Skip to main content
Glama
renl

mcp-rag-local

by renl
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
OLLAMA_PORTNoPort for Ollama server (default 11434)
CHROMADB_PORTNoPort for ChromaDB server (default 8321)

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
memorize_pdf_fileA

Chunk the contents of a PDF file into meaningful segments and store them in memory for later retrieval based on relevance in meaning, not just keywords.

Args:
    ctx (Context): The context of the request.
    file_path (str): The path to the PDF file.
    page (int, optional): The starting page number to read from the PDF file. Defaults to 0.
    metadata (dict, optional): Metadata to associate with the memorized content.

Returns:
    str: A message indicating success or failure of the operation.
greet_userC

Greet the user with their name and the server's name.

Returns:
    str: A greeting message.
memorize_multiple_textsB

Memorize multiple texts for later retrieval based on relevance in meaning, not just keywords.

Args:
    texts (list): A list of texts to memorize.
Returns:
    str: A message indicating success or failure of the operation.
memorize_textC

Memorize a text for later retrieval based on relevance in meaning, not just keywords. 

Args:
    text (str): The text to memorize.
Returns:
    str: A message indicating success or failure of the operation.
remember_similar_textsA

Query memory for texts similar in meaning to the query text.

Args:
    query_text (str): The text to find similar meanings for.
    n_results (int): The number of results to return. This is recommended to be more than 10.
Returns:
    str: A human-readable string with the results and their relevance.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
Context All Your Agents Can ShareAgents share what they learn, hand off work, and hold it when you switch models. Start with 5 GB free.fulcradynamics.comAd

TDQS

B3.3/5.0

Scored across 5 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: greeting, memorizing text, memorizing multiple texts, memorizing PDF files, and retrieving similar texts. No ambiguity between tools.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with verb_noun or verb_adjective_noun structure (e.g., greet_user, memorize_text, remember_similar_texts).

Tool Count4/5

5 tools is appropriately scoped for a simple RAG local server, covering core operations without being overwhelming. Slightly on the low side but reasonable.

Completeness3/5

The tool set provides storage and retrieval but lacks update, delete, or listing operations, which are common in memory systems. This could limit agent capabilities.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources