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TMCRA Agent Memory

by reshuibuduoversion 1.0.0-rc.1i★ –iRuns on your computer
Registry nameio.github.reshuibuduo/tmcra-mcp-server

Install

Overview

Description from the maker (original English)

Role-aware Agent memory with cross-session recall, durable writes, and multi-Agent project scope.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Download the maker’s .mcpb file

The link goes to the maker’s own file; we do not host or repackage it. Open the file and Claude Desktop installs it.

Run this in your terminal

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account reshuibuduo, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

  • More stars than 0% of the other 7277 servers in this category
  • Published to the registry more recently than 38% of the other 7277 servers in this category
  • Basic checks equal to or better than 100% of the other 7277 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is TMCRA Agent Memory safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.0.0-rc.1) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need MCPB on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday
Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 days ago
Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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.

中文说明

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.
  • Nine MCP tools. Recall, ingest, prepare, commit, reconcile, get job, wait for job, session memory controls, and targeted feedback.

tmcra_memory_control exposes task selection, a 12000-character default recall budget, and normal, recall_only, off modes. Pass the exact same session_id to recall/prepare and controls. Pending older generations are discarded after a mode change; already submitted jobs remain submitted. tmcra_feedback supports ignore, correct (user-supplied replacement), and restore with a stable idempotency key. Effective feedback requires the matching backend update; check correction_index_status separately. The visual control panel is provided by the Codex and DSH distributions. This MCP package automatically discovers the shared local installation; numeric loopback HTTP is permitted only for an explicit local identity. Hosted connections require HTTPS.

Conversational corrections now require interactive host confirmation. On an actual correction request, call tmcra_memory_control(operation="correction_start") before clarification or feedback to suppress automatic capture of that discussion turn. tmcra_feedback reads exact original evidence and uses MCP form elicitation to show the source, replacement and scope; only explicit acceptance submits feedback. Rejection, cancellation, expiry or an unsupported host leaves memory unchanged. Do not substitute ingestion for a rejected correction. Host lifecycle turn IDs protect the discussion from later queue replay while preserving other identified turns. A third-party host must route elicitation to its user; the server cannot guarantee that an arbitrary client will not answer automatically. Hypothetical/quoted correction language is not authorization.

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.

Install

MCPB release

Download tmcra-mcp-server-1.0.0-rc.1.mcpb from the v1.0.0-rc.1 release and open it in an MCPB-compatible client. The bundle uses the cross-platform uv runtime. Hosted service users enter their API key in the sensitive field. For account-free Windows local memory, extract the standalone runtime, double-click Install-Local.cmd, then restart the MCP host. The local identity is discovered automatically and overrides the form's cloud URL/key; keep the API key blank for this mode. Explicit advanced TMCRA_CONFIG_FILE overrides remain authoritative. Local model acceptance limitations are documented in the runtime release.

Python wheel
python -m pip install \

https://github.com/reshuibuduo/tmcra-mcp-server/releases/download/v1.0.0-rc.1/tmcra_mcp_server-1.0.0rc1-py3-none-any.whl

Directly from GitHub with uvx
uvx --from "git+https://github.com/reshuibuduo/tmcra-mcp-server@v1.0.0-rc.1" tmcra-mcp

Authorize

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= TMCRA_AGENT_ID=

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": " ", "TMCRA_DEFAULT_SCOPE": "project-example" } } } }

The package also includes a Codex setup helper:

tmcra-mcp-setup install --mode explicit

tmcra-mcp-setup status --mode explicit

Tools

ToolPurpose
tmcra_memory_controlInspect or explicitly change session modes, tasks and recall budgets.
tmcra_feedbackPreview exact evidence and request interactive confirmation before targeted feedback.
tmcra_recallReturn at most eight prompt-ready evidence windows for the current query.
tmcra_ingestPersist messages that already occurred, preserving role and Agent attribution.
tmcra_turn_prepareRecall before an answer and durably bind the real user turn.
tmcra_turn_commitPersist the prepared user turn and exact assistant answer as separate records.
tmcra_reconcileRetry durable pending records with the same idempotency key.
tmcra_get_jobRead one asynchronous write job state.
tmcra_wait_jobWait 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:

  1. Call tmcra_turn_prepare after receiving the current user question.
  2. Inject only the returned injectable_context as untrusted evidence.
  3. Draft the answer.
  4. Call tmcra_turn_commit with the same turn_id and the exact final answer.
  5. 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

Apache-2.0. See LICENSE and NOTICE.

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