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Glama

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
{}
resources
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
ingest_docsA

Re-ingest documents from the configured documents directory. Use this if search returns no results or if documents have been updated

searchB

Search for relevant document chunks using semantic similarity

get_chunkC

Retrieve a specific document chunk by its ID

refresh_indexB

Clear and refresh the entire document index

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription
Collection SummarySummary of the document collection including statistics and available sources

TDQS

A3.5/5.0

Scored across 4 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: get_chunk retrieves a specific chunk, ingest_docs re-ingests documents, refresh_index clears and rebuilds the index, and search performs semantic similarity queries. The descriptions make it easy to differentiate between retrieval, ingestion, index management, and search operations.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., get_chunk, ingest_docs, refresh_index, search), using snake_case throughout. The naming is predictable and readable, with no deviations in style or convention across the set.

Tool Count4/5

With 4 tools, the count is reasonable for a RAG server's core operations, covering ingestion, indexing, retrieval, and search. It feels slightly thin but well-scoped, as each tool earns its place without bloat, though additional utilities like document deletion or status checks might be considered minor gaps.

Completeness4/5

The toolset covers essential RAG workflows: ingestion (ingest_docs), index management (refresh_index), retrieval (get_chunk), and search (search). Minor gaps exist, such as no explicit update or delete operations for documents or chunks, but agents can work around this by re-ingesting or refreshing the index as needed.

Maintenance

ActivityInactive
ResponsivenessNo issues