freedom-mcp

suggest_collaboration

Create a cross-agent collaboration request. Use when one agent identifies work that another agent should handle, or when the analysis reveals a gap that could be filled by an existing team member. If the target role doesn't exist on the team, mention it as a hiring opportunity instead.

Input Schema

TableJSON Schema

Name	Required	Description
`priority`	No	How urgent is this collaboration request
`companyId`	Yes	FreedomOS company id to act within (you must be a member). Required for company-scoped tools.
`to_agent_name`	Yes	Name of the target agent, or a role description if the agent doesn't exist yet
`from_agent_name`	Yes	Name of the agent suggesting the collaboration (e.g., "Maya", "Evan")
`task_description`	Yes	What needs to be done — specific and actionable

TDQS

A4.4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It explains the purpose but does not disclose what happens after creation (e.g., notification, storage, side effects). The mention of 'hiring opportunity' suggests conditional behavior but lacks detail.

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?

Three sentences, front-loaded with the main action, no fluff. Every sentence adds value—purpose, usage guidance, and an edge-case hint.

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?

The description is mostly complete for a creation tool, but lacks details on return values or confirmation. Since there is no output schema, this is a minor gap. Sibling tools like 'create_attention_directive' have similar descriptions.

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 covers all parameters with 100% coverage. The description adds context by explaining when to use 'to_agent_name' as a role description and the alternative behavior for missing roles. This enhances 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 it creates a cross-agent collaboration request, specifying the verb 'Create' and resource 'collaboration request'. It distinguishes from sibling tools like 'send_slack_message' by focusing on internal agent work delegation.

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 explicitly states when to use: when one agent identifies work for another or when a gap is found. It also provides an alternative: if the target role doesn't exist, mention it as a hiring opportunity, implying use of a different tool like 'suggest_next_hire'.

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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