Deterministic Korean Saju / Chinese BaZi Four Pillars MCP server. Heavenly Stems, Earthly Branches, Day Master, five-element distribution, and 0-100 compatibility. No AI, no API key.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 3.7/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: bazi_chart computes a single person's chart, while bazi_compatibility evaluates compatibility between two people. No overlap in functionality.
Both tools follow a consistent 'bazi_' prefix with underscore-separated descriptive nouns (chart, compatibility), forming a clear and predictable naming pattern.
With only 2 tools, the server feels slightly thin for a full 'Four Pillars' server, though it covers core chart computation and compatibility. A typical server of this scope might include 3-5 tools.
The server lacks tools for interpreting the chart, analyzing element relationships, or providing detailed profiles beyond the raw chart and a compatibility score. This leaves significant gaps in the expected lifecycle.
bazi_chartAInspectCompute a deterministic BaZi (八字) / Korean Saju (사주) Four Pillars chart from a solar birth date: Heavenly Stems, Earthly Branches, Day Master, and five-element distribution. KASI-validated engine, no AI.
| Name | Required | Description | Default |
|---|---|---|---|
| day | Yes | ||
| hour | No | 0–23, or -1 if birth time unknown | |
| year | Yes | 1920–2050 | |
| month | Yes | ||
| gender | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It adds behavioral context by stating 'deterministic' and 'KASI-validated engine, no AI', but lacks details on error handling, side effects, 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?
The description is a single, front-loaded sentence that efficiently conveys the tool's purpose without 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?
There is no output schema, so the description should detail return values. It lists components but not their format. Parameter details are lacking, and no comparison with the sibling tool is provided.
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 low (40%). The description mentions 'solar birth date' but does not explain individual parameters like hour (0–23 or -1), gender (M/F), or the year range. It adds little beyond the schema definitions.
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 states a specific verb 'compute', the resource 'BaZi chart', and lists output components (Heavenly Stems, Earthly Branches, Day Master, five-element distribution). It clearly distinguishes from the sibling tool 'bazi_compatibility'.
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 implies usage for computing a BaZi chart from a solar birth date, but does not explicitly state when to use this tool vs. the sibling 'bazi_compatibility' or provide any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
bazi_compatibilityAInspectDeterministic BaZi compatibility score (0–100) and band between two people, from combined five-element balance and Day Master cycle relation.
| Name | Required | Description | Default |
|---|---|---|---|
| person_a | Yes | {year,month,day,hour,gender} | |
| person_b | Yes | {year,month,day,hour,gender} |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose all behavioral traits. It mentions the tool is 'deterministic' and explains the computation basis, but does not cover error conditions, authentication needs, or output structure beyond score and band. This is a moderate disclosure.
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 core purpose and methodology. Every word adds value with no redundancy or filler.
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 or annotations, the description adequately explains the tool's output (score and band) and logic. However, it omits details about the band interpretation, input validation, and return format. It is nearly complete but leaves minor gaps.
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 descriptions for person_a and person_b as '{year,month,day,hour,gender}'. The description adds only that these are for two people, without specifying format or constraints. It adds little meaning beyond the schema, thus 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 computes a BaZi compatibility score (0–100) and band between two people, using five-element balance and Day Master cycle relation. It specifies the verb (compatibility), resource (BaZi), and output, distinguishing it from the sibling bazi_chart.
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 implies use for compatibility assessment but provides no explicit guidance on when to use this tool versus the sibling bazi_chart. There is no mention of prerequisites or alternatives, 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.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
