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get_next_priority

Answer "What should I work on?" in two beats: it leads with the single most-actionable pending Command Center card the operator's rail features first (when the queue has one), then the strategic move synthesized from OKRs, the revenue constraint, and active Playbooks. The featured card includes approval_status, available_actions, and resolution_progress (PR/builder in-flight). Call this when the user asks "What should I work on?" or "What's my priority?" Returns focused recommendations with reasoning.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
focus_areaNoOptional: focus on a specific Playbook category
override_constraintNoOptional operator PIN of the binding revenue constraint. When set, it is SAVED as this company's pin (upsert) and the priority is computed from it instead of the automatic funnel diagnosis. Use only when the operator explicitly overrides the computed constraint.

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It describes the two-beat logic and the fields included in the featured card (approval_status, available_actions, resolution_progress), which is useful. However, it omits that override_constraint persists a pin (a side effect) and does not state whether the operation is read-only. Such behavioral disclosure is missing.

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 moderately long but each sentence delivers specific value: the core answer, the two-beat structure, the featured card contents, and the trigger phrases. It is front-loaded and does not repeat schema info, though it could be slightly trimmed without losing meaning.

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

Completeness3/5

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

The tool has no output schema and no annotations, so the description should clarify the return shape. It only says 'Returns focused recommendations with reasoning,' which is vague. It also does not explain how focus_area or override_constraint affect the prioritization beyond the schema's brief notes. Some ambiguity remains in the output structure and parameter impact.

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% for all three parameters, including enums for focus_area and override_constraint. The main description does not add extra parameter semantics beyond what the schema already explains, so the baseline of 3 applies.

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 states a specific verb ('Answer What should I work on?') and resource (prioritized recommendations from Command Center cards and strategic moves), and clearly distinguishes this from listing tools like get_command_center_items by describing synthesis and reasoning. It leaves no doubt what the tool does.

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?

Explicitly states when to call: 'when the user asks "What should I work on?" or "What's my priority?"' This gives clear trigger phrases. However, it does not mention alternatives or when not to use it, so it falls short of a 5.

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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