N3MemoryCore MCP — Lite (Ephemeral)

by NeuralNexusNote

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.9.1
Disambiguation5/5
Each tool has a distinct purpose: bulk delete, single delete, list, context recall, index repair, save, and search. No two tools overlap in functionality.
Naming Consistency5/5
All tool names follow a verb_noun pattern (e.g., save_memory, delete_memory). Even 'recall_thread' and 'delete_memories_by_session' adhere to this convention with minor additions, maintaining consistency.
Tool Count5/5
With 7 tools covering create, read (list/search), delete (single/bulk), context recall, and maintenance, the count is well-scoped for a Lite ephemeral memory server.
Completeness3/5
The set covers save, list, search, delete, and context recall, but lacks a direct get-by-ID tool. While search can retrieve memories, the absence of a dedicated fetch tool is a notable gap.
Average 4/5 across 7 of 7 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on May 31, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only mentions idempotency and return format. Missing disclosure of potential time consumption, side effects on other operations, or error conditions. For a repair tool, more detail is expected.
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?
Single sentence covering core purpose, idempotency, and return value. Efficient but could benefit from a bit more specificity on what 're-ensure' means operationally.
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?
Description covers basic function and return but lacks context on when/why to invoke this tool. Given no output schema and no annotations, it is minimally adequate for a simple repair operation but not comprehensive.
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?
No parameters in input schema, so description naturally adds no parameter details. Per guidelines, baseline is 4 for zero-parameter tools. The description is sufficient.
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 it re-ensures the RediSearch index and notes idempotency. The verb 're-ensure' is somewhat ambiguous but generally conveys a repair/maintenance action. It distinguishes from siblings which are CRUD operations on memories.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like delete_memory or search_memory. It lacks context such as prerequisites or typical use cases (e.g., after index corruption).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description provides moderate transparency by stating the ordering (newest first) and a display behavior (parent document tag). However, it omits details like pagination, default limit behavior beyond schema, or any side effects.
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?
Two concise sentences, front-loaded with the core purpose, and no unnecessary words. Every sentence adds value.
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 (1 optional parameter, no output schema, no annotations), the description covers key behaviors (order and parent document display). However, it could be more complete by briefly noting the default limit or response structure, but it is sufficient for a basic list tool.
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?
With 100% schema description coverage for the single parameter 'limit', the description adds no additional meaning beyond the schema. The baseline is 3, and the description merely repeats the schema's implication without elaboration.
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 action (list), the resource (stored memories), the order (newest first), and a display detail (parent documents with [doc×N] tag), effectively distinguishing it from siblings like search_memory which implies filtered retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives like search_memory or save_memory. The description only states what it does without any when-to-use or when-not-to-use context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses the hybrid search method and the 7-day expiration for lite memories. No annotations exist, so the description carries the burden; it does not explicitly state read-only or non-destructive nature, but 'search' implies it.
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?
Two sentences with no wasted words. The purpose and usage guideline are front-loaded, making it efficient.
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?
No output schema is provided, but the description does not explain what the tool returns (e.g., relevance scores, memory content). This leaves a significant information gap for the 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?
Schema coverage is 100%, so the schema already describes all 5 parameters in detail. The description adds no additional parameter meaning beyond what is in the schema.
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 it performs a 'Hybrid (vector + BM25) search over stored memories', which is specific and distinct from sibling tools like save_memory or delete_memory.
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?
Explicitly instructs to 'Call this at the start of every user turn', providing strong usage guidance. However, it does not mention when not to use it or explicitly compare to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses TTL ('Lite: 7-day TTL'), auto-deduplication, and chunking behavior. Lacks information on idempotency, overwrite behavior, or error handling, which would improve transparency.
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?
Description is three sentences with zero waste. First sentence states primary purpose, followed by concise coverage of key behaviors (TTL, dedup, chunking). Front-loaded and efficient.
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?
No output schema, so description could explain return values. It covers tool behavior (TTL, dedup, chunking) but lacks information on what is returned (e.g., memory ID, success status). Adequate but not fully complete.
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% with detailed parameter descriptions. The tool description adds overall context (e.g., chunking, dedup) but does not significantly enhance parameter meaning beyond the schema. Baseline 3 is appropriate.
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?
Description clearly states 'Save a memory entry' with specific verb and resource. It distinguishes from sibling tools (delete, list, recall, repair, search) by focusing on creation/insertion.
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?
Description does not explicitly state when to use this tool versus alternatives. It implies usage for saving memories but lacks when-not or alternative context. Sibling tool names suggest distinct purposes, but no guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden. It discloses deletion scope, irreversibility, and scope to configured owner. Lacks auth notification or rate limits, but irreversibility is well-stressed.
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?
Four sentences with no waste. Each sentence contributes: what is deleted, when to use, warning. Front-loaded with action.
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?
For a single-parameter tool with no output schema, description covers purpose, usage, and behavioral caveats. Could mention return value or error handling, but overall sufficient.
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?
Schema parameter description is basic. Description adds meaning by linking session_id to deletion of all associated memories and scoping to owner, enhancing understanding beyond schema.
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?
Clearly states the verb 'delete', the resource 'every memory (singles, parent docs, child chunks, sha guards)', and the scope 'session_id' and 'owner'. Distinguishes from sibling 'delete_memory' by specifying bulk deletion per session.
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?
Provides explicit use cases: 'wrap up a finished project' or 'reset a polluted session'. Includes a strong warning to confirm session_id. Could improve by explicitly contrasting with single memory deletion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully handles transparency. It discloses the cascading behavior for parent document IDs, which is critical. However, it does not mention permanence or confirmations, though this is acceptable for a simple deletion.
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?
Two concise sentences, no wasted words, front-loaded with the core action.
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?
The description covers the essential behavior for a one-parameter tool with no output schema. It could mention the return value, but this is not critical for an agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the description adds meaning beyond the schema: it explains the ID format (doc:<uuid>) and the cascading effect, which is not in the schema.
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 action ('delete') and resource ('memory'), and distinguishes from siblings like 'delete_memories_by_session' by specifying deletion by ID. It also adds unique behavior (cascading for parent documents).
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 explains when to use the tool (delete by ID) and mentions cascading, but does not explicitly state when not to use or provide alternative tools like 'delete_memories_by_session' for session-based deletion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses the retrieval behavior (chronological order), the error case (returns {status:'not_found'}), and the before/after parameters. It does not mention authentication or rate limits, but for a read-only retrieval tool, this is sufficient.
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?
Three sentences with no wasted words. The first sentence states the core action, the second gives usage context, and the third describes the return behavior. Information is front-loaded and efficiently presented.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately describes the return format and error case. It covers the behavior of the parameters and provides a clear use case. No hidden behaviors remain unexplained.
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?
Schema coverage is 100%, but the description adds value by explaining the turn_id parameter's origin ('from a search_memory result') and contextualizing the before/after defaults within the narrative. This goes beyond the schema alone.
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 retrieves a thread of memories around a turn_id in chronological order, and explicitly distinguishes its use from search_memory by providing a specific use case ('when a search_memory hit lacks enough context').
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 provides explicit when-to-use guidance ('Use this when a search_memory hit lacks enough context') and specifies the input (turn_id from a search_memory result). It does not explicitly list when not to use or alternative tools, but the sibling tools help the agent infer.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![n3mcmcp-lite MCP server](https://glama.ai/mcp/servers/NeuralNexusNote/n3mcmcp-lite/badges/card.svg)](https://glama.ai/mcp/servers/NeuralNexusNote/n3mcmcp-lite)

Score Badge

Copy to your README.md:

[![n3mcmcp-lite MCP server](https://glama.ai/mcp/servers/NeuralNexusNote/n3mcmcp-lite/badges/score.svg)](https://glama.ai/mcp/servers/NeuralNexusNote/n3mcmcp-lite)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

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/NeuralNexusNote/n3mcmcp-lite'

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