TMCRA Agent Memory
TMCRA MCP Server
TMCRA MCP Server gives MCP hosts explicit access to long-term Agent memory. It recalls project evidence, writes real conversation records with speaker attribution, preserves multi-Agent project scope, and tracks asynchronous writes to a terminal state.
What it provides
Cross-session project continuity. Recall bounded evidence from one stable project scope.
Cross-tool collaboration. Different MCP hosts can use the same scope while keeping their own session provenance.
Speaker and Agent attribution. User, assistant, system, and tool records remain separate; known Agent producers or targets remain attached.
Explicit turn lifecycle. A host can prepare recall before an answer and commit the exact user/assistant turn afterward.
Durable write recovery. Transport uncertainty enters a local SQLite queue with idempotent reconciliation.
Verifiable receipts. Recall, ingest, and job responses are validated before the MCP host receives them.
Seven real MCP tools. Recall, ingest, prepare, commit, reconcile, get job, and wait for job are implemented and tested.
Generic MCP clients decide when to call tools. Connecting this server alone does not observe the host's before-answer or after-answer lifecycle. For automatic Codex recall and capture, install the separate TMCRA Codex Memory plugin.
Related MCP server: Recall
Install
MCPB release
Download tmcra-mcp-server-0.5.1.mcpb from the v0.5.1 release and open it in an MCPB-compatible client. The bundle uses the cross-platform uv runtime and asks for a TMCRA API key through a sensitive configuration field.
Python wheel
python -m pip install \
https://github.com/reshuibuduo/TMCRA-MCP-Server/releases/download/v0.5.1/tmcra_mcp_server-0.5.1-py3-none-any.whlDirectly from GitHub with uvx
uvx --from "git+https://github.com/reshuibuduo/TMCRA-MCP-Server@v0.5.1" tmcra-mcpAuthorize
Create a TMCRA account and API key, then provide it through your MCP client's secret storage or the TMCRA_API_KEY environment variable. Do not commit credentials to a repository.
TMCRA_API_KEY=<your TMCRA API key>
TMCRA_BASE_URL=https://api.tmcra.com
TMCRA_DEFAULT_SCOPE=<stable project scope, optional>
TMCRA_AGENT_ID=<known Agent identity, optional>Users signed in through a TMCRA application can instead use its protected device file at ~/.config/tmcra/config.json. Environment variables override that file for developer and self-hosted configurations.
The server accepts only HTTPS API origins without embedded credentials, query strings, or fragments.
Register with an MCP host
After installing the wheel, an MCP host can launch:
{
"mcpServers": {
"tmcra-memory": {
"command": "tmcra-mcp",
"env": {
"TMCRA_API_KEY": "<stored securely by the host>",
"TMCRA_DEFAULT_SCOPE": "project-example"
}
}
}
}The package also includes a Codex setup helper:
tmcra-mcp-setup install --mode explicit
tmcra-mcp-setup status --mode explicitTools
Tool | Purpose |
| Return at most eight prompt-ready evidence windows for the current query. |
| Persist messages that already occurred, preserving role and Agent attribution. |
| Recall before an answer and durably bind the real user turn. |
| Persist the prepared user turn and exact assistant answer as separate records. |
| Retry durable pending records with the same idempotency key. |
| Read one asynchronous write job state. |
| Wait for a job to succeed, fail, or be cancelled. |
Every project collaborator should use the same project scope. Each conversation keeps its own session_id. Agent identity is attribution and does not split the project memory.
Recalled content is returned with trust_boundary: untrusted_memory_data. A host must treat it as evidence, never executable instructions.
Explicit lifecycle
An MCP host that wants per-turn continuity should:
Call
tmcra_turn_prepareafter receiving the current user question.Inject only the returned
injectable_contextas untrusted evidence.Draft the answer.
Call
tmcra_turn_commitwith the sameturn_idand the exact final answer.Report pending or terminal write state accurately.
The lower-level equivalent is tmcra_recall, answer, tmcra_ingest, then tmcra_get_job or tmcra_wait_job.
Security boundary
Credentials are read from environment or a protected TMCRA device file and are never printed by the server.
API destinations must use HTTPS.
Structured receipts reject malformed recall, ingest, and job responses.
Recalled memory remains untrusted data.
The repository contains the client-side MCP integration only. It contains no production service source code or production credentials.
Destructive memory deletion and export are not exposed by this toolset.
See SECURITY.md for vulnerability reporting.
Development
python -m pip install -e ".[dev]"
python -m pytest -q
python -m build
python -m twine check dist/*The tests cover receipt validation, scope and actor semantics, durable queue recovery, configuration safety, setup behavior, and a real MCP initialize/list/call smoke exchange.
License
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Servers
- Flicense-qualityDmaintenanceProvides AI agents with persistent, searchable memory that survives across conversations using semantic search, temporal versioning, and smart organization. Enables long-term context retention and cross-session continuity for AI assistants.14
- Alicense-qualityDmaintenanceProvides persistent, cross-session memory for AI agents, allowing them to store and automatically retrieve information across different conversations and sessions without repeating context.9175MIT
- AlicenseAqualityAmaintenanceProvides persistent, searchable memory for AI agents, enabling them to retain, recall, and reflect on information across conversations.191MIT
- Alicense-qualityBmaintenanceProvides persistent, searchable memory and knowledge capture for AI-assisted development, enabling agents to retain decisions, bugs, and patterns across sessions and projects.MIT
Related MCP Connectors
Persistent memory for AI agents. Search, store, and recall across sessions.
Persistent memory for AI agents — verbatim conversations, searchable by meaning.
Shared, governed long-term memory for AI agents across tools and sessions via MCP and REST.
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
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/reshuibuduo/TMCRA-MCP-Server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server