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open_product_request_draft_pr

MANUAL ONLY — open a draft GitHub PR shell for an approved FreedomOS product request. Approve no longer auto-opens a ticket PR (that class emailed the operator and polluted ship-seat). Prefer the builder spawn rail. Use this only when product team explicitly wants a tracking PR. Do NOT use for questions or high/critical items that need design first.

[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
forceNoRe-dispatch even if a draft_pr is already stamped (default false).
companyIdNoFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
request_idYesrequest_id UUID from submit_product_request

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full transparency burden. It discloses key behavioral traits: manual-only invocation, the approval flow (first use may require manager approval, from-now-on vs just-once behavior), and the reason for the change ('polluted ship-seat'). It does not describe return values or exact side effects, but the provided context is strong for a write-tier tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded with the critical 'MANUAL ONLY' caveat and the core action. It conveys essential usage rules and approval nuances in two short paragraphs without redundancy. Every sentence contributes to invocation correctness.

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 tool with 3 parameters and no output schema, the description provides sufficient context: purpose, manual-only restriction, approval workflow, exclusions, and preferred alternative. It lacks explicit return-value information, but that is not mandatory given the schema's parameter descriptions and the tool's clear output (a draft PR shell). Overall, it is nearly complete for an agent to invoke correctly.

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%, and each parameter includes a clear description in the schema. The tool description does not add meaning beyond the schema; it only reinforces the overall purpose. With full schema coverage, a baseline of 3 is appropriate.

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 the tool's purpose with a specific verb and resource: 'open a draft GitHub PR shell for an approved FreedomOS product request.' It also differentiates from siblings by noting 'Approve no longer auto-opens a ticket PR' and directing to 'Prefer the builder spawn rail,' making the tool's unique role unmistakable.

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?

Explicit when-to-use and when-not-to-use guidance is provided: 'Use this only when product team explicitly wants a tracking PR' and 'Do NOT use for questions or high/critical items that need design first.' It also flags the preferred alternative ('builder spawn rail'), giving the agent clear decision criteria.

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