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sync_stripe_conversions

Record won deals from the company's connected Stripe so lead→paid conversion becomes measurable. Reads paid Stripe customers (read-only), matches them to leads by email, and records a closed_won deal per paying customer (idempotent — re-running is safe, never double-counts). Only works if Stripe is connected. Use when conversion "isn't measured yet" or to refresh the conversion picture.

Routing: CRM/sales/revenue → measure conversion / record won deals from Stripe → use this

[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
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations provided, the description bears the full burden. It discloses that the tool is read-only on Stripe ('Reads paid Stripe customers (read-only)'), idempotent ('re-running is safe, never double-counts'), and requires a connected Stripe account. It also mentions an approval tier for first use. This level of detail is exemplary for behavioral transparency.

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 yet comprehensive. It is front-loaded with the primary purpose, then explains the process, usage context, routing, and approval notes. Every sentence adds value, and the structure is logical. No redundant or vague statements.

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 tool with one simple input parameter and no output schema, the description provides complete context: the process (reading customers, matching by email, recording deals), idempotency, prerequisites, and approval tier. An agent can fully understand what the tool does and what to expect without additional information.

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?

The input schema has only one required parameter (companyId) with a description already covering its purpose and required membership. The tool description does not add any further information about the parameter, but given 100% schema coverage, a baseline score 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: recording won deals from Stripe to measure lead→paid conversion. It uses specific verbs like 'reads', 'matches', 'records' and describes a specific resource (paid Stripe customers). It distinguishes from sibling tools by specifying when to use it (conversion not yet measured or to refresh) and implies it's different from other Stripe tools like get_stripe_metrics.

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 provides clear context on when to use the tool: 'Use when conversion isn't measured yet or to refresh the conversion picture.' It also notes a prerequisite ('Only works if Stripe is connected'). However, it does not explicitly state when not to use it or name alternatives among siblings, which would raise it to 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.

Resources