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list_investigations

List the authenticated customer's recent Fixter investigations (alert-investigation and product-support flows only), newest first. Returns a JSON array of summaries: id, publicSlug, headline, flow, createdAt, and claimedBy (display name of the engineer who claimed it, null when unclaimed). The id feeds get_investigation_brief and start_investigation; the publicSlug feeds start_investigation only. Optional ISO-8601 instant filters 'from'/'to' bound createdAt.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoOnly investigations created at or before this ISO-8601 instant.
fromNoOnly investigations created at or after this ISO-8601 instant.
limitNoMax investigations to return (default 20, max 100).

TDQS

A4.3/5.0
Behavior5/5

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

With no annotations provided, the description fully discloses the output shape (JSON array with specific fields), ordering, flow restriction, and how the IDs relate to sibling tools. It even clarifies that claimedBy is null when unclaimed. This is exemplary for a list tool and leaves no behavioral surprises.

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 a single dense paragraph that packs in all key details without fluff. It could be slightly improved with better sentence segmentation, but every sentence earns its place given the absence of an output schema.

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?

The description is comprehensive for a list tool with no output schema: it defines the return structure, fields, scope, filters, and related tools. No major gaps are evident; edge cases like empty results or error handling are not addressed but are not critical for a well-specified list operation.

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 coverage is 100% for all three parameters (to, from, limit). The description adds the clarification that 'from'/'to' bound createdAt and are optional ISO-8601 instants, supplementing the schema. The default and max for limit are already in the schema, so the description adds minimal but non-zero value; a baseline of 3 is appropriate.

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 uses the specific verb 'List', names the resource 'Fixter investigations', and clearly states the scope ('authenticated customer's recent'), the flow restriction ('alert-investigation and product-support flows only'), and ordering ('newest first'). This fully distinguishes it from sibling list_* tools and leaves no ambiguity about what the tool does.

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

Usage Guidelines4/5

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

The description provides actionable guidance by noting that the returned id feeds get_investigation_brief and start_investigation, and that publicSlug feeds start_investigation only. It also scopes usage to two specific flows. While it does not explicitly name alternatives or when-not-to-use cases, the context is clear enough for an agent to decide when to invoke this tool.

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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