freedom-mcp

generate_key_results

Generate intelligent, context-aware key result suggestions for an objective. Uses company mission, vision, financials, and existing KRs to produce high-quality suggestions tied to north star metrics. Returns suggestions that you can then create with create_key_result.

[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

Name	Required	Description
`companyId`	Yes	FreedomOS company id to act within (you must be a member). Required for company-scoped tools.
`objective_id`	No	ID of the objective (optional if using objective_title)
`objective_title`	No	Title of the objective to generate key results for (use this or objective_id)

TDQS

A4.3/5.0

Behavior5/5

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

No annotations are present, so the description carries full weight. It discloses that it uses company data, returns suggestions, and includes an important behavioral note about write-tier approval requirements (first use may require manager approval, with options for ongoing or one-time approval). There is no contradiction with annotations.

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 concise, with two clear paragraphs: the first explains the tool's functionality and value, the second provides behavioral notes. No unnecessary words, though the approval note could be slightly more integrated.

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 no output schema, the description adequately explains the return (suggestions for use with create_key_result) and the input context (mission, vision, financials, existing KRs). It covers main aspects but lacks details on edge cases or limitations.

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

Parameters3/5

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

Schema coverage is 100% with each parameter described. The description reiterates that suggestions are generated for an objective (matching objective_id/objective_title) but adds no new parameter-level details beyond the schema. Baseline of 3 is appropriate.

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 specifies the verb 'Generate' and the resource 'key result suggestions for an objective', and distinguishes itself from sibling tools like 'create_key_result' by stating it returns suggestions that can then be created. It also details the context used (mission, vision, financials, existing KRs).

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 provides a clear workflow: generate suggestions then use create_key_result. However, it does not explicitly state when not to use this tool or compare to alternatives like get_okrs or update_key_result, which would improve guidance.

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

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