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freedom-mcp

by com.getfreedomos

get_okrs

List objectives and key results for the company. Each KR current is THIS calendar month (see month_updated + monthly_history) — not YTD, not a future projection, not the due-date month. Refresh cash numbers from get_financial_summary (displayed_net_cash_flow) and Amazon deposits from get_monthly_trends (Amazon Sales). Bindable live sources: stripe_active_subscribers, stripe_mrr, crm_active_leads. Defaults to current year unless year specified or all_years=true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoFilter by year (e.g., 2026). Defaults to current year.
limitNoMaximum number to return (default: 10)
all_yearsNoSet to true to get OKRs across all years (overrides year filter)
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.

TDQS

A4.3/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does so well. It discloses that results reflect current-month status, that cash and Amazon figures should be refreshed from other tools, and that live sources are bindable. This goes well beyond what the schema or annotations could convey.

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 its purpose and each sentence adds useful context. It is concise but dense; terms like 'Bindable live sources' are slightly domain-specific but not bloated.

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?

For a list tool with no output schema, the description covers the key semantic traps, data freshness caveats, and default behaviors. It does not describe the full output shape, but it references relevant fields like month_updated and monthly_history, giving enough context for an agent to call and interpret results.

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 description coverage is 100%, so the schema already documents all four parameters. The description reinforces the year/all_years defaults but does not add meaningful semantics beyond what the schema contains.

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 pair: 'List objectives and key results for the company.' It also clarifies the unique temporal scope of KR values, which helps distinguish this read tool from create/update/delete OKR siblings.

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?

The description provides important usage context: KR values are for the current calendar month, not YTD/projections/due-date month, and the year filter defaults to the current year unless overridden. It also directs the agent to complementary tools for refreshed cash and Amazon figures, though it does not explicitly name alternatives for the listing itself.

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