A
licenseC
qualityC
maintenancePersistent semantic memory for MCP-compatible agents, enabling them to remember and recall text, audio, and documents across sessions.
10
211
MIT
DeepMem
Server Details
Mem0-compatible persistent memory for AI agents: write facts once, recall them semantically.
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- deepmemteam/deepmem
- GitHub Stars
- 113
- Server Listing
- deepmem
Available Tools
2 toolsdeepmem_searchAInspect
Search memories stored in DeepMemory using semantic search. Returns the most relevant memories for the given query, ranked by hybrid scoring (vector similarity + BM25 keyword match + entity boost + time decay).
Use this to retrieve context from past conversations before responding to the user. Memories are scoped to the user_id provided during write.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| top_k | No | ||
| api_key | No | ||
| user_id | No | default | |
| threshold | No |
TDQS
A4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses the hybrid scoring mechanism (vector similarity, BM25, entity boost, time decay) and scoping behavior, which goes beyond the bare schema. It does not explicitly state read-only nature or discuss rate limits, but the read behavior is implied and the scoring detail is valuable.
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, zero fluff, front-loaded with the primary action. Each sentence adds value: purpose, scoring, usage context, and scoping. Efficient and well-structured.
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 covers purpose, scoring, usage, and scoping. However, with no output schema, it does not specify the return format (e.g., list of memory IDs, scores, text). Given 5 parameters and no annotations, it is decent but not fully complete; it could mention the return structure to aid the agent. It is adequate for a search tool but leaves some ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must explain parameters. It implicitly covers 'query' (given query) and 'user_id' (scoping), but does not explain 'top_k', 'threshold', or 'api_key'. The parameter names are self-explanatory, but the description does not add meaningful detail beyond the schema's defaults, leaving the agent to guess on threshold semantics.
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 searches memories with semantic search and returns ranked results. It distinguishes from sibling 'deepmem_write' by focusing on retrieval rather than storage, and explicitly says to use it for context from past conversations.
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 usage guidance: 'Use this to retrieve context from past conversations before responding to the user.' It also notes memory scoping to user_id, implying the need to pass the correct user_id. However, it does not mention when not to use it or alternative approaches, but the sibling-tool context makes the primary use case clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
deepmem_writeAInspect
Write conversation messages to DeepMemory for fact extraction and persistent storage. Messages are processed by an LLM to extract structured memories, which are then embedded and stored in a vector database for later semantic search.
Set infer=True to enable LLM fact extraction (produces richer memories but costs one LLM call). Set infer=False to store raw messages without extraction.
Returns a list of memory IDs for successfully stored facts.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| infer | No | ||
| run_id | No | ||
| api_key | No | ||
| user_id | No | default | |
| agent_id | No | ||
| messages | Yes |
TDQS
A3.9/5.0
Behavior4/5
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 reveals the LLM processing pipeline, the optional infer mode with its cost, persistent storage, and the return of memory IDs. It does not cover permissions or potential side effects, but it offers substantial context beyond a bare write operation.
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 concise and well-structured, with two short paragraphs. It front-loads the core purpose, then details the key option, and closes with return value. Every sentence adds value with no fluff.
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 tool's moderate complexity, the description effectively covers the main operation, the infer distinction, and the return list of IDs. It lacks details on authentication (api_key) and scoping (user_id, agent_id), which are present in the schema but not explained, making it mostly complete but not fully comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
While the description explains the infer parameter in detail, the schema has 6 parameters and schema description coverage is 0%. The description provides no meaning for run_id, api_key, user_id, agent_id, or messages beyond the overall context. It compensates only for infer, leaving the other five parameters unexplained, which is insufficient for low coverage.
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 writes conversation messages to DeepMemory for fact extraction and persistent storage, using a specific verb and resource. It distinguishes itself from the sibling deepmem_search by focusing on the write/storage operation rather than retrieval.
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 explains the core behavior and the infer parameter trade-off but does not explicitly state when to prefer this tool over deepmem_search or any other alternative. Usage is implied rather than directly stated, and no exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
2 tool updates
- First observed
deepmem_search - First observed
deepmem_write
Frequently Asked Questions
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as
io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such asio.github.acme/serveralso needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at
/.well-known/glama.jsonon the same origin as the connector, then choose Check HTTP challenge.DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Connectors
Persistent memory for AI agents. Search and store durable facts, preferences and decisions.
Persistent memory for AI agents. Search, store, and recall across sessions.
Persistent memory for AI agents. Semantic search, memory graph, W3C DID identity.
Memory system for AI agents with semantic search. Store and recall memories with ease.
Related MCP Servers
Engramofficial
AlicenseNot gradedqualityBmaintenancePersistent memory for AI agents. Stores verbatim conversation transcripts and makes them searchable by meaning via semantic embeddings.2111MIT- AlicenseNot gradedqualityAmaintenanceOpen-source persistent memory infrastructure for AI agents.150321Apache 2.0
- AlicenseAqualityDmaintenanceProvides persistent memory with semantic search for MCP-based AI agents, enabling them to store and recall information across sessions using vector embeddings.41MIT
Try in Browser
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
A4.1/5.0
Disambiguation5/5
The two tools have completely distinct purposes: one searches existing memories, the other writes new ones. No overlap or ambiguity exists between them.
Naming Consistency5/5
Both tools follow a consistent pattern: 'deepmem_' prefix plus a verb (search, write). This is uniform and predictable.
Tool Count3/5
With only 2 tools, the server is minimal but covers its core purpose of memory storage and retrieval. It feels slightly sparse but is not inappropriate for a focused memory server.
Completeness4/5
The server covers the essential lifecycle of memories: writing and searching. However, there is no explicit delete or update operation, which are minor gaps for a persistence layer.