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freedom-mcp

by com.getfreedomos

update_agent_avatar

Generate or regenerate AI agent profile avatar(s) for a company's AI team. Use when an operator wants to create, refresh, or restyle one or more agents' profile avatars. Single agent: pass agent_id OR agent_name. Several agents: pass agent_ids[] OR agent_names[] in ONE call. Whole team: pass all:true. The tool regenerates EVERY target itself in a single call (1 credit per agent) and returns the real new signed avatar_url for each. Report ONLY the agents listed in the result's regenerated array — never claim or invent an avatar for an agent the tool did not return.

[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
allNoSet true to regenerate avatars for EVERY active agent in the company. Takes precedence over the id/name params.
styleNoOptional style override (e.g., "pixel-art", "watercolor", "geometric"). Overrides company avatar_theme for this generation.
agent_idNoUUID of a single agent to (re)generate an avatar for.
agent_idsNoUUIDs of multiple agents to regenerate in ONE batch call.
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
agent_nameNoName of a single agent (used to look up the agent when agent_id is not provided). Must resolve to exactly one active agent.
agent_namesNoNames of multiple agents to regenerate in ONE batch call. Each name must resolve to exactly one active agent (ambiguous names are returned in `failed`).

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations provided, the description fully discloses behavioral traits: it regenerates avatars, consumes 1 credit per agent, returns signed avatar URLs, and mentions the sensitive-tier approval process. It warns against inventing avatars for agents not returned, which is a key behavioral constraint.

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 front-loaded with the purpose and use cases, and every sentence adds value. However, it is slightly lengthy due to the inclusion of the approval note and reporting instructions, which could be more succinct without losing clarity. Still, it is well-structured and easy to follow.

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

Completeness5/5

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

Given the tool's complexity (7 parameters, multiple modes, no output schema), the description is remarkably complete. It covers purpose, usage scenarios, parameter interactions, behavioral details, and post-call reporting. The only missing element is explicit error handling, but the schema comments and description indirectly address failures (e.g., ambiguous names in 'failed').

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%, but the description adds substantial meaning beyond the schema. It explains the precedence of 'all' over id/name parameters, the resolution behavior for agent_name (must map to exactly one active agent), and the style parameter's purpose (overrides company theme). It also clarifies that ambiguous names are returned in 'failed'.

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 generates or regenerates AI agent profile avatars, distinguishing it from sibling tools like 'update_agent' by focusing specifically on avatar operations. It explicitly names the resource (AI agent profile avatar) and the action (generate/regenerate).

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?

The description provides explicit when-to-use guidance (operator wants to create, refresh, or restyle avatars). It details three distinct usage modes with corresponding parameter combinations: single agent (agent_id/agent_name), several agents (agent_ids[]/agent_names[]), and whole team (all:true). It also includes a critical reporting instruction to only list agents in the 'regenerated' array.

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