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Glama
Nithishkaranam2002

FinRAG-MCP

by Nithishkaranam2002
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
EMBED_MODELNoThe embedding model to usetext-embedding-3-large
FINRAG_ROLENoThe role for RBAC access controlEMPLOYEE
OPENAI_MODELNoThe OpenAI model to usegpt-4o-mini
OPENAI_API_KEYYesYour OpenAI API key
QDRANT_LOCAL_PATHNoThe local path for Qdrant vector database.qdrant_local
QDRANT_COLLECTION_PREFIXNoThe prefix for Qdrant collectionsfinrag

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
set_roleB

Set the active role for this MCP session. Returns the domains the role can access.

list_filesA
List files under data/<domain>, RBAC-enforced.
Returns relative paths from repo root.
read_fileC

Read a text-like file relative to repo root, RBAC-enforced by folder domain.

searchA
RBAC-aware vector search across allowed domains/collections.
Args:
  query: natural language query
  top_k: number of results to return
  include_general: include 'general' alongside role-specific domains
Returns a list of hits with:
  score, collection, domain, source, title, doc_type, tags, chunk_id, snippet

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

A3.5/5.0

Scored across 4 tools

Disambiguation5/5

Each tool has a clear, distinct purpose: list_files for listing, read_file for reading content, search for vector search, and set_role for role management. No overlap.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (list_files, read_file, search, set_role), making predictable and easy to understand.

Tool Count4/5

With 4 tools covering file listing, reading, search, and role setting, the count is appropriate for a focused RAG server. Slightly more tools (e.g., for data management) could be justified, but current scope is reasonable.

Completeness3/5

Covers core retrieval operations (list, read, search) and role management, but lacks data ingestion, deletion, or role listing/management beyond setting. Notable gap for a full RAG workflow.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources