freedom-mcp

posthog_query_trends

Query event trends from PostHog (pageviews, signups, DAU, etc. over time). Returns time-series data. Only works if PostHog is connected.

Input Schema

TableJSON Schema

Name	Required	Description
`events`	No	Events to query. Each: { id: "$pageview", name: "Page Views", math: "total" }. Defaults to $pageview.
`date_to`	No	End date (default: now)
`interval`	No	Grouping interval (default: day)
`companyId`	Yes	FreedomOS company id to act within (you must be a member). Required for company-scoped tools.
`date_from`	No	Start date: "-7d", "-30d", "-90d", "2024-01-01" (default: -7d)

TDQS

B3.2/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It does not state whether the tool is read-only (it likely is), or if it has any side effects, rate limits, or cancellation behavior. For a query tool querying an external service, these details are important but missing.

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, front-loaded with purpose and examples. No filler. Every word adds value. Highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema is provided, so the description must explain what the tool returns. It only says 'Returns time-series data,' which is vague. The agent cannot infer the structure or cardinality of the response (e.g., array of {date, value} objects). This is a gap for a data-querying tool.

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 description coverage is 100%, so baseline is 3. The description adds modest value by giving example event names and explaining the default event type. However, it does not significantly enhance understanding of date_from or interval formats beyond the schema, so a score 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?

Description clearly specifies the action (Query), resource (event trends from PostHog), and provides concrete examples (pageviews, signups, DAU). It distinguishes from sibling tools by emphasizing time-series data and trends, which is unique among PostHog-related tool names.

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

Usage Guidelines2/5

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

The only usage hint is 'Only works if PostHog is connected,' which is a prerequisite. There is no guidance on when to use this tool versus alternatives like posthog_hogql (general querying), posthog_query_funnel (funnels), or posthog_list_events. The agent lacks context to choose correctly.

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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