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GarphenGate

moltline-mcp

Search Catalog

search_catalog
Read-onlyIdempotent

Browse a 138-product catalog by keyword or focus area, returning a ranked list with names, areas, segments, and taglines. Invalid input yields clear fix guidance, so you can correct and retry safely.

Instructions

Search all 138 catalog products by keyword and/or focus area. FREE.

Typical input {"query": "email inbox", "limit": 5} returns a ranked list of product objects [{"slug": ..., "name": ..., "area": ..., "segment": ..., "tagline": ...}]. An empty query with an area set browses that area.

Use when the caller names a keyword, product, or focus area to look up. Not for bundles (list_kits) and not for plain-language problem statements, which recommend_products ranks by fit rather than keyword match. 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
areaNoOptional focus-area filter; one of "productivity", "developertools", "personalassistant", "creative", "industryspecific", "educational". Empty means all areas.
limitNoMaximum results to return; values outside 1-100 are clamped. Default 15.
queryNoSpace-separated keywords matched against name, tagline, slug, and segment, e.g. "email inbox".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, and the description adds valuable behavioral detail beyond those: it never raises a protocol error but returns an {'error': ...} object with remediation, every call is safe to retry, and an empty query with an area browses that area. This gives the agent clear expectations for abnormal input without contradicting the annotations.

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 front-loaded with the core action, followed by a compact example, usage rule, exclusions, and error behavior. Every sentence adds distinct value; there is no filler or repetition beyond what annotations already provide.

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 three optional parameters, a full input schema, an output schema, and annotations covering read-only/idempotent behavior, the description fills the remaining gaps: exact error semantics, result shape, usage boundaries, and browsing behavior. An agent has everything needed to select and invoke this tool correctly.

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?

Schema coverage is 100%, so the schema carries the basic parameter definitions. The description adds meaning beyond the schema by showing a typical input object, clarifying that query matches name/tagline/slug/segment in context, and explaining the empty-query-with-area browsing behavior. This exceeds the baseline for well-covered schemas.

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 states a specific verb and resource ('Search all 138 catalog products by keyword and/or focus area') and immediately distinguishes itself from siblings by saying what it is not for: bundles (list_kits) and plain-language problem statements (recommend_products). The example input/output makes 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?

It gives explicit when-to-use guidance: 'Use when the caller names a keyword, product, or focus area to look up.' It also gives explicit when-not-to-use guidance with named alternatives: not for bundles (list_kits) and not for problem statements (recommend_products ranks by fit). This is exactly the level of routing an agent needs.

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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