mifactory-agent-memory
Server Details
Persistent memory for AI agents across Claude, ChatGPT and any MCP client.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- mifactory-bot/agent-memory-api
- GitHub Stars
- 0
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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.
Tool Definition Quality
Average 2.4/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one reads memory, one writes memory. There is no ambiguity between them.
Both tools follow a consistent verb_noun pattern (memory_read, memory_write), making the naming predictable and clear.
With only two tools, the server feels minimal. While appropriate for a simple memory store, it is on the borderline of being too thin for a full-featured toolset.
The server provides read and write operations but lacks delete or clear functionality, which is a notable gap in memory lifecycle management. Agents cannot remove specific memories or reset the memory store.
Available Tools
2 toolsmemory_readCInspect
Read from agent memory
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | ||
| agentId | Yes |
Tool Definition Quality
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 only states 'Read', implying a non-mutating operation, but says nothing about return behavior, error handling, or what happens if the key does not exist. The minimal disclosure leaves significant ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short and front-loaded, which is efficient, but it is under-specified. It conveys the core action in a single sentence, yet omits essential context, making it minimally acceptable rather than well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has two required parameters, no annotations, and no output schema, the description is insufficient. It fails to provide parameter semantics, return value expectations, or edge case behavior, leaving the agent without enough information to use the tool reliably.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain any of the two parameters (agentId, key). The description adds no meaning beyond the schema's bare field names, failing to compensate for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Read') and the resource ('agent memory'), making the primary purpose understandable. However, it does not explicitly differentiate from the sibling tool memory_write, though the verb 'Read' implicitly contrasts with 'Write'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 memory_write or any other alternative. It does not mention prerequisites, context, or scenarios where reading from agent memory is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
memory_writeCInspect
Write to agent memory
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | ||
| value | Yes | ||
| agentId | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description carries the full burden. It only states 'write', implying mutation, but fails to disclose whether existing keys are overwritten, if the write is persistent, any permission requirements, or other side effects. This is a significant transparency gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence with no redundancy, which is structurally concise. However, the extreme brevity borders on under-specification, and the sentence could be somewhat more explanatory without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 required parameters, no output schema, and no annotations, the description is insufficient. It fails to explain parameter semantics, return values, error conditions, or behavioral details, making it incomplete for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides no meaning for agentId, key, or value. The agent is left to guess the role and format of each parameter, so the description adds no value beyond the bare property names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Write to agent memory' clearly identifies the action (write) and resource (agent memory), and naturally contrasts with the sibling tool memory_read. It is specific enough to distinguish the tool's purpose, though the term 'agent memory' is slightly generic.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus memory_read or any other alternatives. The description does not mention prerequisites, typical use cases, or exclusions, leaving the agent to infer usage from the tool name alone.
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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{
"$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.
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