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tinhien11

tdai-memory-mcp

by tinhien11

capture

Store decisions, learnings, and task outcomes to memory. Use this action after completing tasks or fixing bugs to preserve key context for future reference.

Instructions

Save a decision, a learning, or a task outcome to memory. Call this tool after you complete a non-trivial task, make a decision, or fix a bug with a known root cause.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoOptional tags.
typeYesThe type of the memory.
contentYesThe text to remember. The tool redacts secrets before it stores the text.
metadataNoOptional metadata.
session_keyNoThe session key. The default is hash(cwd).
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the basic action and usage timing, failing to mention side effects, idempotency, overwrite behavior, or return values. The redaction behavior appears only in the schema parameter description, not in the tool description itself.

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 two concise sentences, front-loaded with the purpose and immediately providing usage guidance. Every word earns its place with no unnecessary filler.

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

Completeness4/5

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

Given the simplicity of the tool, full schema coverage, and no output schema, the description sufficiently explains what to save and when. It lacks some behavioral details but is adequate for an agent to select and invoke the tool correctly.

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?

Schema coverage is 100%, and the description adds minor context by giving examples of content types (decision, learning, task outcome) that map to the 'type' enum. However, it does not elaborate on other parameters like tags, metadata, or session_key, so it only slightly enhances the schema information.

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 clearly states the tool saves decisions, learnings, and task outcomes to memory using a specific verb ('Save') and resource ('memory'). It distinguishes itself from sibling tools like recall, search, and forget by focusing on writing new memories.

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

Usage Guidelines4/5

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

The description explicitly says to call this tool after completing a non-trivial task, making a decision, or fixing a bug with a known root cause, providing clear usage context. It does not mention exclusions or alternatives, but the context is sufficient without them.

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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