Brazilian CEP MCP
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to compare it to. The tool's purpose is clearly defined and singular.
Naming Consistency5/5Since there is only one tool, naming consistency is inherently perfect, as there are no other tool names to compare it against for patterns or conventions.
Tool Count2/5A single tool for a CEP lookup server feels thin and under-scoped, as it only provides query functionality without supporting related operations like validation, batch processing, or reverse lookup. This limits the server's utility for broader agent workflows.
Completeness2/5The server is severely incomplete for a CEP domain, as it only offers a query tool without any complementary operations such as address validation, error handling for invalid CEPs, or integration with other Brazilian address services, leaving significant gaps in functionality.
Average 3.7/5 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.
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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 the full burden of behavioral disclosure. It states the tool queries address information, which implies a read-only operation, but it does not disclose other traits such as rate limits, error handling, authentication needs, or data sources. The description adds minimal behavioral context beyond the basic function.
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 a single, efficient sentence that directly states the tool's function without any unnecessary words. It is front-loaded with the core purpose and appropriately sized for a simple tool with one parameter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter, no output schema, no annotations), the description is adequate but has gaps. It covers the basic purpose and parameter context but lacks details on behavioral aspects like error cases or return format, which would be helpful for an agent to use it correctly without structured output information.
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?
The input schema has 100% description coverage, clearly documenting the 'cep' parameter with format and length constraints. The description adds value by specifying that it queries 'address information' and mentions 'Brazilian postal code (CEP)', providing context about the parameter's purpose and regional scope, which compensates for the lack of output 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 specific action ('Query address information') and resource ('from a Brazilian postal code (CEP)'). It uses a precise verb and distinguishes the tool's purpose without redundancy, as there are no sibling tools to differentiate from.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving address data from Brazilian postal codes, but it does not provide explicit guidance on when to use this tool versus alternatives (e.g., other geolocation tools) or any exclusions. Since there are no sibling tools, the lack of comparative guidance is less critical, but it still offers only implied context.
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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