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upsert_attention_session

Emit or update thin session telemetry for THIS operator (host coding agent self-announce). Use when YOU are Grok or Claude Code at session start / status change so voice CoS can list_attention_sessions and target you. Prefer tiny goals; never dump transcripts.

[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
cwdNoOptional working directory
goalNoOne-line goal
hostNoclaude-code | claude-desktop | grok | manual | slack | github | freedomos | other
turnsNoGrok Bot only: append last user/bot lines (role you|bot, text ≤280). Server keeps the last 12. Omit to preserve. Never dump a full transcript.
statusNorunning | blocked_on_operator | done | parked | unknown (blocked_on_tim accepted as alias)
projectNoOptional project name
artifactNoShip-seat stamp when known (e.g. pr:1752). Local and FO spawns use the same field — origin does not matter. If omitted and goal names a PR, server may infer pr:N.
priorityNoOptional priority (higher = sooner)
companyIdNoFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
last_beatNoResume line — where this session left off, one sentence (≤240 chars). Voice CoS speaks it as "here's where we left off" so the operator never re-reads a transcript. Real content only, never bookkeeping text.
company_idNoOptional company id
session_idYesStable session id (same string used as target_session_id for directives).
ask_for_operatorNoIf blocked: one sentence the operator must answer

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool is scoped to 'THIS operator' (host coding agent self-announce) and includes a write-tier warning about approval requirements. However, it doesn't disclose whether updates overwrite or merge, what happens if session_id is new vs existing, or the persistence/retention behavior of the telemetry.

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 at ~60 words for the main purpose and a parenthetical for write-tier. Key information is front-loaded. Every sentence adds value. Minor deduction: The write-tier note could be more elegantly integrated or structured as a separate line, but it's not wasteful.

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 the tool's moderate complexity (13 parameters, but simple inputs with no output schema), the description covers the core use case well: when to call it, what it's for, and key behavioral notes. It's complete enough for a self-telemetry tool. The write-tier approval flow adds helpful context. The absence of output schema discussion is acceptable as the input schema is rich.

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?

With 100% schema coverage and 13 parameters, the schema already documents each parameter well. The description adds value by specifying usage patterns (e.g., 'tiny goals', 'never dump transcripts'), the purpose of last_beat for voice CoS, and the context for artifact. It also clarifies the write-tier approval flow beyond schema. The description doesn't add syntax details for parameters already well-described in schema, which 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 clearly states the tool emits or updates thin session telemetry for 'THIS operator', specifying it's for Grok or Claude Code self-announcement at session start or status change. It distinguishes from siblings via purpose (self-telemetry vs. list_attention_sessions or ack_attention_directive), though it doesn't explicitly differentiate from park_attention_sessions.

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 says 'Use when YOU are Grok or Claude Code at session start / status change' and references a sibling (list_attention_sessions) as a consumer. It gives pragmatic advice ('prefer tiny goals; never dump transcripts'). However, it doesn't explicitly state when NOT to use this tool versus related tools like park_attention_sessions.

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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