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freedom-mcp

by com.getfreedomos

generate_image_xai

Generate or EDIT an image using xAI Imagine (Quality Mode default — highest live API fidelity; closest to consumer Image 2.0 until API ships a 2.0 model id). Photorealistic, illustrations, flat graphics, icons, banners. 1K/2K. Single or multi-image edit (≤3 refs via artifact_ids / reference_image_urls). Use model_tier=standard only for cheap drafts.

Routing: ALL image generation and editing → use this (2 credits). Quality default; multi-ref composite via artifact_ids.

[sensitive-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
folderNoOptional Media gallery folder to file this image into (freeform name, e.g. "q3-campaign" or "brand-assets"). Shown as a folder chip on the /media page so the operator can find it later. Reuse an existing folder name when the work belongs to it.
promptYesDetailed description of the image. Include lighting, camera angle, environment, style, and subject details.
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
model_tierNoquality (default, best fidelity) or standard (cheaper draft). Prefer quality for customer-facing work.
resolutionNoOutput resolution. 1k (default) or 2k (print/pro).
artifact_idNoID of an existing artifact to edit. Prefer over raw URLs (company-scoped resolve).
folder_nameNoSubfolder name for Drive save (e.g. "Product Shots", "Headshots"). Only used when save_to_drive is true.
artifact_idsNoMultiple artifact IDs for multi-ref edit/composite (max 3).
aspect_ratioNoAspect ratio. Defaults to 1:1. Use "auto" to let the model choose.
save_to_driveNoIf true, also save the image to Google Drive for permanent storage. Defaults to false.
reference_image_urlNoURL of an existing image to EDIT. Prefer artifact_id when possible.
reference_image_urlsNoMultiple source image URLs for multi-ref edit/composite (max 3). Prefer artifact_ids.

TDQS

A4.7/5.0
Behavior5/5

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

No annotations exist, so the description carries full responsibility. It discloses default behavior (Quality Mode, highest live API fidelity), capability boundaries (max 3 refs, 1K/2K), and operational caveats (first use may require manager approval, approval type implications). It also explains the model_tier tradeoff and routing priority, offering rich behavioral context well 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 somewhat long but densely packed with useful information. It opens with the core function, then lists capabilities, routing, and approval notes. Each section earns its place, though some redundancy with the schema exists (e.g., 'Use model_tier=standard only for cheap drafts' mirrors schema text). Overall well-structured and front-loaded.

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?

With 12 parameters, no output schema, and no annotations, the description covers the key behavioral aspects: generation/edit, quality/resolution, multi-ref limits, routing, and approval workflow. It omits some details like return values or save_to_drive behavior, but these are covered in the schema. Given the tool's complexity, the description is sufficiently complete for an agent to use it confidently.

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

Parameters4/5

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

Schema coverage is 100%, and schema descriptions already explain most parameters with detail. The description adds value by clarifying the meaning of model_tier in context ('Quality Mode default — highest live API fidelity; closest to consumer Image 2.0') and reinforcing the multi-ref composite pattern via artifact_ids. It doesn't reinvent parameter semantics but enriches the selection rationale.

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 opens with a specific verb+resource: 'Generate or EDIT an image using xAI Imagine.' It enumerates supported styles (photorealistic, illustrations, flat graphics, icons, banners), resolutions (1K/2K), and editing capabilities (single or multi-image edit with ≤3 refs). It explicitly designates itself as the routing hub for ALL image generation and editing, distinguishing it from siblings like generate_vector_image and generate_video.

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

Usage Guidelines5/5

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

Provides explicit routing: 'ALL image generation and editing → use this (2 credits).' It also gives conditional guidance: 'Use model_tier=standard only for cheap drafts' and warns about the sensitive-tier approval requirement. This tells the agent when to choose this tool and how to adjust parameters based on use case.

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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