Skip to main content
Glama
compiuta-origin

Connhex MCP Server

Official

List Manufacturing Resources

list_manufacturing_resources
Read-only

Retrieve manufacturing resources like devices and plants from Connhex IoT platform using filters, sorting, and relationship includes.

Instructions

List resources of a given type from a JSON:API service.

Supports filtering, sorting, relationship includes, and pagination. Argument details are provided in the parameter schema.

If available, call the get_schema tool first to discover valid resource types, attributes, and relationships.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoComma-separated sort fields. Prefix a field with "-" for descending.
filterNo JSON:API filter tree. The dict is walked recursively and translated into `filter[...][...]=value` query params. Examples: {"serial": "ABC"} → exact match (default) {"serial": ["A", "B"]} → match any of A or B {"serial": {"fuzzy-match": "ABC"}} → case-insensitive prefix {"createdAt": {"min": "2024-01-01", "max": "2024-12-31"}} → range {"site": {"exists": True}} → presence check {"address.city": "Milan"} → object property (dot) {"site:name": {"fuzzy-match": "Plant"}} → related-resource property (colon) {"or": {"name": {"fuzzy-match": "x"}, "serial": {"fuzzy-match": "x"}}} → OR across fields NOTE: do not filter a relationship directly by its resource ID (for example, {"installation": "<installation-id>"} is not supported). Relationship filters must end in an actual attribute from the related resource, such as {"installation:name": "Plant A"}. When only a related resource ID is known, first call `get_resource` for it, extract a suitable attribute, then call `list_resources` with a `relationship:attribute` filter. For example: get the installation by ID, read its `name`, then list devices with {"installation:name": "<installation-name>"}. A relation path is one literal, flat dict key: use {"installation:name": "Plant A"}, never nest it as {"installation": {"name": "Plant A"}}. To verify that a match belongs to a specific related resource ID, set `include` to the relationship name (for example, `include="installation"`) and compare its returned ID. Operators: match (default, can be omitted), fuzzy-match, min, max, exists. Combinators: and (default), or — top-level only, cannot be mixed. Field names come from the schema resource.
includeNoComma-separated relationship names to include inline. Use this whenever the answer needs data from related resources: it replaces relationship references with full nested included resources and avoids a second API call. Example: to find devices for an installation, call resource_type="devices", filter={"installation:name": "Plant A"}, include="installation" so each device result includes the matched installation details.
page_limitNoNumber of results per page.
page_offsetNoPagination offset.
resource_typeYesResource type name from get_schema, e.g. "devices" or "plants".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
totalNo
has_nextYes
Behavior3/5

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

Annotations already declare `readOnlyHint: true`, so the description does not need to restate this. It adds functional details (supports filtering, sorting, includes), but no additional behavioral traits beyond what annotations provide. Moderate value added.

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?

The description is concise with three sentences that front-load the purpose and capabilities. It avoids redundancy but includes a slightly unnecessary line about argument details being in the schema, which is already obvious.

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

Completeness4/5

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

Given the rich input schema and existence of an output schema, the description provides useful context such as suggesting `get_schema` and outlining supported features. It is complete enough for an agent to understand the tool, though it lacks explicit sibling differentiation.

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% with detailed descriptions for all 6 parameters, especially the `filter` parameter. The description adds minimal extra meaning beyond the schema, only noting that argument details are in the parameter schema and suggesting `get_schema`. Baseline 3 is appropriate.

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

Purpose4/5

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

The description states it lists resources of a given type from a JSON:API service. The tool name includes 'manufacturing', making the domain clear, but it does not explicitly differentiate from the sibling 'list_resources' tool, which might have similar functionality for non-manufacturing resources.

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?

The description provides explicit guidance to call `get_schema` first to discover valid types and attributes. However, it does not specify when to use this tool versus alternatives like `list_resources`, leaving some ambiguity for an AI agent.

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

Install Server

Other Tools

Latest Blog Posts

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/compiuta-origin/connhex-tools'

If you have feedback or need assistance with the MCP directory API, please join our Discord server