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AliYar-Khan

light-agent-memory-mcp-server

by AliYar-Khan

Save Project

memory_project_save
Idempotent

Save or update project context including tech stack, architecture, conventions, and notes to persistent memory for future retrieval.

Instructions

Save or update project context — tech stack, architecture, conventions, and notes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesUnique project name (e.g. 'my-app')
pathNoFilesystem path to the project root
notesNoGeneral notes about the project
tech_stackNoTechnologies used (e.g. ['TypeScript', 'SQLite', 'React'])
conventionsNoCoding conventions and patterns
architectureNoArchitecture notes
Behavior3/5

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

Annotations already establish that this is not read-only, is idempotent, and is not destructive. The description adds some useful nuance with 'save or update', indicating an upsert-like behavior, but it does not disclose whether updating an existing project merges fields, overwrites them, or handles omitted fields in a particular way.

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 efficient sentence with the key action and resource front-loaded. Every word earns its place; no repetition, filler, or unnecessary abstraction.

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?

The description is reasonably complete for a simple parameter-rich, schema-covered save operation, especially with idempotentHint available. However, the lack of update semantics details, such as whether existing fields are merged or replaced, and the absence of any mention of path or project identity behavior, leave some ambiguity for an agent making corrective updates.

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 description coverage is 100%, so the input schema already documents name, path, notes, tech_stack, conventions, and architecture. The description only re-lists a few of those fields at a high level and does not add meaning beyond what the schema already provides. This maps to the baseline of 3.

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 has a specific action ('Save or update') and a clear resource ('project context'), plus an explicit list of the stored content areas: tech stack, architecture, conventions, and notes. The 'project' framing distinguishes this from sibling memory tools such as memory_pref_save, memory_learning_save, and general memory_save.

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 'project context' wording implies this tool is for project-specific memory rather than preferences or learnings, but it does not explicitly say when to choose this tool over memory_save, memory_project_get, or other siblings. There are no alternatives, caveats, or when-not-to-use conditions 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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