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freedom-mcp

by com.getfreedomos

generate_carousel

Render a multi-slide image carousel + a LinkedIn-PDF from structured slide copy. Text (including the cited answer) is rendered as REAL, legible text — never the garbled in-frame text AI image/video models produce. Use for value-demonstration B2B content (the cited-answer overlay, peer-proof decks). Produces artifacts only; publish via send_to_user(intent:"publish").

Routing: Carousel / slide deck / LinkedIn PDF / legible cited-answer overlay → use this (the text stays sharp; $0).

[write-tier — first use may require a manager's approval; a from-now-on approval makes future calls seamless, a just-once approval re-asks next time.]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
themeNoVisual theme. Defaults to brand (dark canvas + accent).
folderNoOptional Media gallery folder to file this carousel into (freeform name, e.g. "q3-campaign"). Shown as a folder chip on the /media page. Reuse an existing folder name when the work belongs to it.
formatNoSlide dimensions. linkedin_portrait (1080×1350, 4:5, default — best LinkedIn engagement), square (1080×1080), wide (1280×720).
slidesYesOrdered slides. Each: { kicker?, title (required), body?, citation? }. 3–8 ideal, max 12.
captionYesThe post caption that accompanies the carousel. Combined with the slide copy into the gate-text the ICP+Pledge gate scores.
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
accent_hexNoOptional brand accent color as 6-digit hex (e.g. "#F97316"). Pass the tenant's brand color. Defaults per theme.
brand_labelNoOptional per-tenant wordmark shown in the slide footer (e.g. your company name). Pass YOUR company's label only. Omit to render no wordmark — never a hardcoded brand.

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries full burden. It discloses that text is rendered as real legible text (differentiator), that it only produces artifacts requiring separate publishing via send_to_user, and mentions an approval gate (write-tier). It does not cover side effects or destructive behavior, but the description adds useful context beyond the schema.

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 fairly long but front-loaded with the core purpose. It uses bullet points for routing and approval info, which aids readability. Slight redundancy in the routing section could be trimmed, but overall it is well-organized.

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

Completeness3/5

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

Given the complexity (8 parameters, no output schema), the description explains input semantics well but lacks details about output format (e.g., artifact type, how to access the generated carousel/PDF). The absence of output schema makes this gap more significant.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description adds significant meaning: for slides it gives ideal range (3-8, max 12), for citation it specifies 'GENUINE external source ONLY' and warns against prefixing with 'Source:', for accent_hex it says to pass tenant's brand color, and for brand_label it says pass your company's label only. These details go well beyond the schema descriptions.

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 renders a multi-slide image carousel and LinkedIn-PDF from structured slide copy, emphasizing legible text which distinguishes it from AI image/video tools that produce garbled text. It also specifies use cases (value-demonstration B2B content) and routing, making the purpose unambiguous.

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 states when to use this tool (carousel/slide deck/PDF/legible cited-answer overlay) and includes a routing hint. However, it does not explicitly list alternative tools or conditions when not to use it, though the context implies differentiation from other image generation tools.

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.

Resources