ai-mcp-server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| AI_MCP_DB_PATH | No | SQLite database path. Default: $AI_MCP_CONFIG_DIR/db.sqlite3 | $AI_MCP_CONFIG_DIR/db.sqlite3 |
| AI_MCP_UI_TOKEN | No | Access token for Web UI when exposed (--expose). Default: none | |
| AI_MCP_CONFIG_DIR | No | Override data/config directory. Default: ~/.ai-mcp-server | ~/.ai-mcp-server |
| AI_MCP_MASTER_KEY | No | Fernet master key for api_key encryption. Auto-generated to system keyring if not set. |
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
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| usage_guideA | Return current capability inventory and usage instructions. Call this first whenever you connect. |
| list_modelsA | List models matching the filters. Args: capability: capability tags the model must support (e.g. ["vision"]). min_context_length: minimum context window in tokens. endpoint: limit to a single endpoint name. include_unprobed: include models whose capabilities have not been probed yet (default True). |
| invoke_modelA | Forward a request to the selected (endpoint, model). Args:
endpoint: endpoint name registered via the CLI.
model: model_id as returned by list_models.
operation: one of chat / embedding / image_gen / tts / stt / rerank.
payload: upstream-compatible body (OpenAI shape for openai-compat
endpoints). The |
| model_performanceB | Return近3天 aggregated call metrics per model (background-updated). Args: endpoint: limit to a single endpoint name. sort_by: one of call_count / success_count / avg_first_byte_ms / avg_prompt_tokens / avg_output_tokens. limit: max rows to return. Each row includes call_count, success_count, success_rate, avg_first_byte_ms, avg_prompt_tokens, avg_output_tokens, window_days. |
| refresh_endpointA | Enqueue probe jobs. Server-internal worker will drain them. Args: endpoint: endpoint name; if None, refresh every endpoint. capabilities: list of capability tags; if None, choose probes per model using known metadata. refresh_model_list: re-fetch /v1/models first (default True). |
| add_modelsA | Manually register models or user-confirmed model features. Args: endpoint: endpoint name. model_ids: one or more model_id to register. context_length: optional context window in tokens. capabilities: optional capability tags to mark as supported (override source). Aliases tts/stt/asr are accepted. feature_overrides: optional key/value overrides. Keys may be capability tags or context_length; capability values must be booleans. Example: {"audio_tts": true, "context_length": 32000}. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 6 tools
Each tool has a clear and distinct purpose: registration, inference, listing, metrics, probing, and guidance. No overlap in functionality.
All tools use snake_case, but not all follow verb_noun pattern strictly (e.g., model_performance, usage_guide are noun_noun). Still clear and consistent in style.
6 tools is well-scoped for an AI model server, covering registration, inference, listing, performance, probing, and guidance. Not too few or too many.
Covers core workflows (register, list, invoke, metrics, update via probe, guidance). Missing explicit delete or update tool for models, but add_models can override features, partially filling that gap.