alternatives: 'For "where are errors / latency concentrated?" start with aggregate_spans' and 'For raw columns or custom selection use run_sql.' It also offers common usage patterns and mentions get_trace as an alternative for trace-level retrieval.

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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