industrial-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct resource and action: plants, silos, thermometry, motors, alerts, and motor actions. The only possible overlap is between list_silos and get_silo_thermometry, but one lists silos and the other retrieves data for a specific silo, so there is no ambiguity.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern: list_plants, list_silos, list_motors, get_active_alerts, get_silo_thermometry, trigger_motor_action. The verbs are clear and the style is uniformly snake_case, making the set predictable and easy to navigate.
Tool Count5/5
With 6 tools, the server is well-scoped for industrial monitoring and control. Each tool serves a clear purpose without excessive overlap or unnecessary additions, and the count is neither too thin nor too heavy for the domain.
Completeness4/5
The tool set covers the core workflows: listing assets, retrieving detailed sensor data, checking alerts, and issuing motor control commands with safety checks. Minor gaps exist, such as no dedicated tool for getting a single motor's detailed status or acknowledging alerts, but these do not critically hinder agent operations.
Average 3.9/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It adds behavioral detail by indicating the output includes capacity in metric tons, but it does not state whether the operation is read-only, whether it returns all silos, or any other behavioral traits. This is slightly better than a tautology but still minimal for an unannotated tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence of nine words. Every word contributes meaning, with no redundancy or filler. It is appropriately sized for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one parameter, list operation), and an output schema exists, so return values do not need to be described. However, the description lacks usage guidance and provides only minimal behavioral context. It is adequate as a minimum viable description but leaves gaps in guidance and transparency for an agent selecting this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It links the sole parameter plant_id to the phrase 'at a plant', which confirms that plant_id identifies the silo location. However, it does not explain the format, allowed values, or any other constraints, leaving the parameter semantics mostly to the schema's field name and title.
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?
The description clearly states a specific verb ('List') and resource ('grain silos'), and adds the scope ('at a plant') and a relevant detail ('with capacity in metric tons'). This distinguishes it from siblings like list_plants (by resource), list_motors (by resource), and get_silo_thermometry (by listing vs. retrieving temperature data).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is given about when to use this tool versus alternatives. The phrase 'at a plant' implies the plant_id parameter, but the description does not mention any exclusions or provide context for choosing list_silos over other tools (e.g., get_silo_thermometry).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of disclosing behavioral traits. It states 'List' which implies a read-only operation, but provides no additional context such as pagination, ordering, authentication needs, or side effects. It is not misleading but is minimal, lacking any behavioral detail beyond the action itself.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that states the action, object, context, and optional filter without any redundant words. Every part contributes meaning, and it is appropriately concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple, has an output schema, and the description covers the core function. However, it lacks usage guidance and behavioral context that would make it fully self-sufficient. It is adequate but has clear gaps in when-to-use and behavior disclosure, so it earns a mid-range score.
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?
With schema description coverage at 0%, the description must compensate for parameter meaning. It does mention 'kind' as an optional filter, providing semantic value beyond the schema's plain enum. However, it does not describe 'plant_id' at all, though that is partially inferable from 'at a plant.' The description only partially compensates for the lack of schema descriptions.
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?
The description clearly states a specific action ('List') on a specific resource ('motors') within a context ('at a plant'), and mentions an optional filter ('by kind'). This distinguishes it from sibling tools like list_plants, list_silos, and trigger_motor_action, which operate on different entities or actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a clear context (motors at a plant, optionally filtered by kind) but does not explicitly say when to use this tool versus alternatives, nor mention any exclusions or prerequisites. The usage is implied rather than explicitly articulated, so it earns a mid-range score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description is brief and does not disclose side effects, authorization needs, or rate limits. The verb 'List' implies read-only, but with no annotations, the description carries the burden and provides only minimal behavioral context beyond the action itself.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single concise sentence that is front-loaded with the verb and resource. Every word earns its place with no unnecessary elaboration.
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?
For a simple list tool with no parameters and an existing output schema, the description adequately covers the essentials. It specifies the scope ('the server can see') and leaves return details to the output schema. Slightly more context about what qualifies as 'industrial facilities' could improve completeness, but it remains sufficient.
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?
The tool has zero parameters, so schema coverage is complete. The description adds no parameter details, but the baseline for zero-parameter tools is 4, and the description aligns with that baseline.
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?
The description uses a specific verb 'List' and identifies a clear resource 'industrial facilities', with the scope 'the server can see'. It distinguishes from siblings like list_silos and list_motors by targeting plants specifically.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description only states what it does, without mentioning exclusions, prerequisites, or that siblings like list_silos are for different resource types.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. 'List' implies a read-only operation, and no side effects or limitations are hidden. However, it does not disclose behavioral details such as output sorting, pagination, or definition of 'active', though these are minor for a simple list.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single concise sentence that is front-loaded with the action, zero filler, and no repetition of schema properties.
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 simple two-parameter read-only tool and the presence of an output schema, the description sufficiently covers the core functionality and optional filtering. It lacks only minor contextual guidance such as sourcing plant_id from list_plants, which is not critical.
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 0%, so the description must compensate. It adds meaning by linking 'plant' to plant_id and 'optionally filtered by minimum severity' to min_severity, explaining the filter concept. It does not elaborate on enum values or defaults, but the schema provides those.
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?
The description clearly states the verb 'List', the resource 'active alerts', and the scope 'at a plant', distinguishing it from sibling tools that handle plants, silos, thermometry, motors, and motor actions.
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 clear context for when to use the tool: when active alerts for a plant are needed. While it does not explicitly name alternatives or exclusions, the sibling tools are clearly different resources, so usage is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the 'latest snapshot' nature, includes specific data fields (fill percent, cable count, min/max/avg temperature in Celsius), and implies read-only behavior. This adds value beyond the bare schema, though it could mention error cases or data availability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three concise, front-loaded sentences, each serving a purpose: what it does, what it includes, and when to use it. No redundant language.
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 simplicity of the tool (one parameter, read-only), the description is sufficiently complete. It lists key output fields, and an output schema is present for detailed return values. It doesn't address edge cases, but these are less critical for a straightforward read operation.
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 0%, so the description must compensate. It clarifies that silo_id identifies the specific silo for the request, but adds no format or example. This is minimal but adequate for a single required string parameter.
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?
The description clearly states 'Return the latest thermometry snapshot for one silo,' specifying the verb, resource, and scope. It distinguishes from siblings like list_silos or get_active_alerts by focusing on a single silo's thermometry data.
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 explicitly says 'Use this when the operator asks about a specific silo's health,' providing a clear when-to-use scenario. It doesn't mention exclusions or alternatives, but the context is sufficiently distinct from sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given there are no annotations, the description carries full responsibility for disclosing behavior. It extensively covers the dry-run default, safety precondition evaluation, no field command on dry-run, audit logging for executed commands, and the server flag that gates writes. This is exceptional transparency for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a numbered list and every sentence provides distinct value. It is concise yet comprehensive, covering behavior, prerequisites, and security considerations without redundancy.
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 an industrial control tool with no annotations, the description thoroughly covers execution requirements, safety behavior, and logging. The presence of an output schema reduces the need to explain return values. It is complete for its intended use.
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 description coverage is 0%, so the description must compensate. It explains the meaning and purpose of dry_run, operator_id, and reason effectively within the execution steps. It does not explicitly define motor_id and action, but those are straightforward (motor_id from name, action from enum). This good partial compensation earns a 4.
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?
The description clearly states 'Request a start or stop of an industrial motor' with a specific action and resource. This distinguishes it from sibling tools like list_motors and get_active_alerts, which are read-only.
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 gives explicit step-by-step instructions for executing a real action, including passing dry_run=False, operator_id, and reason, plus the server-side environment requirement. It does not explicitly mention when not to use the tool or compare it to alternatives, but the context is clear and practical.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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