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Glama

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
DB_DSNYesDatabase connection string. MySQL: user:pass@tcp(host:3306)/db?charset=utf8mb4&parseTime=True&loc=Local; or sqlite:///xx.db
MCP_HOSTNoHost for HTTP mode.0.0.0.0
MCP_PORTNoPort for HTTP mode.8081
MCP_API_KEYNoCurrent tenant's API key. Alternative to YUNTU_API_KEY.
MCP_TRANSPORTNoTransport mode: 'stdio' (default) or 'http'.stdio
YUNTU_API_KEYNoAlternative environment variable for MCP_API_KEY.
API_KEY_ENCRYPT_SECRETYesAPI key encryption secret (>=16 characters), must match the main platform.

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}
logging
{}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
extensions
{
  "io.modelcontextprotocol/ui": {}
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
chat_completionB

调用大模型进行对话,从渠道管理中选择可用渠道并自动扣除 Token。

get_balanceA

查询当前租户的 Token 余额(需配置 MCP_API_KEY 识别租户)。

list_modelsA

列出当前可用的所有模型列表。

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

A3.9/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: chat_completion handles LLM interactions, get_balance checks token quota, and list_models shows available models. No overlap or ambiguity between them.

Naming Consistency5/5

All three tool names follow a consistent verb_noun pattern in snake_case: chat_completion, get_balance, list_models. The naming is uniform and predictable.

Tool Count4/5

With only 3 tools, the surface is minimal but appropriate for a straightforward LLM gateway. It covers the essential actions (use model, check balance, list models) without unnecessary bloat, though a few more (e.g., channel management) could be expected.

Completeness4/5

The tool set covers the core user-facing operations for an LLM gateway: chatting, checking balance, and discovering models. It lacks administrative features like managing channels, but for the apparent consumer-focused scope, these are reasonable gaps.

Maintenance

ActivityMaintained
ResponsivenessNo issues