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recalibrate_agent_jd

Regenerate an agent's JD using fresh company context. Updates mission, expertise, guardrails, success metrics, and optionally activity plans. Works for both hired agents and Linnet. Use when the company has evolved, an agent needs recalibration, or the user wants to refine an agent's direction.

[sensitive-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
NameRequiredDescriptionDefault
agent_idYesUUID of the agent to recalibrate. Use get_team_roster to find IDs.
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
focus_areasNoOptional user guidance for recalibration, e.g. "focus more on SEO" or "add financial analysis"
regenerate_activitiesNoAlso regenerate the activity plan (default: false — preserves evolved activities). Regenerated activities must name a Key Result or they are not loops — create the KR first if none exists.

TDQS

A3.9/5.0
Behavior4/5

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

No annotations are provided, so the description carries full behavioral transparency. It discloses that this is a mutating operation, that it optionally regenerates the activity plan, that activity regeneration preserves evolved activities by default, that regenerated activities must name a Key Result, and that the operation is sensitive/provision. This is usefulerly behavioral tone. It doesn't explicitly state rollback/reversibility, but it explains planning and side effects meaningfully.

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 leads with the primary action and a compact scope list, then gives the when to use, then the approval caveat at the end. It front-load the purpose, contains no wast, and the long bracket note is still relevant. It is not perfectly acousisic, but it is easily scanable.

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?

It includes what is updated, when to use it, optional behavior details, the special default for activity plans, the KR rule, and a notible process (sensitive-tier approval) that is not in structred fields. The main gap is that it doesn't mention the output format, but with no output schema and a strong description, this weighs more minor.

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 percent and the schema already describes all four parameters in detail, including how to find agent IDs and what companyId must be.a. The description repeats 'updates ... optionally activity plans' and adds no essential parameter guidance beyond the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description is clear: 'Regenerate an agent's JD' is a specific verb+resource, and it enumerates the parts that are updated: mission, expertise, guardrails, success metrics, and planned activities. It also distinguishes who it works for, hired agents and Linnet, from generic agent tools. It does not fight an explicit sibling contrast, but the core purpose is unambiguous.

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 explicitly names when to use the tool: when the company has evolved, when an agent needs knowack, or when the user wants to refine direction. It adds an operational condition around sensitive approval. It does not provide negative guidance or alternatives, but it gives enough context for an agent to identify the primary triggering situation.

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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