open-sales-stack
Server Quality Checklist
Latest release: v0.0.2
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or confusion. The tool's purpose is clearly defined.
Naming Consistency5/5The single tool uses a clear snake_case name that follows a verb_noun pattern, which is consistent and readable.
Tool Count1/5The server is named 'open-sales-stack', implying a broad sales automation toolkit, but provides only a single web scraping tool. This is a severe mismatch between scope and tool count.
Completeness1/5The server offers only webpage extraction, leaving out essential sales stack functionalities such as lead management, CRM integration, or deal tracking. The tool surface is severely incomplete for the implied domain.
Average 4.5/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits 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
This repository is licensed under MIT License.
This repository includes a README.md file.
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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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavior. It explains the two modes ('scrape' with JS rendering, 'crawl' with page limit), the requirement for a schema and prompt, and the return format (structured JSON). It does not mention rate limits, authentication, or side effects, but for a read operation like scraping, these gaps are minor. The description is sufficient for an agent to understand what the tool does and its boundaries.
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 highly concise yet comprehensive. It uses a bullet-like structure for modes and requirements, making it easy to parse. Each sentence serves a purpose: stating the tool's function, usage context, modes, and mandatory inputs. There is no fluff or redundancy. It is well-organized and front-loaded with the core action and requirements.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, modes, nested schema) and the presence of an output schema (which reduces the need to explain return values), the description is complete. It covers all aspects: what the tool does, when to use it, the two modes, the three required inputs, and examples for each parameter. The agent has all the information needed to invoke the tool correctly without ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, meaning every parameter has a description in the schema. The description adds value by explaining the modes, the necessity of three things, and providing clear examples for each parameter. For instance, it clarifies the difference between 'scrape' and 'crawl' and gives use-case examples for the prompt and schema. This extra context goes beyond the schema, justifying a score above the baseline of 3.
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: 'Scrape or crawl a webpage and extract structured data as JSON using a custom schema.' It provides specific verbs (scrape/crawl) and the resource (webpage), making it unambiguous. With no sibling tools to differentiate, it achieves maximum clarity.
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 specifies when to use the tool: 'when you know the specific URL of a website and need to extract particular information in a well-defined, structured format.' It also outlines two modes and the three required inputs. Although it does not explicitly state when not to use it or compare to alternatives, the context is clear enough for an agent to decide. The lack of sibling tools reduces the need for exclusions, so a 4 is appropriate.
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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