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list_operator_cos_events

List THIS operator's recent CoS telemetry (operator_cos_events: open/speech/close, host_push actions, card_decide/confused/buggy). Use to verify dogfood soak, or before propose_cos_content_atoms. Never invent events. Do not speak UUIDs aloud — counts + kinds only unless they ask for detail.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNoOptional filter to one kind (e.g. host_push, card_buggy, cos_open).
hoursNoLookback window in hours (1–168, default 24).
limitNoMax rows (1–100, default 40).
companyIdNoFreedomOS company id to act within (you must be a member). Required for company-scoped tools.

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries the behavioral burden. It discloses important constraints: 'Never invent events' and 'Do not speak UUIDs aloud — counts + kinds only unless they ask for detail.' This goes beyond a bare list operation, though it doesn't cover auth or rate limits.

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 extremely concise: four short sentences with zero filler. It front-loads the core purpose, then adds usage and behavioral guidance. Every sentence earns its place.

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?

For a list tool with no annotations and no output schema, the description sufficiently covers purpose, use cases, and important output-format constraints. It omits details like pagination or return shape, but the description's behavioral notes ('counts + kinds only') provide useful context. A bit more about expected output structure would make it fully complete.

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%, so all 4 parameters (kind, hours, limit, companyId) are already documented in the schema. The description adds no specific parameter semantics beyond the schema, which is adequate given full schema coverage.

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?

Description uses a specific verb ('List') with a specific resource ('THIS operator's recent CoS telemetry') and enumerates exact event types (open/speech/close, host_push, card_decide/confused/buggy). It clearly distinguishes this from sibling tools like list_cos_lessons and get_cos_preferences by focusing on telemetry events.

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?

Explicitly states when to use the tool: 'Use to verify dogfood soak, or before propose_cos_content_atoms.' It does not explicitly mention when not to use it or alternative tools, but the primary use cases are clear.

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

A3.6/5.0
Disambiguation4/5

The tool set is heavily disambiguated by detailed routing descriptions, domain prefixes, and lifecycle verbs, so most tools have a clear intended purpose. However, at 297 tools there are still close pairs and overlapping decision surfaces (e.g., approval workflows, 'what should I work on' readers, multiple finance/ads readers) that require careful description reading to avoid misselection.

Naming Consistency4/5

Naming is predominantly consistent snake_case verb_noun with strong domain prefixes like shopify_, x_, posthog_, and list_/create_/update_ patterns. Minor inconsistencies exist, such as several collection-returning tools using get_ (get_team_members, get_icps, get_okrs) instead of list_, and some generate_ vs create_ vs draft_ verbs, but the pattern is still predictable overall.

Tool Count1/5

297 tools is an extreme outlier and far beyond a usable MCP tool surface. Even a large suite has no justification for this count in one server; the agent would struggle to select among hundreds of similarly descriptive tools, and the natural 3-15 tool range is exceeded by nearly 20x.

Completeness4/5

The individual domains represented — OKRs, CRM/leads, Shopify, content pipelines, ads, PostHog, team hiring, knowledge, finance, and session management — are covered remarkably well with full lifecycle patterns. Minor gaps exist, such as no full deal CRUD, no delete for several Google/Shopify artifacts, and some analytical surfaces being read-heavy, but most workflows can be completed without dead ends.

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