ats serve
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| ATS_DB | Yes | Path to the SQLite database file (traces.db) used by the ats-memory MCP server. |
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 |
|---|---|
| recallB | Retrieve relevant memories from agent session history using hybrid BM25 + semantic search. Args: query: Natural language query (e.g. "PDF rendering error", "auth bug pattern") memory_type: Optional filter — 'episodic', 'procedural', or 'preference' top_k: Number of memories to return (default 10) include_graph: Whether to expand results with graph-connected memories workspace: Optional — restrict to one project/workspace (substring match) Returns: JSON list of memories with content, type, extraction_method, and rrf_score |
| chunk_searchA | Search raw conversation chunks from agent session history using hybrid BM25 + semantic search. Args: query: Natural language query session_id: Optional — restrict to a specific session (full ID) top_k: Number of chunks to return (default 20) include_graph: Expand top hits with chunks (from any session) that mention the same entity — a cross-session hop via shared file/commit/PR/etc. workspace: Optional — restrict to one project/workspace (substring match) Returns: JSON list of chunks with chunk_text, session_id, chunk_index, and rrf_score |
| graph_walkA | Walk the session graph from a seed memory to find related memories. Args: seed_memory_id: Full memory ID to start from depth: BFS depth (1 = direct neighbours, 2 = neighbours of neighbours) Returns: JSON list of related memories |
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 3 tools
recall and chunk_search both perform hybrid search over agent session history, but they target distinct granularities (extracted memories vs raw conversation chunks). graph_walk is clearly separate. The overlap is manageable given descriptions, but an agent could still confuse the two search tools without careful reading.
All names are snake_case, but 'recall' is a bare verb while 'chunk_search' and 'graph_walk' follow a noun_verb pattern. This mixed verb style is readable but not fully consistent.
Three tools is a focused set for a memory retrieval service, and each tool has a clear role. However, it sits at the lower boundary of typical well-scoped sets and could benefit from one or two more retrieval utilities.
The surface covers search over memories and chunks plus graph expansion, but lacks a direct get-by-ID operation (problematic since graph_walk needs a seed memory ID) and session/workspace listing. These are notable gaps for a memory service, though core search workflows are covered.