The CerebroChain MCP server connects AI agents to CerebroChain's supply chain APIs for logistics and warehouse management, offering tools across three access tiers.
š Free Tools (No API Key Required)
Compare shipping rates across UPS, FedEx, USPS, and DHL (by ZIP code, weight, dimensions)
Check platform health and service availability
š Authenticated Tools (API Key Required)
Search inventory by name, SKU, barcode, or category
Check stock levels for a specific item (on hand, reserved, available)
Look up items by SKU ā full details including stock, location, and pricing
List warehouse locations with zone/aisle/bin hierarchy
List and filter orders by status (pending, processing, shipped, delivered, cancelled)
Get order status and tracking for a specific order
Get order statistics and KPIs (fulfillment rate, average value, trends)
Get fleet statistics (active vehicles, utilization, fuel efficiency, maintenance)
List fleet vehicles filtered by status
Track shipments in real-time (location, ETA, stops completed)
View shipment history with 90-day analytics and carrier performance
š Premium Tools (Subscription Required)
AI route optimization considering traffic, capacity, time windows, and fuel costs
AI demand forecasting for inventory needs and sales trends
AI bottleneck detection with severity levels and recommendations
AI optimization recommendations for warehouse, logistics, or overall supply chain
Financial metrics (revenue, margins, cash flow, profit/loss)
KPI dashboard showing targets vs. actuals for all supply chain metrics
Natural language commands to query or control the system (e.g., "show delayed shipments" or "reserve 50 units of SKU-1234")
Enables comparison of shipping rates and tracking of shipments through DHL's logistics network.
Enables comparison of shipping rates and tracking of shipments through FedEx's delivery services.
Enables comparison of shipping rates and tracking of shipments through UPS's logistics infrastructure.
Enables comparison of shipping rates and tracking of shipments through the United States Postal Service.
The first logistics/WMS MCP server for AI agents. Connect Claude, Cursor, or any MCP-compatible AI to CerebroChain's supply chain APIs.
npm install -g @cerebrochain/mcp-serverRelated MCP server: ShippingRates
Add to ~/.config/claude-desktop/claude_desktop_config.json (Linux) or ~/Library/Application Support/Claude/claude_desktop_config.json (macOS):
{
"mcpServers": {
"cerebrochain": {
"command": "cerebrochain-mcp",
"env": {
"CEREBROCHAIN_API_KEY": "your-api-key"
}
}
}
}Add to .cursor/mcp.json in your project:
{
"mcpServers": {
"cerebrochain": {
"command": "cerebrochain-mcp",
"env": {
"CEREBROCHAIN_API_KEY": "your-api-key"
}
}
}
}Variable | Required | Description |
| For Tier 2/3 | Your CerebroChain API key |
| No | Custom API URL (default: |
| No | Alternative auth via JWT token |
Tool | Description |
| Compare rates across UPS, FedEx, USPS, DHL |
| Check platform health |
Tool | Description |
| Search inventory items with filters |
| Get stock levels for an item |
| Look up item by SKU |
| List warehouse storage locations |
| List and search orders |
| Get order details and tracking |
| Get order KPIs |
| Get fleet utilization metrics |
| List fleet vehicles |
| Track a shipment route |
| Get shipment history |
Tool | Description |
| AI-powered route optimization |
| AI demand forecasting |
| AI bottleneck detection |
| AI optimization suggestions |
| Revenue, margins, cash flow |
| KPI targets vs actuals |
| Natural language commands |
URI | Description |
| Inventory overview |
| Fleet status snapshot |
| Executive KPIs |
Once configured, ask your AI:
"Compare shipping rates from 90210 to 10001 for a 5lb package"
"Search inventory for items containing 'widget'"
"What's the status of order ORD-12345?"
"Optimize delivery route for these 10 stops"
"What items will be out of stock in 30 days?"
Visit cerebrochain.com to create an account and generate an API key.
MIT
Get current stock levels for a specific inventory item by ID. Returns quantity on hand, reserved, available. Requires API key.
| Name | Required | Description | Default |
|---|---|---|---|
| item_id | Yes | Inventory item ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description discloses auth requirement and return fields but does not explicitly state read-only nature or potential side effects. Adequate for a simple read tool but could be more thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with front-loaded purpose. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Sufficient for a simple tool with 1 parameter and no output schema. Could mention limitations like warehouse scope, but overall complete for its complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only one parameter 'item_id' with schema description 'Inventory item ID'. Schema coverage is 100% so baseline 3; description adds no extra semantics beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Get current stock levels for a specific inventory item by ID' and lists returned fields. Differentiates from siblings like 'search_inventory' and 'lookup_sku'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives. Mentions API key requirement but no conditions or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare shipping rates across multiple carriers (UPS, FedEx, USPS, DHL) for a package. Returns cheapest and fastest options. Free ā no API key needed.
| Name | Required | Description | Default |
|---|---|---|---|
| from_zip | Yes | Origin ZIP/postal code | |
| to_zip | Yes | Destination ZIP/postal code | |
| weight_lbs | Yes | Package weight in pounds | |
| length_in | No | Package length in inches | |
| width_in | No | Package width in inches | |
| height_in | No | Package height in inches | |
| from_country | No | Origin country code | US |
| to_country | No | Destination country code | US |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the tool is free and requires no API key, but does not mention rate limits, precision of rates, or whether dimensions are needed for accurate comparisons. Behavioral traits are partially covered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no filler. The first sentence delivers the core purpose with carrier names, the second adds a key selling point (free, no API key). Highly efficient and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema is provided, so the description should hint at return format. It says 'returns cheapest and fastest options' but does not explain structure (e.g., object with carrier, price, time). Also does not clarify if dimensions are mandatory for accurate rates, though schema marks them optional.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description does not add extra meaning to parameters beyond what the schema provides; it only mentions carriers which are not reflected in parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool compares shipping rates across multiple named carriers (UPS, FedEx, USPS, DHL) and returns cheapest and fastest options. This distinctly separates it from sibling tools like track_shipment or list_shipments.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives among siblings, such as track_shipment for tracking or get_order_status for order details. The mention of 'Free ā no API key needed' is a condition but not a comparative guideline.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
AI-powered bottleneck detection. Identifies current and predicted supply chain bottlenecks with severity and recommendations. Premium tool. Requires API key.
| Name | Required | Description | Default |
|---|---|---|---|
| scope | No | Analysis scope | full-chain |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It notes the tool is 'AI-powered' and mentions premium status and API key requirement, but fails to disclose other behavioral traits like whether it is read-only, rate limits, or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences, front-loading the core purpose. However, it could be slightly more structured to separate functional from non-functional aspects.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one optional parameter and no output schema, the description provides minimal context. It explains the basic purpose and premium nature, but lacks details about return format, data volume, or how to interpret results, which are important for an AI-powered tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter 'scope'. The description adds no additional meaning beyond the schema's enum values and description 'Analysis scope'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool detects current and predicted supply chain bottlenecks with severity and recommendations. It provides a specific verb+resource combination and distinguishes from sibling tools like check_stock_levels and forecast_demand.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions it is a premium tool requiring an API key, giving limited usage context. However, it lacks explicit guidance on when to use this tool versus alternatives or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
AI-powered demand forecasting. Ask natural language questions about future inventory needs, sales trends, and supply chain patterns. Premium tool. Requires API key.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural language forecast question (e.g., "What items will be out of stock in 30 days?") | |
| hemisphere | No | Which hemisphere to query | wms |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must convey all behavioral traits. It mentions 'AI-powered' and 'Requires API key' but omits details on response types, rate limits, latency, error handling, or whether it can handle multi-part questions. The description is insufficient for safe invocation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each adding distinct value: core function, capabilities, and constraints. No redundancy or wall of text. Efficiently front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For an AI forecasting tool with no output schema, the description lacks details on return format (e.g., confidence intervals, time series data), error states, or prerequisites beyond API key. Incomplete for agent to gauge results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds an example for query parameter, which is helpful. Hemisphere parameter is well-defined in schema. No further elaboration needed, but no extra value beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it forecasts demand using natural language questions, specifying target areas: inventory needs, sales trends, supply chain patterns. While it distinguishes from generic tools like natural_language_command, it could more explicitly contrast with siblings like detect_bottlenecks or get_optimization_recommendations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The 'Premium tool. Requires API key.' hints at access but does not explain conditions or exclusions. Siblings suggest multiple ways to query data, but no comparative advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get real-time financial metrics ā revenue, margins, cash flow, profit/loss. Premium tool. Requires API key.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It indicates the tool is read-only ('Get real-time financial metrics') and discloses authentication requirements ('Requires API key'). However, it lacks details on rate limits, data freshness, or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, front-loaded with the main action and key differentiators. No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and no output schema, the description is fairly complete. It explains the tool's function, sample data fields, and prerequisite. However, it does not mention output structure or potential overlap with sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters (100% schema coverage), so description does not need to explain parameters. It adds value by listing the data fields returned, which helps the agent understand output.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get real-time financial metrics' and lists specific examples (revenue, margins, cash flow, profit/loss). It distinguishes itself from sibling tools by focusing on financial data and mentioning 'Premium tool. Requires API key.'
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions the tool is premium and requires an API key, implying access control but does not explicitly state when to use it versus alternatives like get_kpi_dashboard. No guidance on when not to use is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get real-time fleet KPIs ā active vehicles, utilization, fuel efficiency, maintenance status. Requires API key.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It states 'real-time' and lists KPIs but does not specify whether the operation is read-only, idempotent, or if it has side effects. Additionally, there is no mention of rate limits, caching, or error behavior. For a real-time data retrieval tool, more transparency about data freshness and potential delays is needed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, starting with the core purpose 'Get real-time fleet KPIs', followed by a list of KPIs and authentication requirement. Every sentence adds value without redundancy. It is well-structured and efficiently conveys essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no parameters, no output schema, and no annotations, so the description is the sole source of information. While it covers purpose and authentication, it lacks details on the return format, data volume, update frequency, and whether the 'real-time' aspect imposes rate limits. For a KPI tool used for monitoring, more context about data staleness and pagination would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameters (100% coverage by description), so the baseline is 4. The description lists the specific KPIs returned (active vehicles, utilization, fuel efficiency, maintenance status), which adds context beyond the empty schema. No parameter documentation is needed, but the description could enhance clarity by noting that no parameters are required.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the verb 'Get', the resource 'real-time fleet KPIs', and lists specific KPIs (active vehicles, utilization, fuel efficiency, maintenance status). The name 'get_fleet_stats' further reinforces purpose. Among sibling tools like 'list_vehicles' and 'get_kpi_dashboard', this tool is distinct for fleet-specific KPIs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions 'Requires API key', which is an authentication requirement, but provides no explicit guidance on when to use this tool versus alternatives. It does not state when not to use it or offer comparisons to sibling tools like 'get_kpi_dashboard' or 'detect_bottlenecks', leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get KPI performance dashboard ā targets vs actuals for all key supply chain metrics. Premium tool. Requires API key.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It mentions API key requirement but lacks details on read-only behavior, rate limits, or data refresh frequency. Does not disclose if the dashboard is aggregated or real-time.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no redundancy. First sentence immediately states purpose, second adds important access constraint. Every word contributes.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no parameters or output schema, description is minimal. It covers purpose and access but omits output format, interpretability, or integration hints with sibling tools. Adequate for a simple tool but could be more helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Zero parameters; schema coverage is 100%. Description adds value by explaining the dashboard content ('targets vs actuals for all key supply chain metrics') beyond the empty schema. Baseline score of 4 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description uses specific verb 'Get' and resource 'KPI performance dashboard', clarifying scope as 'targets vs actuals for all key supply chain metrics'. It distinguishes from sibling tools like get_financial_metrics or get_optimization_recommendations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
States 'Premium tool. Requires API key' as a constraint but does not explicitly say when to use this tool versus alternatives like check_stock_levels or get_order_statistics. Usage context is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get AI-generated optimization recommendations for warehouse operations, logistics routes, or overall supply chain efficiency. Premium tool. Requires API key.
| Name | Required | Description | Default |
|---|---|---|---|
| area | No | Area to optimize | overall |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It only mentions 'AI-generated' without details on side effects, rate limits, or authentication requirements beyond an API key.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences that efficiently convey purpose and access requirements without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple input (one optional enum) and no output schema, the description provides adequate context. Lacks details on response format but is otherwise sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear enum descriptions. The description adds general context but does not enhance parameter meaning beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool retrieves AI-generated optimization recommendations for warehouse operations, logistics routes, or overall supply chain efficiency. Differentiates from sibling tools like optimize_route which is more specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Mentions it's a premium tool requiring an API key, implying restricted usage, but does not explicitly compare to alternatives or state when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get order statistics and KPIs ā total orders, fulfillment rate, average value, trends. Requires API key.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It mentions the auth requirement (API key) but omits other behavioral traits like data staleness, read-only nature, or rate limits. The coverage is minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, each adding value. Could be more concise but no wasted words. Structure is front-loaded with purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, so description must explain returns. It lists the key outputs (total orders, fulfillment rate, average value, trends). Though format details are missing, it covers the main semantics. For a parameterless tool, this is fairly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so schema coverage is irrelevant. The description adds no parameter info but none is needed. Baseline 4 applies as no value is lost.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves order statistics and KPIs, listing specific metrics. It is unambiguous but does not explicitly differentiate from siblings like get_kpi_dashboard. Score 4 for clear purpose with minor lack of distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The only usage guidance is 'Requires API key'. There is no advice on when to use this tool versus alternatives (e.g., get_kpi_dashboard, get_financial_metrics). The agent receives no context for selection decisions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get detailed status and tracking for a specific order. Requires API key.
| Name | Required | Description | Default |
|---|---|---|---|
| order_id | Yes | Order ID to look up |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only mentions the requirement for an API key, but omits details like rate limits, error handling, or data freshness.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the main purpose, and a succinct auth note. Could be slightly more concise but generally well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter and no output schema, the description covers basic purpose and auth but lacks detail on behavior (e.g., idempotency, data freshness, error responses) that would aid an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a single parameter, and the description does not add significant meaning beyond the schema's minimal description. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'detailed status and tracking for a specific order,' which distinguishes it from sibling tools like 'list_orders' or 'track_shipment'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives (e.g., track_shipment, get_order_statistics), nor when not to use it. The description lacks context for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check CerebroChain platform health and service availability. Free ā no API key needed.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the full burden of behavioral disclosure. It reveals that the tool is free and requires no API key, which is useful. However, it does not mention other traits like rate limits, data freshness, or whether it is read-only, leaving some behavioral aspects unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, consisting of only two sentences that deliver essential information without any wasted words. It is front-loaded with the purpose and followed by a key usage note.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema), the description is reasonably complete. It covers what the tool does and a critical usage detail (free, no auth). However, it could be slightly more complete by mentioning what the health check returns (e.g., status indicators), but this is minor.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters and the schema coverage is 100% (empty schema). According to the guidelines, this gives a baseline of 4, as no parameter details are needed beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Check' and the resource 'CerebroChain platform health and service availability', making the tool's purpose unambiguous. It also distinguishes from siblings by focusing on platform health, which no other sibling tool addresses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description notes 'Free ā no API key needed', which provides clear guidance on when to use (no authentication required) and implies a cost-free check. However, it does not explicitly mention when not to use or list alternatives, though no obvious alternatives exist among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List warehouse storage locations with hierarchy (zones, aisles, bins). Requires API key.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It states it's a read operation (list) and requires authentication. However, it does not disclose response structure, pagination, or any additional behavioral details beyond the basic operation. Adequate but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently conveys the action, resource, and a key constraint (hierarchy detail) plus authentication requirement. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and no output schema, the description covers the essential purpose and a key security requirement. It is sufficiently complete for a simple list tool, though it could optionally mention default sorting or limits.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, and schema description coverage is 100%. The description does not need to add parameter info, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists warehouse storage locations with hierarchy, specifying zones, aisles, and bins. The verb 'list' and resource 'warehouse locations' are precise, and the hierarchy detail distinguishes it from related tools like check_stock_levels or search_inventory.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description only mentions a prerequisite (requires API key) but provides no guidance on when to use this tool versus alternatives, nor any exclusions or context for optimal use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List and search orders with filters. Returns order ID, status, items, total value. Requires API key.
| Name | Required | Description | Default |
|---|---|---|---|
| status | No | Filter by order status | |
| page | No | Page number | |
| limit | No | Orders per page |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose behavioral traits. It mentions 'Requires API key' and return fields, but does not address pagination behavior, rate limits, or data freshness. Provides some but not comprehensive transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, then return info and requirement. No wasted words, efficient and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, so description must cover returns. It lists some fields (ID, status, items, total value) but lacks pagination details, total count, or error handling. Adequate but not fully complete for a listing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds no specific parameter details beyond the schema; 'with filters' is generic. No additional meaning for status, page, or limit.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List and search' and resource 'orders', and mentions filters. It distinguishes from siblings like get_order_status (single order) and get_order_statistics (aggregates).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus siblings like get_order_status or get_order_statistics. The description only notes an API key requirement, not usage context or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get shipment history with analytics ā 90-day view of all shipments, delivery rates, carrier performance. Requires API key.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses the 90-day time window and requirement of API key, but does not explicitly state read-only nature, lack of modification, pagination, or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no wasted words. First sentence clearly states purpose and scope, second adds requirement. Perfectly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, so description must compensate. It mentions content (delivery rates, carrier performance) but lacks details on fields, pagination, or data structure, leaving some uncertainty.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0 parameters with 100% coverage; baseline is 4. Description adds no param details but none are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states 'Get shipment history with analytics' and specifies '90-day view of all shipments, delivery rates, carrier performance', which is specific and distinguishes from siblings like track_shipment.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Only mentions 'Requires API key' as a precondition. No guidance on when to use this tool versus alternatives like compare_shipping_rates or detect_bottlenecks, and no when-not-to-use conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List fleet vehicles with status, location, and capacity. Requires API key.
| Name | Required | Description | Default |
|---|---|---|---|
| status | No | Filter by vehicle status |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only mentions that an API key is required but does not state whether the operation is read-only, whether there are rate limits, pagination behavior, or any side effects. This is insufficient for a tool with zero annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: a single sentence that conveys the purpose and a required authentication detail. Every word earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one optional parameter, no output schema), so the description covers the basics: what is listed and auth requirement. However, it lacks details about return format, pagination, data limits, or ordering, which are relevant even for a simple list tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter 'status', which is fully described in the schema with an enum. The description adds no extra meaning beyond what the schema already provides, which is the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and the resource 'fleet vehicles', and mentions specific attributes (status, location, capacity). This distinguishes it from sibling tools like 'get_fleet_stats' which likely return aggregated data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives (e.g., 'get_fleet_stats' or 'track_shipment'). It does not mention any prerequisites or exclusions, leaving the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Look up an inventory item by its SKU code. Returns full item details including stock, location, and pricing. Requires API key.
| Name | Required | Description | Default |
|---|---|---|---|
| sku | Yes | SKU code to look up |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It notes the API key requirement and return details (stock, location, pricing), but lacks info on side effects, rate limits, or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences front-load the purpose and return value, with no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 purpose, return details, and a requirement. Could mention it's a read-only operation for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a description for the sku parameter. The description adds minimal value beyond 'by its SKU code', so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool looks up an inventory item by SKU, a specific verb and resource. It distinguishes from siblings like search_inventory which may have broader scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions an API key requirement but provides no guidance on when to use this tool versus alternatives like search_inventory or check_stock_levels.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Process a natural language command through CerebroChain AI Command Center. Supports queries like "show me all delayed shipments" or "reserve 50 units of SKU-1234". Premium tool. Requires API key.
| Name | Required | Description | Default |
|---|---|---|---|
| command | Yes | Natural language command or query |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided. The description notes it is a 'Premium tool' and 'Requires API key', but does not disclose whether the command is read-only, destructive, or has side effects. For a tool that executes arbitrary natural language commands, this is a significant gap in behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief with three sentences, including a clear opening statement and two supplementary notes. It avoids unnecessary detail, though the 'Premium tool' line could be integrated more seamlessly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema and the tool's broad capability, the description should clarify what the tool returns (e.g., confirmation, data, error messages). It covers basic usage and requirements, but lacks completeness for an NL command executor.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema describes the parameter 'command' as 'Natural language command or query', and the description adds concrete examples ('show me all delayed shipments', 'reserve 50 units of SKU-1234'), enriching the semantic understanding beyond the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool processes natural language commands via CerebroChain AI Command Center, with specific examples like 'show me all delayed shipments'. This distinguishes it from the many sibling tools that handle specific functions, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus siblings. While it implies broad applicability, it does not advise against using it for tasks better handled by specific tools, nor does it mention prerequisites or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
AI-powered route optimization. Calculates optimal delivery route considering traffic, capacity, time windows, and fuel costs. Premium tool. Requires API key.
| Name | Required | Description | Default |
|---|---|---|---|
| stops | Yes | Delivery stops to optimize | |
| vehicle_capacity_lbs | No | Vehicle weight capacity in pounds |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool is 'AI-powered' and 'calculates,' implying a read-only operation, but does not disclose side effects, idempotency, or whether it requires write permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is conciseāthree short sentencesāand front-loaded with the core purpose. No redundant or unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains the tool's purpose and key factors, but lacks details on output format (e.g., what is returned) and does not mention constraints like minItems=2 on stops. Given no output schema, this is a gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description adds meaning by linking the parameters to optimization factors (e.g., 'capacity' relates to vehicle_capacity_lbs, 'time windows' to stops' time_window fields). This goes beyond the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool calculates optimal delivery routes considering key factors (traffic, capacity, time windows, fuel costs). However, it does not explicitly differentiate from sibling tools like get_optimization_recommendations, which may overlap.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The description mentions 'Premium tool. Requires API key,' which hints at access restrictions but does not provide context on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search inventory items with filters. Returns SKU, name, quantity, location, category. Requires API key.
| Name | Required | Description | Default |
|---|---|---|---|
| search | No | Search term (name, SKU, barcode) | |
| category | No | Filter by category | |
| page | No | Page number | |
| limit | No | Items per page |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must cover behavioral traits. It mentions the API key requirement and lists return fields, but omits pagination behavior, search semantics (fuzzy/exact), error handling, and rate limits. This is minimal for a search tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two efficient sentences with no fluff. The core purpose and return fields are front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 4 parameters, no output schema, and no annotations, the description is incomplete. It does not explain pagination behavior, default sorting, or when to use this tool over siblings with overlapping functionality.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema descriptions are already clear and cover 100% of parameters. The tool description adds no additional semantic value beyond stating 'with filters', which is already implied. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it searches inventory items with filters and lists return fields (SKU, name, quantity, location, category). However, it does not differentiate from similar sibling tools like 'lookup_sku', which likely offers a more specific search by SKU.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'lookup_sku' or 'get_warehouse_locations'. It only mentions a prerequisite (API key) but lacks context on appropriate scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get real-time tracking for a shipment route ā current location, progress, ETA, stops completed. Requires API key.
| Name | Required | Description | Default |
|---|---|---|---|
| route_id | Yes | Route or shipment ID to track |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It mentions real-time tracking and API key requirement but does not explicitly state that it is a read-only operation, nor any side effects or rate limits. It is adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loading the purpose and return values, and then the requirement. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately describes what the tool returns (location, progress, ETA, stops). It is fairly complete for a simple tracking tool with one parameter, though could explicitly state the output is a tracking object.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear parameter description for route_id. The tool description does not add additional meaning beyond what the schema already provides, so baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get', the resource 'shipment route', and the specifics it returns ('current location, progress, ETA, stops completed'). This distinguishes it from sibling tools like get_order_status or list_shipments.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes a prerequisite ('Requires API key') but does not provide guidance on when to use this tool versus alternatives like get_order_status. Usage context is implied but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0check_stock_levelscompare_shipping_ratesdetect_bottlenecksforecast_demandget_financial_metricsget_fleet_statsget_kpi_dashboardget_optimization_recommendationsget_order_statisticsget_order_statusget_platform_statusget_warehouse_locationslist_orderslist_shipmentslist_vehicleslookup_skunatural_language_commandoptimize_routesearch_inventorytrack_shipmentScored across 20 tools
Most tools have distinct purposes (e.g., check_stock_levels vs lookup_sku vs search_inventory target different access patterns), but the natural_language_command tool significantly overlaps with list_orders, list_shipments, search_inventory, and others, creating ambiguity about when to use specific structured tools versus the flexible NL interface.
Excellent consistency throughout. All tools use snake_case with clear verb_noun patterns (get_, list_, check_, search_, optimize_, compare_). Verb usage is predictable: 'get' for specific items, 'list' for collections, 'search' for filtered queries.
Twenty tools is slightly above the ideal range but reasonable given the domain complexity spanning inventory, orders, shipments, fleet, warehouse, and AI analytics. Each tool serves a distinct supply chain function without obvious redundancy.
Strong coverage across the supply chain lifecycle: inventory management, order tracking, shipment logistics, fleet optimization, and financial metrics. Minor gaps in explicit write/update operations (create_order, update_inventory), though natural_language_command appears to handle some mutations.
Furgonetka MCP Server is an extension for LLMs (such as Claude) that integrates AI assistants with Poland's most popular courier brokerage platform. The server enables models to interact directly with services from various couriers (including InPost, DPD, DHL, UPS, and Poczta Polska) through a single, unified interface. With this integration, your AI stops just "writing about logistics" and starts actually managing it.
The OpenRouter for tools. One MCP connection gives any AI agent 254 hosted tools, pay per call.
Pay-per-use tool marketplace for AI agents. Search, price-check, and call APIs via MCP.
MCP server giving AI agents one-connection access to supply-chain & logistics data: AIS vessel track
