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freedom-mcp

by com.getfreedomos

deliberate

Run an adversarial deliberation on a decision. Multiple AI perspectives argue opposing positions over multiple rounds, iteratively strengthening arguments, and converge on a recommendation with confidence scoring. Use for important decisions where you want to stress-test options from multiple angles. Over MCP the deliberation runs in the background: the first call returns a run_id immediately; call deliberate again with { run_id } (plus the same companyId) after ~1-2 minutes to fetch the result.

Routing: Important decision → deliberate for adversarial analysis

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
run_idNoPoll a background deliberation started earlier (MCP mode). Pass the run_id returned by the starting call, with the same companyId. Omit question/positions when polling.
contextNoGoals, constraints, values, and relevant data that should inform the deliberation. The more context, the better the arguments.
criteriaNoOptional weighted evaluation criteria. Each item should have "name" (string) and "weight" (number 0-1, should sum to ~1). If omitted, defaults are generated.
questionNoThe decision or question to deliberate. Be specific — e.g., "Should we invest in mobile app development or API partnerships for growth in Q2?" Required unless polling with run_id.
companyIdNoFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
positionsNoTwo or more positions to argue. Each should be a clear, distinct option — e.g., ["Mobile app development", "API partnerships", "Content marketing"]. Required unless polling with run_id.
max_roundsNoMaximum rounds of deliberation (default: 5). More rounds = better arguments but more compute.

TDQS

A4.3/5.0
Behavior4/5

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

No annotations provided, but the description transparently explains the asynchronous behavior: 'first call returns a run_id immediately; call deliberate again... after ~1-2 minutes to fetch the result.' It also describes the multi-round argument process, adding value 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 lengthy but well-organized: purpose, usage, async flow, routing. It front-loads the key action and is structured with clear sentences. A minor reduction could improve conciseness, but it remains efficient.

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?

Given the complexity (async, multiple parameters, no output schema), the description covers the core flow, parameter usage, and rationale. Some details like error handling or maximum wait times are missing, but overall it's sufficiently complete for correct usage.

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%, so baseline is 3. The description adds context for parameters, e.g., 'Omit question/positions when polling', 'The more context, the better the arguments', and required conditions. This adds meaningful guidance 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's purpose: 'Run an adversarial deliberation on a decision.' It uses a specific verb and resource, and the adversarial deliberation concept is unique among siblings, distinguishing it effectively.

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?

It advises 'Use for important decisions where you want to stress-test options from multiple angles.' It includes a routing note: 'Important decision → deliberate for adversarial analysis.' While it doesn't explicitly mention when not to use, context 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.

Resources