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posthog_list_vision_scanners

List Replay Vision scanners in the connected PostHog project (AI probes that watch session recordings) for the operator or analytics agent. Use when checking which scanners exist before creating or updating one. Only works if PostHog is connected.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax scanners to return (default 20, max 50)
searchNoSearch scanners by name
enabledNoFilter by enabled state
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
scanner_typeNomonitor | classifier | scorer | summarizer

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It indicates this is a read-only listing operation and discloses the PostHog connection prerequisite. However, it does not describe the return format, pagination behavior, or potential errors, leaving some behavioral aspects unstated.

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?

Two sentences with zero redundancy. The first sentence states purpose and defines the resource; the second provides usage direction and a prerequisite. Information is front-loaded and every word 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 straightforward list operation with five documented parameters, the description covers purpose, usage, and a prerequisite. It does not mention return structure or ordering, but that is implied and likely consistent with other list tools. A minor gap, but overall sufficient for correct invocation.

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?

All five parameters are fully described in the input schema (100% coverage). The description does not add extra meaning to the parameters beyond what the schema already provides, so it meets the baseline of 3 for high 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?

The description clearly states a specific verb ('List') and resource ('Replay Vision scanners'), and defines what those are ('AI probes that watch session recordings'). This distinguishes it from sibling tools like posthog_get_vision_scanner (single) and create/update/delete, which are obviously different operations.

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 explicitly says when to use it: 'Use when checking which scanners exist before creating or updating one.' It also notes a prerequisite: 'Only works if PostHog is connected.' It does not explicitly name alternative tools, but the context implies the create/update tools are the alternatives, so guidance is clear enough.

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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