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park_attention_sessions

Park THIS operator's coding host sessions (N6 hygiene). Use after "clean tabs" / "park ghosts" / list shows dead running hosts. Pass session_ids for explicit targets, or stale_running=true to park running/unknown hosts that failed freshness (no recent heartbeat). dry_run=true previews only. Marks FO rows parked — does not kill Terminal processes. Never parks blocked_on_operator needs-you hosts unless listed in session_ids.

[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
NameRequiredDescriptionDefault
dry_runNoIf true, return candidates without writing.
companyIdNoFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
session_idsNoExplicit session ids to park (from list_attention_sessions / get_attention_quest tool-only fields).
stale_runningNoIf true, also park running/unknown sessions that fail freshness hygiene.

TDQS

A4.8/5.0
Behavior5/5

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

No annotations exist, so the description carries the full burden. It discloses the side effect ('Marks FO rows parked — does not kill Terminal processes'), the preview behavior ('dry_run=true previews only'), and the safety rule about blocked_on_operator hosts. It also notes write-tier approval requirements. These are concrete behavioral insights beyond the bare operation.

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 a dense paragraph covering many aspects (trigger, targeting modes, exclusions, side effect, approval) without redundancy. It is longer than the minimal example but every sentence adds information. The bracketed approval note is a minor structural extraneity but relevant.

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

Completeness5/5

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

For a mutation tool with no output schema and no annotations, this description is remarkably complete. It covers when to use, how to target, dry-run behavior, exclusions, and side effects, making it safe for an agent to invoke correctly. The only gap is the absence of return-value information, but that's not required given the description's depth.

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 coverage is 100%, so baseline is 3. The description adds value by explaining the relationship between session_ids and stale_running ('Pass session_ids for explicit targets, or stale_running=true'), and clarifies 'stale' as 'failed freshness (no recent heartbeat)' — a detail not fully defined in the schema. It also points to list_attention_sessions / get_attention_quest as sources for session_ids, though the schema already includes this.

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 a specific action ('Park THIS operator's coding host sessions') and resource ('coding host sessions'), with a hygiene purpose. It distinguishes from siblings like list_attention_sessions and upsert_attention_session by focusing on parking (de-prioritization) rather than listing or upserting.

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?

Explicitly states when to use: 'Use after "clean tabs" / "park ghosts" / list shows dead running hosts.' It also explains the two targeting modes (session_ids vs stale_running) and the exclusion rule: 'Never parks blocked_on_operator needs-you hosts unless listed in session_ids.' The note that it does not kill Terminal processes provides an alternative boundary.

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