Persistent memory for AI coding agents. Store coding standards, architecture decisions, and project context across sessions with AES-256 encryption.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 3.4/5 across 8 of 8 tools scored.
Most tools have distinct purposes, but 'query_knowledge' and 'search_knowledge' could cause confusion as both involve searching knowledge, though their descriptions clarify that one uses AI and the other uses keyword matching. Other tools like 'add_knowledge', 'get_topic', and 'list_topics' are clearly differentiated.
All tool names follow a consistent verb_noun pattern with snake_case, such as 'add_knowledge', 'capture_session', 'get_topic', 'list_sessions', 'list_topics', 'query_knowledge', 'recall_memory', and 'search_knowledge'. This uniformity makes the tool set predictable and easy to understand.
With 8 tools, the count is well-scoped for a knowledge and memory management server. It covers core operations like adding, retrieving, listing, and searching knowledge and sessions, without being overly complex or too sparse for the domain.
The tool set provides good coverage for knowledge base and session memory management, including CRUD-like operations (add, get, list, search). A minor gap is the lack of update or delete tools for knowledge or sessions, which agents might need to work around, but core workflows are supported.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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
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.
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