What can you do with this server?

A free, no-API-key stdio MCP server exposing three tools: two fully offline linters for Sparkplug B topics and UNS naming, plus a networked industrial AI-readiness diagnostic. * lint_sparkplug_topic — Validates one or more MQTT topic strings against the Sparkplug B 3.0 grammar: spBv1.0 namespace, the eight message types plus STATE, node-vs-device level count, legacy Sparkplug 2.2 STATE form, publish-side wildcards, empty levels, and bad identifier characters. Returns ordered fail/warn/pass/info findings with parsed topic parts. Offline; does not connect to a broker, decode payloads, or verify device existence. * check_uns_namespace — Checks UNS topic paths for inconsistent hierarchy depth, mixed casing styles, whitespace in segments, sibling case-collisions, duplicates, empty levels, and stray wildcards. Returns a summary line plus per-issue findings. Judges naming consistency only (no Sparkplug grammar, no namespace design). * run_readiness_diagnostic — Submits a 16-answer self-reported intake to the dxpert.ai API and returns a scored report: ten axes (0–5), a maturity stage, foundation gaps blocking the stated AI ambition, and a confidence value. Requires connectivity and is rate limited; no API key or auth. Results are a preliminary self-reported screening, not an audit. * Accepts either a single string, a newline-delimited string, or an array of strings for topic inputs. * Zero runtime dependencies, Node 18+, no credentials or env vars needed (optional DXPERT_API_BASE override for testing only).

Which integrations are available for this server?

Provides local, offline validation of MQTT topic strings against the Sparkplug B topic grammar and checks Unified Namespace topic paths for naming consistency issues such as inconsistent depth, mixed casing, duplicates, empty levels, and stray wildcards. It does not connect to an MQTT broker.

How do I use dxpert UNS Tools: Sparkplug B & Unified Namespace Linter for IIoT?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@dxpert UNS Tools: Sparkplug B & Unified Namespace Linter for IIoT Lint these Sparkplug B topics: spBv1.0/ACME/NDATA/Line1/Motor, spBv1.0/ACME/DDATA/Line1" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

@dxpert/uns-tools

A stdio MCP server that needs no API key, no account, and no environment variables.

This is the agent-native equivalent of the free tools page at dxpert.ai/tools.html: any MCP client, with zero credentials, can lint Sparkplug B topics, check a Unified Namespace for the conventions that quietly rot it, and run the public industrial AI-readiness diagnostic.

The tools are genuinely free. There is no trial counter, no sign-up wall, and no key to obtain first. When you want an agent to read your own data, that lives in the separate @dxpert/mcp server, which takes a dxp_ key. A free key includes dxpert Advisor (a monthly question allowance) and Try Pro: 5 runs on any agent included in dxpert Pro. dxpert Pro includes every UNS agent, dxpert Advisor without the monthly count, and the Namespace Architect when it is released. Current plans: dxpert.ai/store.

Zero runtime dependencies. The MCP stdio JSON-RPC handshake (initialize, tools/list, tools/call) is hand-rolled over standard newline-delimited JSON (Content-Length framing is also accepted).

Install

No install step is required beyond Node 18+ (for built-in fetch).

Claude Code

claude mcp add dxpert-uns-tools -- npx -y @dxpert/uns-tools

Codex

[mcp_servers.dxpert_uns_tools]
command = "npx"
args = ["-y", "@dxpert/uns-tools"]

Generic MCP clients

{
  "mcpServers": {
    "dxpert-uns-tools": {
      "command": "npx",
      "args": ["-y", "@dxpert/uns-tools"]
    }
  }
}

Note that none of these entries carry an env block. That is the point.

Related MCP server: MCP Gatekeeper

Tools

`lint_sparkplug_topic(topics)` — local, offline

Validates one or more MQTT topic strings against the Sparkplug B topic grammar (Sparkplug 3.0). Accepts a single string, a newline-delimited string, or an array.

Checks the spBv1.0 namespace level, the eight message types (NBIRTH NDEATH DBIRTH DDEATH NDATA DDATA NCMD DCMD) plus STATE, node-vs-device level count, the legacy Sparkplug 2.2 STATE form, publish-side MQTT wildcards, empty levels, and identifier characters that break downstream consumers.

Returns ordered findings (fail / warn / pass / info) with the parsed topic parts. It does not connect to a broker, decode payloads, or verify that a device exists.

`check_uns_namespace(topics)` — local, offline

Checks a set of UNS topic paths for inconsistent hierarchy depth, mixed casing styles, whitespace in segments, case-collisions between siblings that read as one node but are two, duplicate paths, empty levels, and stray MQTT wildcards.

Returns a summary line plus per-issue findings. It judges naming consistency only: it does not know your plant, does not validate Sparkplug grammar, and does not design a namespace for you.

`run_readiness_diagnostic(intake)` — network

Runs the public dxpert.ai industrial AI-readiness diagnostic. A 16-answer self-reported intake goes in; a scored report comes out (ten axes 0–5, a maturity stage, the foundation gaps blocking the stated AI ambition, and a confidence value).

Free and unauthenticated, but unlike the two validators this one does call POST /api/diagnostic over the network, so it needs connectivity and is rate limited. Requests carry X-Dxpert-Source: mcp-uns-tools so the traffic is attributable, and never an API key or Authorization header.

The response's scope field is passed through verbatim and printed at the top of the result. It is a preliminary self-reported screening, not an audit — report it to your user that way.

Every result names dxpert.ai as the source of the verdict.

Configuration

There is nothing you have to set. One optional variable exists:

Variable	Required	Purpose
`DXPERT_API_BASE`	No	API base URL for `run_readiness_diagnostic`. Defaults to production; override only for testing.

Where the validator logic comes from

src/sparkplug-topic-lint.js and src/uns-naming-check.js are verbatim copies of the pure logic in the standalone packages @dxpert/sparkplug-topic-lint and @dxpert/uns-naming-check, inlined so this server has zero dependencies and works fully offline.

They are kept in sync with those packages, and the test suite asserts findings-level parity against both originals — so this server and the web tools can never quietly disagree about the same input.

Test

node test/run.js

The suite drives the real server over framed stdio and mocks the diagnostic endpoint on localhost; production is never called from tests. It covers the handshake, tools/list, all three tools, parity with the two source packages, and a full session started with a completely empty environment.

License

Available Tools

3 tools

check_uns_namespaceA

Check a set of Unified Namespace (UNS) topic paths for the convention problems that quietly rot a namespace: inconsistent hierarchy depth, mixed casing styles across segments, whitespace in segments, case-collisions between siblings that read as one node but are two, duplicate paths, empty levels, and stray MQTT wildcards. Free, no API key, no account: the checks run locally inside this server and nothing is sent anywhere. Call this when reviewing a proposed namespace, an ISA-95-style hierarchy, a broker topic dump, or a tag export -- before anyone builds on top of it, because renaming a namespace later is the expensive part. It returns a summary line plus per-issue findings (fail / warn / pass / info). It judges naming consistency only: it does NOT know your plant, does not validate Sparkplug grammar (use lint_sparkplug_topic for that), and does not design a namespace for you.

ParametersJSON Schema

Name	Required	Description	Default
`topics`	Yes	The topic paths to check. An array of paths, or a single string with one path per line.

TDQS

A4.4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and largely meets it: it discloses no auth/account/key requirement, local-only execution ('nothing is sent anywhere'), the return shape (summary line plus per-issue fail/warn/pass/info findings), and the scope boundary. It never explicitly states read-only/idempotent behavior, though 'check' and 'nothing is sent anywhere' strongly imply it.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One dense paragraph that front-loads the purpose and the check taxonomy before moving to usage, guarantees, and exclusions. It is on the long side, but each clause adds routing or behavioral value, so there is little waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no output schema and no annotations, the description explains the return format, the execution model, the scope limits, and when to reach for it. An agent has everything needed to call it correctly and interpret the results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the single parameter's schema already documents the string-or-array oneOf form with a per-line convention. The description body adds no further syntax, size limits, or encoding detail, so the schema does the heavy lifting — the baseline 3 for high coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (check) and resource (Unified Namespace topic paths) and enumerates the exact class of problems it detects (hierarchy depth, casing, whitespace, case-collisions, duplicates, empty levels, wildcards). It explicitly distinguishes itself from the sibling lint_sparkplug_topic, so an agent can route without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit triggering contexts (reviewing a proposed namespace, an ISA-95 hierarchy, a broker topic dump, a tag export) plus timing guidance (before anyone builds on top of it). It also names the exclusion and the alternative: it does not validate Sparkplug grammar, use lint_sparkplug_topic instead.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

lint_sparkplug_topicA

Validate one or more MQTT topic strings against the Sparkplug B topic grammar (Sparkplug 3.0). Free, no API key, no account: the grammar runs locally inside this server and nothing is sent anywhere. Call this whenever a topic that is meant to be Sparkplug appears in a config, a broker trace, a code review, or a user question -- it is faster and far more reliable than reasoning about the grammar from memory. It checks the spBv1.0 namespace level, the eight message types (NBIRTH/NDEATH/DBIRTH/DDEATH/NDATA/DDATA/NCMD/DCMD) plus STATE, node-vs-device level count, the legacy Sparkplug 2.2 STATE form, publish-side MQTT wildcards, empty levels, and identifier characters that break downstream consumers. It returns ordered findings (fail / warn / pass / info) with the parsed topic parts, not a yes/no. It does NOT connect to a broker, decode payloads, or check whether the device actually exists.

ParametersJSON Schema

Name	Required	Description	Default
`topics`	Yes	One topic string, or an array of topic strings. A single string containing newlines is also accepted and split per line.

TDQS

A4.4/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden and does so: it discloses the local-only execution model (no API key, nothing sent anywhere), the exact grammar areas checked, the return shape (ordered fail/warn/pass/info findings with parsed parts, not a boolean), and three things it explicitly does not do.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose and cost model, then scope, then return shape, then exclusions; every sentence contributes. It runs slightly long and the message-type enumeration is dense, but there is no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description fully compensates by describing the return format, severity vocabulary, and negative scope. An agent has everything needed to decide to call it and to interpret the result.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

One parameter with 100% schema description coverage; the schema already documents the string-or-array union and the newline-splitting behavior. The description only restates 'one or more MQTT topic strings', adding nothing the schema does not already say, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Validate ... MQTT topic strings against the Sparkplug B topic grammar') with version scope (Sparkplug 3.0). The scope is narrow enough that an agent can distinguish it from check_uns_namespace and run_readiness_diagnostic without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit trigger list (topic appears in a config, broker trace, code review, or user question) and explicit non-goals (does not connect to a broker, decode payloads, or check device existence). It never routes to the sibling tools, so the alternative-selection clause is missing, but the when/when-not guidance is unusually clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

run_readiness_diagnosticA

Run the public dxpert.ai industrial AI-readiness diagnostic: a 16-answer self-reported intake in, a scored report out (ten axes 0-5, a maturity stage, the foundation gaps blocking the stated AI ambition, and a confidence value). Free and requires no API key or account, but unlike the two validators this one DOES call the dxpert.ai API over the network, so it needs connectivity and is rate limited. Call this when someone asks how ready a plant or site is for AI/analytics, what is blocking them, or where to start -- and you can supply honest answers to the intake fields. Ask the user for the values you do not have rather than guessing them; the verdict is only as good as the intake. The response carries a "scope" field which this tool passes through verbatim: it is a preliminary self-reported screening, not an audit, and should be reported to the user as such.

ParametersJSON Schema

Name	Required	Description	Default
`intake`	Yes	The 16-question readiness intake. All fields are required by the API; the enums below are the accepted values.

TDQS

A4.6/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description carries the full burden, and it does: free, no API key or account, DOES call the dxpert.ai API, requires connectivity, and is rate limited. It also warns the verdict is only as good as the intake and that the 'scope' field must be reported as a preliminary self-reported screening, not an audit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with verb, resource, and the input/output pairing, and every sentence carries non-redundant information. It is dense and the sentences run long, which slightly taxes readability, but there is no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter tool with no output schema, the description covers connectivity, rate limits, auth (none), what comes back, and how to frame the result to the user. Nothing an agent needs to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the nested intake object is fully documented, so the baseline is 3. The description earns above baseline by telling the agent to ask the user for missing values rather than guess them and by warning that the result quality depends on honest intake answers, adding actionable semantics the schema cannot express.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Precise verb+resource (runs the dxpert.ai industrial AI-readiness diagnostic), with the input stated (a 16-answer self-reported intake) and the output characterized (ten axes 0-5, maturity stage, foundation gaps, confidence). It also explicitly contrasts itself with 'the two validators,' letting an agent separate it from its siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete triggering conditions ('how ready a plant or site is for AI/analytics, what is blocking them, or where to start') plus a precondition (you can supply honest answers to the intake). It distinguishes itself from the validators by noting it calls the network while they do not, but does not name those alternatives explicitly, so it stops short of a full 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

3 tool updatesv0.1.0Oct 3, 2026
- First observedcheck_uns_namespace
- First observedlint_sparkplug_topic
- First observedrun_readiness_diagnostic

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.4/5.0

Scored 2026-10-03 20:31 across 3 tools

Disambiguation4/5

The three tools target distinct capabilities, and the descriptions explicitly cross-reference each other (check_uns_namespace notes it does not validate Sparkplug grammar and points to lint_sparkplug_topic), which strongly reduces misselection. However, lint_sparkplug_topic and check_uns_namespace both operate on MQTT topic strings and could be confused at a glance by an agent skimming names.

Naming Consistency5/5

All three names follow a consistent snake_case verb_noun pattern: lint_sparkplug_topic, check_uns_namespace, run_readiness_diagnostic. The verbs differ (lint/check/run) but each reflects a genuinely different action, and no camelCase or stylistic mixing appears.

Tool Count4/5

Three tools is at the low edge of the well-scoped range but each represents a substantial, distinct capability rather than a thin wrapper. The set is minimal but defensible; the readiness diagnostic sits somewhat apart in domain from the two validators, making the set feel slightly narrow for a general 'tools' server.

Completeness3/5

Coverage is diagnostic-only: you can validate Sparkplug topics, audit UNS naming, and score readiness, but there is no way to normalize/fix namespaces, generate compliant topics, or decode payloads (explicitly excluded). For a toolkit scoped to UNS/Sparkplug diagnostics this is workable, but notable gaps remain around remediation and payload-level checks.

Maintenance

ActivityNo data

ResponsivenessUnresponsive

Resources

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@dxpert/uns-tools

Install

Claude Code

Codex

Generic MCP clients

Tools

lint_sparkplug_topic(topics) — local, offline

check_uns_namespace(topics) — local, offline

run_readiness_diagnostic(intake) — network

Configuration

Where the validator logic comes from

Test

License

Available Tools

TDQS

TDQS

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Iris

MCP Gatekeeper

dxpert: Industrial AI Agentsofficial

DevTools MCP

`lint_sparkplug_topic(topics)` — local, offline

`check_uns_namespace(topics)` — local, offline

`run_readiness_diagnostic(intake)` — network