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maschmann

mcp-context-memory

by maschmann

remember_decision

Save architectural decisions with detailed context and reasoning to preserve project knowledge across AI sessions.

Instructions

Allows the LLM to save manual architectural notes or 'Why' something was built a certain way.

Args: topic: A short topic or category name for this decision. context: The detailed reasoning, architectural decision, or context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYes
contextYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations, the description carries full burden. It discloses the tool's action (saving notes) but does not mention persistence behavior, whether it overwrites existing decisions, or any side effects. No annotation contradiction detected.

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 concise and well-structured, with a lead sentence and clear arg list. No fluff or redundancy.

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

Completeness3/5

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

Given the simple two-string-parameter form, the description covers the essential purpose and params. However, it omits behavioral details (e.g., how decisions are stored or retrieved) and does not provide usage context relative to siblings, leaving minor gaps.

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?

The schema itself has 0% description coverage, but the tool description compensates by defining both params: topic as a short category and context as detailed reasoning. This gives meaningful semantic guidance beyond the raw string type.

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 saves manual architectural notes or 'Why' decisions, using the verb 'save' with a specific resource. It distinguishes itself from sibling tools (index_project, search_context) by its save/remember role, though it doesn't explicitly name alternatives.

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 use for recording architectural decisions but provides no explicit guidance on when to use it instead of search_context or index_project. No when-not-to-use conditions are given.

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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