MCP server

Valem

io.github.vlad-public-code/valem

Checked 25 Oct 2026, 20:29 UTC · Connected to it and listed its tools

Valem - Deterministic reactive state models. Create your own calculators/forms with AI

Website Source

At a glance

Type

MCP server

Publisher

Not verified

Status

Working at last check

Tools

29

Token cost

About 9,078 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

25 Oct 2026, 20:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http valem https://valem.run/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.valem]
url = "https://valem.run/mcp"

Gemini CLI

{"mcpServers":{"valem":{"httpUrl":"https://valem.run/mcp"}}}

Cursor

{"mcpServers":{"valem":{"url":"https://valem.run/mcp"}}}

VS Code

{"servers":{"valem":{"type":"http","url":"https://valem.run/mcp"}}}

Antigravity

agy mcp add valem https://valem.run/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"valem","server_url":"https://valem.run/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"valem","server_url":"https://valem.run/mcp"}

Tools

• convert_spreadsheet ~300 tokens
  Compile 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 worksheet, one header-row t
• create_model ~813 tokens
  Create 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 postfix (-2, -3, …) is app
• delete_model Deletes data · ~68 tokens
  Remove a model from the registry. Fails (isError) if the model does not exist.
• download_blob ~155 tokens
  Fetch 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.
• dry_run ~721 tokens
  Compile 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.
• eval_expression ~389 tokens
  Evaluate 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 to verify an expressi
• evolve_spec ~1176 tokens
  Apply 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/upsertSchemaDefs, upse
• explain ~192 tokens
  Explain 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 "$constraint:<id>". Trace records can be bulky — pass 'limit' to return only the most recent N.
• get_audit ~314 tokens
  Query a model's durable, append-only audit trail (newest-first): one record per committed reactive cycle (mutations, derivedUpdated, traces, flaggedConstraints, dispatchedEffects, source, sequence). This is the queryable superset of get_history/explain — it survives the in-memory ring buffer rolling
• get_domain_guidance ~718 tokens
  Get vetted instructions for the hard modelling shapes this domain involves (progressive tax/fee charges, per-period schedules, classification, date math, group-by, …). Read the model description in ANY language, pick the matching topic ids, and call this BEFORE authoring the spec — then follow what
• get_effective_schema ~168 tokens
  Get the effective JSON Schema for a field: the static schema overlaid with LIVE meta-derived constraints (current min/max/required/…). Check this BEFORE writing a value to learn what the reactive pipeline will accept, instead of discovering an invalid mutation only by trying it and getting a schema-
• get_field ~109 tokens
  Get the value of a single field by its JSON Path address (e.g. "$.order.total"). Evaluates a LAZY derivation on demand.
• get_history ~68 tokens
  List the ISO-8601 timestamps of a model's committed mutations (most recent 100).
• get_library ~134 tokens
  List 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 the model declares
• get_model_info ~72 tokens
  Get summary info for a model: id, version, and derivation/meta/constraint/effect counts.
• get_spec ~58 tokens
  Get the full stored ModelSpec JSON for a model.
• get_state ~340 tokens
  Get 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' to cap nesting (deep
• get_view ~122 tokens
  Evaluate 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.
• list_models ~45 tokens
  List the ids of all currently registered Valem models (alphabetical).
• mutate ~296 tokens
  Apply 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 structured list of violated
• pair_browser ~286 tokens
  Pair 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_paired", namespaceId
• patch_model ~267 tokens
  Apply 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/0"}]. 'path' fields
• restore ~140 tokens
  Restore 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.
• search_document ~341 tokens
  Deterministic, 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 document length — use th
• snapshot ~119 tokens
  Capture 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.
• test_spec ~720 tokens
  Run 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.
• upload_blob Changes files · ~158 tokens
  Store 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.
• validate_spec ~670 tokens
  Validate 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.
• verify_audit ~119 tokens
  Verify 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.)

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