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Glama
e-x-h-i-b-i-t

turbocontext

by e-x-h-i-b-i-t
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

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": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
index_workspaceB
Recursively finds code files in directory_path and indexes them using Tree-sitter AST chunking,
FastEmbed vector generation, and Turbovec storage. Skips unchanged files automatically unless force=True.
search_codeA
Embeds the search query using FastEmbed, retrieves candidate chunks from Turbovec vector index,
reranks them using Cross-Encoder, and returns top_k matching code/memory chunks (default top_k=3).
add_memoryC

Shortcut tool to store a plain-text memory or note into the index without needing a file path.

get_statusA

Returns real-time status and statistics about the Turbocontext vector store, database size, chunk counts, unique files, stored memories, and model metadata.

clear_workspaceC
Purges all indexed code chunks, stored memories, and file hashes for workspace_id from storage.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.6/5.0

Scored across 5 tools

Disambiguation5/5

Each tool targets a distinct operation: indexing code files, searching the index, adding plain-text memories, getting status, and clearing the workspace. There is no functional overlap.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., index_workspace, search_code). The naming is predictable and easy to understand.

Tool Count5/5

With 5 tools, the server covers the core workflow of indexing, searching, and managing a code/memory vector store without being overly sparse or bloated.

Completeness4/5

The tool set covers indexing, searching, memory addition, status, and full clearing. A minor gap is the lack of granular deletion (e.g., removing specific files or memories without clearing everything), but for most use cases this is sufficient.

Maintenance

ActivityMaintained
ResponsivenessSyncing

Resources