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Glama
manish6007

Combined MCP Server

by manish6007
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
REDSHIFT_CLUSTER_IDYesRedshift cluster identifier
POSTGRES_SECRET_NAMEYesSecrets Manager secret for pgvector DB
BEDROCK_EMBEDDING_MODELYesTitan embedding model ID
KNOWLEDGEBASE_S3_BUCKETYesS3 bucket with markdown files

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
run_queryA
Execute a SQL query on Redshift.

For queries returning more than 100 rows, the full result set is stored 
in S3 and only 20 sample rows are returned.

Args:
    sql: The SQL query to execute
    db_user: Database user for authentication via get_cluster_credentials
    db_group: Optional database group for permissions
list_schemasB
List all schemas in the Redshift database.

Args:
    db_user: Database user for authentication
    db_group: Optional database group for permissions
list_tablesB
List all tables in a Redshift schema.

Args:
    schema: Schema name to list tables from
    db_user: Database user for authentication
    db_group: Optional database group for permissions
describe_tableB
Get detailed information about a Redshift table including columns and data types.

Args:
    schema: Schema name
    table: Table name
    db_user: Database user for authentication
    db_group: Optional database group for permissions
build_vectorstoreA
Build or rebuild the knowledge base vector store from S3 markdown files.

Downloads all markdown files from the configured S3 location, processes them 
into chunks, generates embeddings using AWS Bedrock Titan, and stores in PostgreSQL.
query_vectorstoreA
Search the knowledge base vector store.

Supports semantic search (vector similarity), keyword search (full-text), 
or hybrid search combining both with RRF reranking. Results are cached for performance.

Args:
    query: The search query text
    top_k: Maximum number of results to return (default: 10)
    search_type: Type of search - semantic, keyword, or hybrid (default)
get_vectorstore_statusA
Get the current status of the knowledge base vector store.

Returns build status, document count, and cache statistics.

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

A3.7/5.0

Scored across 7 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap. The vectorstore tools (build, get_status, query) handle knowledge base operations, while the Redshift tools (list_schemas, list_tables, describe_table, run_query) handle database operations. The separation between these two domains is unambiguous.

Naming Consistency5/5

All tools follow a consistent verb_noun naming pattern. The verbs are descriptive (build, describe, get, list, query, run) and the nouns clearly indicate the target resource (vectorstore, table, schemas, tables, query). There are no deviations in naming conventions.

Tool Count4/5

With 7 tools, the count is reasonable for a combined server covering two domains. However, the vectorstore side feels slightly thin with only three tools (build, status, query), lacking operations like update or delete. The Redshift side is well-covered with four tools.

Completeness4/5

For the Redshift domain, the toolset provides comprehensive coverage (list schemas/tables, describe table, run query). For the vectorstore domain, core operations exist (build, query, status), but there are minor gaps such as updating or deleting documents from the knowledge base, which agents might need to work around.

Maintenance

ActivityStale
ResponsivenessNo issues

Resources