Agent-Native Calendar
Agent-Native Google Calendar - manage events, sync, and public booking
คำอธิบายจากผู้ทำ (ภาษาอังกฤษตามต้นฉบับ)
Valem - Deterministic reactive state models. Create your own calculators/forms with AI
สามเรื่องนี้ไม่รวมเป็นคะแนนเดียว เพราะความเห็น การวัด และความนิยม เป็นคนละเรื่องกัน
ตัวนี้ยังไม่ได้เรียบเรียงเป็นภาษาไทย ด้านล่างคือข้อเท็จจริงที่เราอ่านจาก registry
เลือกโปรแกรม AI ที่คุณใช้ แล้วคัดลอกค่าตั้งไปวาง
ต้องเตรียมก่อน
คุณใช้แอปไหน
เลือกแอปด้านบน เราจำไว้ให้ ครั้งหน้าปุ่มจะขึ้นว่า “ติดตั้งลง …”
ใช้ได้ทั้ง Claude บนเว็บ แอปคอมพิวเตอร์ และมือถือ:
https://valem.run/mcpพิมพ์คำสั่งนี้ใน Terminal
claude mcp add --transport http valem https://valem.run/mcpวางในไฟล์ .cursor/mcp.json ของโปรเจกต์ หรือ ~/.cursor/mcp.json
{
"mcpServers": {
"valem": {
"url": "https://valem.run/mcp"
}
}
}เบราว์เซอร์จะถามก่อนเปิดแอป จากนั้นแอปแสดงค่าตั้งให้ตรวจแล้วกดยืนยัน ช่อง key เว้นว่างไว้ให้คุณใส่เอง
วางในไฟล์ .vscode/mcp.json ของโปรเจกต์
{
"servers": {
"valem": {
"type": "http",
"url": "https://valem.run/mcp"
}
}
}เบราว์เซอร์จะถามก่อนเปิดแอป จากนั้นแอปแสดงค่าตั้งให้ตรวจแล้วกดยืนยัน ช่อง key เว้นว่างไว้ให้คุณใส่เอง
ChatGPT ต่อได้เฉพาะ server ที่อยู่บนอินเทอร์เน็ต:
https://valem.run/mcpชื่อเมนูอาจต่างกันเล็กน้อยตามแพ็กเกจและรุ่นของแอป
ตัวเลือกเก็บในคุกกี้บนเครื่องนี้ ไม่ต้องมีบัญชี
ค่าตั้งนี้สร้างจากข้อมูลใน registry ของผู้ทำ ถ้าไม่ตรงกับ README ให้เชื่อ README
ชื่อและคำอธิบายเครื่องมือตามที่ server ตอบมาเมื่อ 10 พ.ย. 2569 ผู้ทำเป็นคนเขียน ไม่ใช่เรา แสดงเป็นข้อความล้วน
list_modelsList the ids of all currently registered Valem models (alphabetical).
create_modelCreate a new model from a declarative ModelSpec. The spec carries the JSON schema plus derivations (computed fields), constraints (invariants), and optional effects. Returns the created id. Fails (isError) on an invalid spec. If the requested id is already taken, a numeric postfi
get_model_infoGet summary info for a model: id, version, and derivation/meta/constraint/effect counts.
get_specGet the full stored ModelSpec JSON for a model.
get_libraryList the JSONata functions and values a model's library exports — what an expression in this model may call, with signature, arity and originating layer. Call it before writing an expression so you reuse the model's existing vocabulary instead of re-deriving it. Returns null when
get_stateGet a model's merged state (base fields plus all computed derived fields). On a large model this is the biggest context cost — narrow it: pass 'paths' to project only specific subtrees (canonical addresses, each spliced back into a pruned document at its address), and/or 'depth'
get_fieldGet the value of a single field by its JSON Path address (e.g. "$.order.total"). Evaluates a LAZY derivation on demand.
mutateApply field mutations to a model and run the reactive pipeline (derivations recompute, constraints enforce, effects dispatch). 'mutations' is a flat map keyed by canonical JSON Path address, e.g. {"$.order.qty": 3}. A ROLLBACK constraint violation returns isError with the structu
patch_modelApply an RFC 6902 JSON Patch document to a model and run the reactive pipeline. Unlike 'mutate' (a flat address→value map), a patch expresses array insert/remove/move and test/copy ops, e.g. [{"op":"add","path":"/order/items/-","value":{...}}, {"op":"remove","path":"/order/items/
get_effective_schemaGet the effective JSON Schema for a field: the static schema overlaid with LIVE meta-derived constraints (current min/max/required/…). Check this BEFORE writing a value to learn what the reactive pipeline will accept, instead of discovering an invalid mutation only by trying it a
snapshotCapture an immutable point-in-time snapshot of a model's state (base document + derived/meta caches). A natural safety step before a risky evolve_spec: keep the returned snapshot and, if the change goes wrong, hand it back to 'restore' to roll the state back.
restoreRestore a model's state from a snapshot previously returned by 'snapshot' (pass it back verbatim as 'snapshot'). Overwrites the model's current base state. The snapshot must be for the same model.
explainExplain why a field is what it is: returns the recent derivation/constraint trace records for a path from the in-memory ring buffer (inputs, expression, result). For a constraint use the synthetic path "$constraint: ". Trace records can be bulky — pass 'limit' to return only the
get_historyList the ISO-8601 timestamps of a model's committed mutations (most recent 100).
get_auditQuery a model's durable, append-only audit trail (newest-first): one record per committed reactive cycle (mutations, derivedUpdated, traces, flaggedConstraints, dispatchedEffects, source, sequence). This is the queryable superset of get_history/explain — it survives the in-memory
verify_auditVerify the tamper-evidence hash chain of a model's durable audit trail. Returns {valid, recordsChecked, firstBrokenSequence, detail}; a false 'valid' points at the first altered/reordered/deleted record. (Embedded mode has no hash chain and reports valid.)
upload_blobStore binary content (base64-encoded in 'data') in the content-addressed blob store and get back a BlobRef {$blobId, $mediaType, $bytes} to embed in a model's binary field. Storage is content-addressed (SHA-256), so uploading identical bytes returns the same $blobId.
download_blobFetch a blob's bytes by id, base64-encoded in the result. Pass 'modelId' to fetch a blob referenced by a specific model (access-scoped); omit it for a direct store fetch. Large blobs may exceed the result-size limit — this channel suits small binaries only.
evolve_specApply an incremental SpecEvolution diff to a model, preserving live state. Returns the new version. Fails (isError) if the evolved spec is invalid, if expectedVersion no longer matches, or if a schema change would strand existing state. Prefer targeted diffs (upsertSchemaNodes/up
delete_modelRemove a model from the registry. Fails (isError) if the model does not exist.
get_viewEvaluate a model's embedded view definition against current state and return the resolved component tree. Pass an optional 'viewId' for a named view; omit for the default view.
get_domain_guidanceGet vetted instructions for the hard modelling shapes this domain involves (progressive tax/fee charges, per-period schedules, classification, date math, group-by, …). Read the model description in ANY language, pick the matching topic ids, and call this BEFORE authoring the spec
validate_specValidate a ModelSpec WITHOUT creating it: returns a 'valid' flag plus structured findings (errors + warnings, each with a location and message). Use this to iterate on a draft — fix the reported errors, re-validate — before committing with create_model.
eval_expressionEvaluate a single JSONata expression against a sample input document and return the computed value, or the exact compile/eval error. Write the expr exactly as in a derivation/constraint 'expr': bare dot-paths, no leading $ (e.g. "loan.amount * loan.annualRate / 1200"). Use this t
test_specRun a spec's embedded test cases (or ad-hoc given->expect cases) through the real reactive pipeline in a throwaway runtime, returning pass/fail plus per-field failures (path, expected, actual). Use this to certify domain behavior before create_model / promotion.
dry_runCompile a candidate ModelSpec in an ISOLATED throwaway runtime, apply optional sample mutations, and return the resulting merged state (base + derived) — WITHOUT registering it in the live registry. Use this to preview the full reactive cascade of a draft spec.
convert_spreadsheetCompile an uploaded .xlsx workbook's OWN formulas into a ready ModelSpec — deterministic transpilation (Excel formula language -> JSONata), never an LLM call: every number the compiled spec computes is checked to exactly match what the source workbook itself computed. One workshe
search_documentDeterministic, LLM-free search over an uploaded PDF/DOCX: ranks the document's pages/sections against your query (term-overlap, no embedding model, no network call) and returns the top matches with page/section citations and verbatim quotes. Cost is bounded by topK, never by docu
pair_browserPair this MCP session with a browser tab on the hosted Valem sandbox so both drive the same live model session. Mints a pairing on first call (or resumes an existing not-yet-approved one) and waits up to a minute for the developer to approve it. Returns {status:"paired"|"already_
valem_spec_guideHow to write a Valem ModelSpec that render_model accepts: sections, path notation, JSONata rules, view components and common mistakes. Call this once before writing your first spec in a conversation.
render_modelShow the user a Valem model as an interactive card they can edit in the chat: a form whose computed fields update on every change, checked by a deterministic engine. Use it when the user asks for a calculator, quote, budget, planner, pricing or any model with inputs and computed
explain_valueExplain how a value in a Valem card was computed: the formula, the values it read and the inputs it ultimately comes from, as Valem's engine has them for the given inputs. Use it when the user asks why a number is what it is. Pass the specHash and current inputs from the latest r
valem_recomputeCalled by the Valem card when the user edits an input. Not for direct use: call render_model instead.
show_modelShow the user one of this server's models (created with create_model) as an interactive card in the chat. Every edit the user makes in the card is a mutate on the model, so get_state and explain see it. Use it after create_model or evolve_spec when the user should see or try the
valem_card_editCalled by the Valem card when the user edits a stored model. Not for direct use: call mutate instead.
เราตรวจจากข้อมูลที่ใครก็เปิดดูได้ ยังไม่ได้อ่านโค้ด ข้อที่ไม่เกี่ยวกับตัวนี้ไม่นับในตัวหาร · ตรวจเมื่อ 10 พ.ย. 2569
ชื่อใน registry ยืนยันด้วยบัญชี GitHub vlad-public-code ซึ่งเป็นเจ้าของ repo
ยังไม่ได้อ่านข้อมูล repo จาก GitHub
ยังไม่ได้อ่านข้อมูล repo จาก GitHub
ตอบกลับใน 178 ms (HTTP 200)
ไม่ต้องล็อกอิน
อ่านคำอธิบายเครื่องมือ 35 ตัวแล้ว ไม่พบคำสั่งแอบแฝงถึง AI
เทียบกับทุก server ที่ยังใช้งานในหมวดนี้ทั้งทะเบียน ดาวนับเฉพาะเมื่อผู้ลงทะเบียนเป็นเจ้าของ repo
จากการตรวจเบื้องต้นของเรา ผ่าน 3 จาก 3 ข้อที่ตรวจได้ เรายังไม่ได้อ่านโค้ด ก่อนใส่กุญแจหรือ token ให้ดูว่า server ขอสิทธิ์อะไร และเลือกกุญแจที่ทำได้เท่าที่จำเป็น
registry ไม่ได้ระบุว่าต้องใช้กุญแจ แต่บางตัวจะขอให้ล็อกอินตอนใช้ครั้งแรก
รันบนคลาวด์ของผู้ทำ ต่อผ่านอินเทอร์เน็ตได้เลย ไม่ต้องลงโปรแกรม แต่ข้อมูลที่ส่งให้จะผ่านเซิร์ฟเวอร์ของผู้ทำ
repo บน GitHub มีการแก้โค้ดล่าสุด – และส่งรุ่นล่าสุด (1.0.0) เข้า registry 3 เดือนที่ผ่านมา
โปรแกรมที่รองรับ MCP เช่น Claude Desktop, Claude Code, Cursor และ VS Code ตัวนี้ต่อผ่าน URL จึงใช้กับโปรแกรมที่รับ remote MCP ได้ เช่น ChatGPT ที่เปิดโหมดนักพัฒนา
ในหมวดเดียวกันมี Agent-Native Calendar, Excel MCP Server, MCP Server for Excel
server หมวดเดียวกันจากผู้ทำคนอื่น เรียงตามดาว
อ่านการ์ด:↓ ดาวน์โหลดต่อสัปดาห์i★ ดาว GitHubiสามเสา: รีวิว · ผลตรวจ · การใช้งานiอัปเดต = วันที่ส่งรุ่นล่าสุดเข้า registryi
Agent-Native Google Calendar - manage events, sync, and public booking
ให้ AI สร้างและแก้ไฟล์ Excel ได้เอง ทั้งสูตร กราฟ และ PivotTable โดยไม่ต้องมีโปรแกรม Excel ในเครื่อง
Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.
ส่วนนี้คัดจาก repo ของผู้ทำตามต้นฉบับ PopNx MCP ไม่ได้ตรวจหรือรับรองเนื้อหา ตัด HTML รูปภาพ และสคริปต์ออกแล้ว ลิงก์จะพาออกนอกเว็บ
A spreadsheet engine for AI agents. An LLM describes a domain as a JSON spec (fields, formulas, invariants); Valem compiles it into a live model that recomputes every dependent value on each change, refuses changes that break a rule, and can explain why any number is what it is.
LLMs are good at *describing* a domain and bad at *keeping state consistent* over it. Ask one to maintain a quote, an order, or a loan application across twenty turns and the total drifts from its line items, a limit checked three messages ago is forgotten, and arithmetic is off by a rounding step.
Valem moves the rules out of the conversation and into a deterministic runtime:
The agent writes the spec; Valem validates it, tests it, and keeps it true.
In the browser, no install. Open valem.run, describe a model in plain words ("a mortgage with overpayments", "an invoice with VAT and discounts"), then type into the live form and watch every derived value update.
From your agent (MCP, hosted, nothing to download):
# Claude Code
claude mcp add --transport http valem https://valem.run/mcp
Claude Desktop: Settings → Connectors → *Add custom connector* → https://valem.run/mcp. Then ask the agent to call pair_browser and it will build models you see live in a browser tab. The authoring tools (validate_spec, eval_expression, test_spec, dry_run) work without pairing. Guide: Connect your agent.
Self-hosted with Docker:
docker run --rm -p 8080:8080 ghcr.io/vlad-public-code/valem:latest
{
"id": "order", "version": "1.0.0", "schema": {}, "derivations": [ { "path": "$.order.items[*].lineTotal", "expr": "$parent.qty * $parent.price" }, { "path": "$.order.total", "expr": "$sum(order.items.(qty * price))" } ], "constraints": [ { "id": "max-order", "expr": "order.total <= 5000", "message": "Order exceeds the cap", "policy": "rollback" } ] }
Write qty on any line item and lineTotal and total recompute; a write that pushes the total over 5,000 is rejected before it commits. Full format: ModelSpec reference.
| You want | Use |
|---|---|
| An agent that maintains structured state | The MCP server, hosted (valem.run/mcp) or self-hosted (stdio jar from Releases) |
| A backend shared by many clients | valem-web: REST + WebSocket, pluggable storage (filesystem, Postgres, Mongo, Redis, S3) |
| The engine inside your JVM app | Embed valem-core/valem-service |
| Scripting with no server | The JSON console (one JSON command per line on stdin) |
| A live model inside your web page | Embed a live model |
Clients: TypeScript and Java SDKs.
Published at [valem.run/docs](https://valem.run/docs/) (source in docs/), organised in six chapters: Getting started, Usage scenarios, Model guide, Reference, Deployment, Extending. Good first reads:
valem.* propertyThe console app is the fastest way to use Valem from a script or an AI agent. It reads one JSON command per line from stdin and writes one JSON response per line to stdout. No HTTP, no browser, no server process. All state is held in memory for the lifetime of the process.
# Build the fat jar (first time)
mvn install -pl valem-core,valem-service -q mvn package -pl valem-console -q
java -jar valem-console/target/valem-console-1.0.0-SNAPSHOT.jar
echo '{"cmd":"list-models"}' | java -jar valem-console/target/valem-console-1.0.0-SNAPSHOT.jar
Example session:
// stdin
{"cmd":"create-model","spec":{"id":"order","version":"1.0.0","schema":{},"derivations":[{"path":"$.total","expr":"subtotal + tax"}]}} {"cmd":"mutate","id":"order","mutations":{"$.subtotal":100,"$.tax":8}} {"cmd":"get-state","id":"order"}
// stdout {"ok":true,"result":{"id":"order","status":"created"}} {"ok":true,"result":{"success":true,"mutatedPaths":["$.subtotal","$.tax"],"derivedUpdated":["$.total"],...}} {"ok":true,"result":{"subtotal":100,"tax":8,"total":108}}
Full command list: console JSON protocol.
# From the repo root — build and start the Spring Boot server on port 8080.
mvn install -pl valem-core,valem-service -q mvn spring-boot:run -pl valem-web
The API is now available at http://localhost:8080, with the management UI served at /. Storage is in-memory by default; other backends are à-la-carte adapter jars (mvn -Pweb-postgres -pl valem-web package, then --valem.storage.type=postgres) — see configuration.md. For durable setups, persistence layout, and the hardening checklist see operations.md.
To enable LLM-powered spec generation, configure a provider before starting (the key is read from valem.llm.api-key, settable as VALEM_LLM_API_KEY; there is no provider-specific env fallback):
# Anthropic (default provider)
export VALEM_LLM_API_KEY=sk-ant-... mvn spring-boot:run -pl valem-web
VALEM_LLM_PROVIDER=openai VALEM_LLM_MODEL=gpt-4o \ VALEM_LLM_API_KEY=$OPENAI_API_KEY mvn spring-boot:run -pl valem-web
ollama serve first)VALEM_LLM_PROVIDER=ollama VALEM_LLM_MODEL=llama3 mvn spring-boot:run -pl valem-web
Without any provider configured the server starts normally; the /models/generate* endpoints return 503. All LLM knobs (providers, tool budgets, retries, temperatures): configuration.md.
In a separate terminal:
cd valem-ui
npm install # first time only npm run dev
Open http://localhost:5173 in your browser.
The UI proxies all /models and /blobs requests (including WebSocket connections) to the backend, which must be running.
The UI's ✦ Generate button drives a human-in-the-loop workflow: enter a model ID and a plain-text domain description → Preview Prompt (editable) → send to the LLM → review/edit the generated spec → Register Model. The same workflow is available over REST (POST /models/generate/preview → /models/generate → /models).
See generating-specs-with-llm.md for the workflow and provider setup, and llm-prompts.md for the exact prompts and the validate-and-repair loop.
# Create a model
curl -s -X POST http://localhost:8080/models \ -H 'Content-Type: application/json' \ -d '{ "id": "order", "version": "1.0.0", "schema": {}, "derivations": [ { "path": "$.order.total", "expr": "order.subtotal + order.tax" } ], "constraints": [ { "id": "max-order", "expr": "order.total <= 5000", "message": "Order exceeds the cap", "policy": "rollback" } ] }'
curl -s -X POST http://localhost:8080/models/order/mutations \ -H 'Content-Type: application/json' \ -d '{ "$.order.subtotal": 200, "$.order.tax": 20 }'
curl -s http://localhost:8080/models/order/state | python -m json.tool
Every endpoint (audit, snapshots, views, blobs, spec evolution, composition, …): api-reference.md.
# All modules
mvn test
mvn test -pl valem-core
The valem-e2e module contains Playwright browser tests that drive the full stack (backend + UI).
Prerequisites: the backend must be running on port 8080 (see Running the backend). The UI dev server is started automatically by Playwright.
cd valem-e2e
npm install # first time only npx playwright install # download browser binaries (first time only)
npm test # headless Chromium npm run test:headed # watch the browser npm run test:ui # Playwright interactive UI mode npm run report # open the last HTML report
Apache-2.0 — see LICENSE.