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Glama

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
SHUNT_MODELNoModel to use. Default is 'auto' which automatically selects the best model for the task.
GROQ_API_KEYNoAPI key for Groq.
SHUNT_API_KEYNoGeneric API key for custom or fallback providers.
GEMINI_API_KEYNoAPI key for Google Gemini.
OPENAI_API_KEYNoAPI key for OpenAI.
SHUNT_BASE_URLNoBase URL for the worker endpoint (required for Ollama and custom providers). Default is provider-specific.
SHUNT_PROVIDERNoThe worker model provider. One of: gemini, groq, ollama, deepseek, openai, openrouter, anthropic, or a custom provider via SHUNT_BASE_URL.
DEEPSEEK_API_KEYNoAPI key for DeepSeek.
ANTHROPIC_API_KEYNoAPI key for Anthropic.
SHUNT_ALLOWED_ROOTSNoPATH-style list of allowed root directories for file access. Defaults to the server's working directory.

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
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
bulk_readA

Reads multiple or large files and answers a targeted question using a cheap, fast worker model (e.g. Gemini Flash, Groq, Ollama). Saves ~90% tokens by returning only structured bullet points.

code_writeA

Generates boilerplate code (tests, mocks, stubs, configs) matching the patterns of a reference file. Can write directly to disk without consuming frontier output tokens.

get_available_modelsA

Discovers active models from the worker provider and recommends the best model for reading (high context / low cost) and writing (code intelligence). Enables calling agents to delegate dynamically to the best model.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

A3.8/5.0

Scored across 3 tools

Disambiguation4/5

The three tools have distinct purposes: bulk_read handles file reading and summarization, code_write handles code generation, and get_available_models handles model discovery. There is minor potential confusion between bulk_read and code_write since both involve file operations, but their core functions are clearly separated.

Naming Consistency3/5

Tool names use a mix of conventions: bulk_read and code_write follow a noun_verb pattern, while get_available_models follows a verb_adjective_noun pattern. The naming is readable and descriptive, but the inconsistent verb placement (bulk_read vs get_available_models) creates minor inconsistency.

Tool Count3/5

Three tools is on the low end but appropriate for a focused utility server that handles delegated reading, writing, and model selection. The count feels slightly thin for a server that could benefit from additional tools like a direct file write or model configuration tool, but it is not unreasonable.

Completeness3/5

The server covers the core workflow of reading files, generating code, and selecting models, but there are notable gaps. There is no tool for direct file writing (code_write writes to disk but only for boilerplate), no tool for updating existing code, and no way to configure or manage the worker provider beyond model discovery.

Maintenance

ActivityMaintained
ResponsivenessNo issues