Korral StoreLink MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have clearly distinct purposes: one retrieves inventory and sales data, the other creates a replenishment order. There is no overlap or ambiguity.
Naming Consistency5/5Both tools follow a consistent verb_noun pattern in snake_case: get_store_inventory_and_sales and create_replenishment_order.
Tool Count3/5Only 2 tools is minimal, but acceptable for a narrowly scoped replenishment server. However, typical integrations might benefit from a few more tools.
Completeness2/5The server covers the core check-and-create cycle but lacks essential operations like listing, updating, or canceling replenishment orders, which agents may need.
Average 4/5 across 2 of 2 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
- No code scanning findings
- CI status not available
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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
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only notes that quantity must be positive, but fails to disclose behavioral traits like whether the action is irreversible, required permissions, or rate limits. The word 'create' implies mutation, but more transparency 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose. Efficient and no wasted words. Could potentially be slightly more concise, but it's well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool with three required parameters and no output schema, the description provides adequate context: purpose, usage trigger, and parameter note. Could mention response or constraints (e.g., max quantity) but overall complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema describes all parameters. The description adds the note that quantity is 'number of units to order (positive integer)', but this largely repeats schema info. No meaningful new semantic context beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Raise a replenishment (restock) order for a store/SKU via StoreLink.' The verb 'raise' and resource 'replenishment order' are specific, and it distinguishes from the sibling tool by implying this is the action after inventory check.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use: 'Use after get_store_inventory_and_sales shows a meaningful demand gap.' This provides clear context and prevents misuse.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions returning pre-computed fields but omits details like read-only nature, error handling, rate limits, or authentication requirements. Some transparency is present but incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the action, and contains no extraneous information. It efficiently communicates the tool's purpose and key return values.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite lacking an output schema, the description mentions the key return fields (demand_gap, stockout_risk) which are critical for its usage. For a simple tool with two parameters, the description covers the main points, though it could include more behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents both parameters adequately. The description adds no additional parameter semantics beyond what the schema provides, meeting the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb (fetch) and resource (store/SKU inventory and sales). It explicitly distinguishes itself from the sibling tool 'create_replenishment_order' by highlighting that it combines two data points and provides pre-computed metrics for replenishment decisions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context: when you need both inventory and sales for replenishment, avoiding two calls. However, it does not explicitly state when not to use it or list alternative tools beyond the sibling.
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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