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Moltline Outbound Engine

by com.moltlinestudio

Get Full Product

get_full_product
Read-onlyIdempotent

Load one product in full: its persona plus every paid skill. PREMIUM (license).

Typical input {"slug": "linkedin-outreach"} returns {"slug": ..., "name": ..., "persona": "", "skills": [{"name": ..., "instructions": ...}, ...], "free_skill": {...}}.

Returns persona plus every skill for one product. Use when the caller wants the whole product. Not for a single skill (get_full_skill) and not for a free look, which list_products and get_free_skill provide with no plan. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "unknown slug ''"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesProduct slug exactly as returned by list_products, e.g. "linkedin-outreach".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description goes beyond these by detailing the error behavior ('returns an error object' instead of a protocol error), the idempotent/read-only nature for safe retries, and the premium license requirement. No contradictions.

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 front-loaded with the core purpose and includes a concrete example of input/output. It is slightly longer than minimal due to the thorough error behavior and recovery details, but every sentence adds significant value. Could be tightened by merging the second sentence on typcial structure, but still efficient.

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?

Given the presence of an output schema, detailed annotations, and a single parameter with full schema coverage, the description covers everything: purpose, when to use vs alternatives, error handling, idempotency, and license requirement. It is complete without being redundant.

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?

The input schema already covers the single parameter 'slug' with a clear description. The description adds value by showing a concrete example return structure and noting the source for valid slugs (list_products), which aids correct invocation. Since schema coverage is 100%, baseline is 3, and this extra context justifies a 4.

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 starts with a clear verb+resource combination: 'Load one product in full: its persona plus every paid skill.' It also distinguishes itself from siblings like get_full_skill and list_products, making the purpose 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?

The description explicitly states when to use this tool ('when the caller wants the whole product') and when not to ('Not for a single skill... and not for a free look'), naming specific sibling tools as alternatives. It also provides error recovery guidance, which is exceptional.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Moltline Outbound Engine toolsInspect
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TDQS

A4.6/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: audit_copy for message bodies, subject_line_scorer for subject lines, sequence_planner for cadences, utm_builder for UTM URLs, and separate tools for product/skill retrieval (list_products, get_free_skill, get_full_skill, get_full_product) with explicit differences in scope. No two tools overlap in function.

Naming Consistency5/5

All tools follow a consistent snake_case naming convention with a verb_noun pattern (e.g., audit_copy, list_products, subject_line_scorer). The naming is predictable and easy to understand.

Tool Count5/5

With 8 tools covering copy auditing, subject line scoring, cadence planning, UTM building, and product/skill access, the tool count is well-scoped for an outbound outreach server. Each tool serves a necessary function without any redundancy or missing essential operations.

Completeness4/5

The tool surface covers the core tasks of outreach planning and analysis: copy auditing, subject line ranking, cadence planning, UTM building, and product/skill instructions. Minor gaps exist (e.g., no tool for generating outreach copy or managing contacts), but these are outside the server's stated scope and can be handled by other servers.

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