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Glama

Moltline Outbound Engine

List Products

list_products
Read-onlyIdempotent

List the 7 outbound products with their included skills. FREE.

Takes no arguments. Returns a list of product objects, each {"slug": "linkedin-outreach", "name": ..., "tagline": ..., "skills": ["Skill A", ...], "free_skill": "Gateway Skill Name"}. Use the returned slug values with get_free_skill, get_full_product, or get_full_skill.

Returns metadata only - no persona text and no skill instructions. Use when the caller wants to see what this server covers. Not for keyword search across the whole 138-product catalog, which the catalog server's search_catalog does, and not for instructions the caller can act on (get_free_skill). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, but the description adds valuable behavioral context: return format is metadata only (no persona/skill instructions), errors are returned as objects rather than protocol errors, and retries are safe. This exceeds what annotations alone provide.

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?

Well-structured, front-loaded with the core function, then usage guidance, error behavior, and retry safety. Every sentence adds distinct value with no redundancy. Length is appropriate for the information density.

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 zero parameters, rich annotations, and an output schema, the description covers all essential aspects: what returns, what it excludes, error handling, and retry semantics. No gaps remain for correct invocation and interpretation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Description explicitly states 'Takes no arguments' and 'FREE', which is fully aligned with the empty schema. Since there are zero parameters, baseline is 4, but it earns a 5 by explicitly clarifying the input contract and referencing the output structure that uses returned slug values, adding meaning beyond schema.

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?

States a specific verb and resource ('List the 7 outbound products with their included skills'), mentions the exact count and scope, and distinguishes itself from siblings by explicitly naming get_free_skill, get_full_product, and get_full_skill. Clear differentiation from the catalog search tool.

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 says when to use ('when the caller wants to see what this server covers'), what not to use it for ('Not for keyword search... which the catalog server's search_catalog does'), and points to alternatives for actionable instructions. Also notes zero-argument simplicity and error-retry safety.

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

A4.7/5.0
Disambiguation5/5

Each tool has a well-defined, unique purpose. The product/skill tools (list_products, get_free_skill, get_full_skill, get_full_product) are differentiated by scope and access level, and the writing tools (audit_copy, subject_line_scorer, sequence_planner, utm_builder) cover distinct aspects of outreach creation with no overlap. Descriptions explicitly state what each tool is not for, further reducing ambiguity.

Naming Consistency5/5

All tool names use snake_case with a consistent verb_noun pattern: 'verb_object' for actions like list_products, get_free_skill, and 'noun_verb' for compound tools like subject_line_scorer. There is no mixing of styles or inconsistent abbreviations.

Tool Count5/5

With 8 tools, the set is well-scoped for an outbound engine. It covers product discovery, skill retrieval, copy auditing, subject line scoring, sequence planning, and UTM building—neither too few to be useful nor too many to be overwhelming.

Completeness5/5

The tool surface appears complete for its stated domain: product/skill lookup, content quality checks (audit_copy, subject_line_scorer), cadence planning (sequence_planner), and campaign tracking (utm_builder). There are no obvious gaps for the knowledge and planning phase of outbound outreach.

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