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dogfoodlab-io

StashDog MCP Server

manage_inventory_items

Add, update, search, or delete inventory items using natural language commands. Manage items with tags, notes, custom fields, and organize them in containers.

Instructions

Add, update, search, delete, or manage inventory items using natural language instructions. Supports complex operations like adding items with tags, notes, custom fields, and organizing them in containers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
instructionYesNatural language instruction for the item operation. Examples: "Add a new laptop with tags electronics, work", "Search for items tagged with kitchen", "Update item abc123 to add note about warranty", "Delete item xyz789"
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions operations like add, update, delete (implying mutations) and supports complex features, but fails to disclose critical traits such as authentication needs, rate limits, error handling, or whether changes are reversible. This is inadequate for a multi-operation tool with zero annotation coverage.

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 appropriately sized with two sentences that efficiently cover purpose and features. It's front-loaded with core operations and avoids unnecessary details, though the second sentence could be slightly more streamlined.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (multiple operations like add/delete), lack of annotations, and no output schema, the description is incomplete. It doesn't explain return values, error conditions, or behavioral nuances needed for safe invocation, leaving significant gaps for an AI agent.

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 schema description coverage is 100%, so the input schema already documents the single parameter thoroughly with examples. The description adds no additional parameter semantics beyond what's in the schema, such as format constraints or edge cases, meeting the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

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 specific verbs (add, update, search, delete, manage) and resource (inventory items), distinguishing it from siblings like get_inventory_stats or manage_tags. However, it doesn't explicitly differentiate from manage_collections or manage_groups, which might handle similar resources.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage through natural language instructions for inventory operations, but provides no explicit guidance on when to use this tool versus alternatives like smart_search or manage_tags. It mentions complex operations but doesn't specify prerequisites or exclusions.

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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