Skip to main content
Glama
wanbnn
by wanbnn

remember_delivery

Save a record of implemented UI components and summaries for a research project, so later reviews can verify that delivery matches intent.

Instructions

Persist what the agent implemented so later reviews can compare intent and delivery.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filesNo
summaryYes
project_idYes
research_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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 indicates a write operation ('persist') but does not mention whether it overwrites existing records, whether it is idempotent, if authorization is required, or any side effects. It adds only minimal context beyond the tool's name and purpose.

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 a single sentence that is front-loaded with the key verb ('Persist') and contains no filler or redundant information. Every word contributes to explaining the core functionality, making it highly concise and well-structured.

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 has 4 parameters and no schema-level descriptions, the description is too sparse to provide complete context. Even though an output schema exists, the description does not explain how the tool fits into the workflow, what constitutes 'delivery', or what data structures are involved. The minimal text leaves important gaps for an agent deciding when and how to use it.

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

Parameters1/5

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

Schema description coverage is 0% for 4 parameters, and the description does not compensate. It makes no mention of project_id, summary, files, or research_id, nor explains their roles or relationships. This leaves the input schema as the only source of parameter information, which is insufficient for correct invocation.

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 uses a specific verb ('persist') and identifies the resource ('what the agent implemented'). It also states the intended outcome ('so later reviews can compare intent and delivery'), which clearly distinguishes it from sibling tools like review_implementation (which likely performs the comparison) and recall_project (which likely retrieves prior records).

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 context: after implementing something, persist it for later reviews. However, it does not explicitly state when not to use the tool or mention alternatives. It lacks direct guidance on choosing between remember_delivery and its siblings, though the purpose is clear enough to infer the intended timing.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/wanbnn/MCPUIResearch'

If you have feedback or need assistance with the MCP directory API, please join our Discord server