The MCP Vertica server provides a local NLP-to-SQL interface for Vertica databases, running entirely locally with no authentication required. Key capabilities include:
• Execute SQL queries directly against Vertica with streaming results and batching options
• Natural language to SQL conversion using a local LLM (Ollama) for both data queries and DDL operations like table creation
• Data loading via bulk COPY operations into Vertica tables
• Database introspection to retrieve table structures, columns, types, constraints, indexes, and views
• Similar incident analysis to find incidents based on ID or text description
• Multiple access methods: REST API at http://127.0.0.1:8001/api/, terminal commands via uvx mcp-vertica nlp, and SSE MCP server on port 8000
• Dry-run capabilities to preview generated SQL without execution
• Local deployment using Docker and Ollama for the complete stack
Runs Vertica CE database via Docker containers for local development and testing environments
Integrates with Ollama's local LLM service to provide natural language to SQL query translation capabilities for Vertica databases
Provides Python-based tools for executing SQL queries, managing database schemas, copying data, and streaming query results from Vertica databases
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Vertica show me the top 10 customers by total sales this month"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
This repository provisions two isolated stacks on AWS:
DB stack (deploy/db/**) — Spot t3.xlarge Amazon Linux 2023 instance running Vertica CE via Docker (port 5433).
MCP stack (deploy/mcp/**, src/**, tests/**, Dockerfile.mcp) — Spot t3.small instance that pulls the MCP FastAPI server image from ECR and exposes port 8000.
Each stack has its own GitHub Actions workflow with dedicated remote Terraform state, fail-fast credential checks, and post-deploy smoke tests via AWS Systems Manager. Pushes scoped to one stack never trigger the other.
.
├─ deploy/
│ ├─ db/
│ │ ├─ README.md
│ │ └─ terraform/
│ │ ├─ backend-bootstrap.sh
│ │ ├─ main.tf
│ │ ├─ outputs.tf
│ │ ├─ user_data_db.sh
│ │ └─ variables.tf
│ └─ mcp/
│ ├─ README.md
│ └─ terraform/
│ ├─ backend-bootstrap.sh
│ ├─ main.tf
│ ├─ outputs.tf
│ ├─ user_data_mcp.sh
│ └─ variables.tf
├─ .github/workflows/
│ ├─ db-apply-destroy.yml
│ └─ mcp-apply-destroy.yml
├─ src/mcp_vertica/
│ ├─ __init__.py
│ └─ server.py
├─ Dockerfile.mcp
├─ tests/
│ ├─ test_health.py
│ └─ test_sql_rendering.py
├─ PROMPTS.md
├─ pyproject.toml
├─ uv.lock
└─ docker-compose.ymlRelated MCP server: Teradata MCP Server
Set these under Settings → Secrets and variables → Actions before running any workflow:
AWS_REGION (default ap-south-1)
AWS_ACCOUNT_ID
Either OIDC: AWS_ROLE_TO_ASSUME and AWS_OIDC_ROLE_SESSION_NAME, or static keys: AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY
Optional:
ALLOWED_CIDRS — comma-separated IPv4 CIDRs (e.g. "49.37.x.x/32","122.166.x.x/32") to open ports 5433/8000 only to those networks
MCP_HTTP_TOKEN — if set, the MCP HTTP server requires Authorization: Bearer <token>
Triggered by pushes to deploy/db/** or manual workflow_dispatch.
Bootstraps the Terraform backend (vertica-mcp-tf-<account>-<region> bucket + DynamoDB lock table).
Applies Terraform with defaults: Spot t3.xlarge, 50 GiB gp3 volume, Vertica CE image 957650740525.dkr.ecr.ap-south-1.amazonaws.com/vertica-ce:v1.0.
Runs /usr/local/bin/db-smoke.sh through SSM (executes SELECT NOW(); via vsql).
Job summary prints the public IP and a copy/paste connection string (HOST=<ip> PORT=5433 USER=dbadmin DB=VMart).
Destroy by dispatching the workflow with action=destroy.
Triggered by pushes to deploy/mcp/**, src/**, tests/**, or Dockerfile.mcp.
Runs uv sync --frozen, ruff, and pytest before touching AWS.
Builds Dockerfile.mcp, pushes to mcp-vertica ECR repo, then applies Terraform for the MCP EC2 instance.
Terraform reads the DB stack’s remote state to populate DB_HOST and writes /opt/mcp.env for the container.
Smoke test hits GET /healthz via SSM; summary prints the MCP URL (http://<ip>:8000).
Destroy by dispatching with action=destroy.
The MCP FastAPI server (src/mcp_vertica/server.py) supports both stdio and HTTP transports. Environment variables at startup:
DB_HOST, DB_PORT (default 5433), DB_USER, DB_PASSWORD, DB_NAME
Optional MCP_HTTP_TOKEN enabling bearer-token auth
Endpoints:
GET /healthz
POST /api/render
POST /api/query
For Claude Desktop (local stdio):
{
"mcpServers": {
"vertica-local": {
"command": "uvx",
"args": ["mcp-vertica", "--transport", "stdio"]
}
}
}For remote HTTP (beta):
{
"mcpServers": {
"vertica-remote": {
"command": "npx",
"args": ["-y", "mcp-remote", "http://<MCP-PUBLIC-IP>:8000/sse"],
"env": {
"AUTH_HEADER": "Authorization: Bearer <MCP_HTTP_TOKEN>"
}
}
}
}uv sync --frozen
uv run ruff check
uv run pytest -q
MCP_HTTP_TOKEN=local DB_HOST=localhost DB_PORT=5433 DB_USER=dbadmin DB_NAME=VMart \
docker compose up --build
./scripts/wait-for-port.py localhost 8000 --timeout 120
curl -H "Authorization: Bearer local" http://127.0.0.1:8000/healthzDestroy AWS resources when idle to minimize costs; both stacks default to Spot instances with security-group ingress restricted to ALLOWED_CIDRS.
Copy data into a Vertica table using COPY command.
Args:
ctx: FastMCP context for progress reporting and logging
schema: vertica schema to execute the copy against
table: Target table name
data: List of rows to insert
Returns:
Status message indicating success or failure
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| schema | Yes | ||
| table | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'progress reporting and logging' via the ctx parameter, which adds some context, but fails to disclose critical traits: whether this is a read/write operation (implied write from 'copy'), potential side effects (e.g., table locking, data overwriting), error handling, or performance considerations. For a data mutation tool with zero annotation coverage, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and concise: a clear purpose statement followed by Args and Returns sections. Every sentence adds value, with no redundant information. It could be slightly more front-loaded by emphasizing the core action earlier, but overall it's efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a data mutation tool with 3 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain return values beyond 'status message' (no format or examples), error conditions, or behavioral nuances like transaction handling. For a tool that modifies database state, this leaves significant gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the schema provides no parameter descriptions. The description compensates by listing parameters (ctx, schema, table, data) with brief explanations, adding meaning beyond the bare schema. However, it doesn't detail formats (e.g., data structure, schema/table naming rules) or constraints, leaving gaps. With 3 parameters and partial coverage, a baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Copy data into a Vertica table using COPY command.' It specifies the verb ('copy'), resource ('data'), and target ('Vertica table'), though it doesn't explicitly differentiate from sibling tools like execute_query (which might also insert data). The mention of 'COPY command' adds technical specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like execute_query (which could potentially insert data via SQL), nor does it specify prerequisites or contexts for usage. The agent must infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Execute a SQL query and return the results.
Args:
ctx: FastMCP context for progress reporting and logging
query: SQL query to execute
database: Optional database name to execute the query against
Returns:
Query results as a string
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions that results are returned as a string, it doesn't cover critical aspects like whether the query is read-only or can modify data, authentication requirements, error handling, performance implications, or rate limits. For a SQL execution tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized, with clear sections for Args and Returns. Each sentence adds value without redundancy. However, the inclusion of 'ctx' in the Args section without explanation slightly reduces efficiency, as it doesn't clarify its purpose to the agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of executing SQL queries, the lack of annotations, no output schema, and incomplete parameter documentation, the description is insufficient. It doesn't address safety concerns (e.g., read vs. write operations), result formatting beyond 'string', or error scenarios. For a tool that could potentially modify data or return complex results, this leaves too many unknowns for reliable agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description lists three parameters (ctx, query, database), but the input schema only documents one (query) with 0% schema description coverage. The description adds some semantic context by explaining that 'query' is a SQL query and 'database' is optional, but it doesn't specify format, constraints, or what 'ctx' entails. Since schema coverage is low, the description partially compensates but doesn't fully bridge the gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Execute a SQL query and return the results.' It specifies the verb (execute) and resource (SQL query), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like stream_query or get_table_structure, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like stream_query, list_views, and get_table_structure available, there's no indication of when execute_query is appropriate versus when other tools might be better suited. The absence of usage context leaves the agent without decision-making criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the structure of a table including columns, data types, and constraints.
Args:
ctx: FastMCP context for progress reporting and logging
table_name: Name of the table to inspect
schema: Schema name (default: public)
Returns:
Table structure information as a string
| Name | Required | Description | Default |
|---|---|---|---|
| schema | No | public | |
| table_name | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns 'Table structure information as a string,' but doesn't describe format details, error handling, permissions required, or whether it's a read-only operation. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized. It front-loads the purpose in the first sentence, followed by clear sections for Args and Returns. There's minimal waste, though the Args section could be more integrated into the flow. Overall, it's efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description provides basic purpose and parameter info but lacks behavioral context. It covers the core functionality but doesn't address error cases, return format details, or usage relative to siblings. For a tool with 2 parameters and no structured support, this is minimally adequate but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It documents both parameters (table_name and schema) in the Args section, explaining their purposes. However, it doesn't add meaning beyond basic definitions (e.g., format constraints or examples). With two parameters fully listed but no rich details, this meets the baseline for adequate coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get the structure of a table including columns, data types, and constraints.' It specifies the verb ('Get'), resource ('structure of a table'), and scope ('including columns, data types, and constraints'). However, it doesn't explicitly differentiate from sibling tools like list_indexes or list_views, which might also provide structural information about database objects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like list_indexes or list_views, nor does it specify prerequisites or exclusions (e.g., when table_name is invalid). Usage is implied from the purpose statement but lacks explicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all indexes for a specific table.
Args:
ctx: FastMCP context for progress reporting and logging
table_name: Name of the table to inspect
schema: Schema name (default: public)
Returns:
Index information as a string
| Name | Required | Description | Default |
|---|---|---|---|
| schema | No | public | |
| table_name | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool returns 'Index information as a string' but doesn't specify format, structure, or what happens with invalid inputs (e.g., non-existent tables). For a read operation with zero annotation coverage, this leaves significant behavioral questions unanswered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (Args, Returns) and uses minimal sentences. However, the inclusion of 'ctx' in Args (which appears to be an internal framework parameter not in the actual input schema) adds slight unnecessary complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read operation with 2 parameters and no output schema, the description covers the basics but lacks depth. It doesn't explain what 'Index information' includes (e.g., index names, columns, types) or handle edge cases, making it minimally adequate but with clear gaps for practical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explicitly documents both parameters (table_name and schema) with brief explanations, though schema coverage in the input schema is 0%. It adds meaningful context beyond the bare schema, particularly noting the default value for schema, but doesn't elaborate on constraints or examples for table_name format.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and the resource 'all indexes for a specific table', making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_table_structure', which might also provide index information as part of table metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_table_structure' or 'execute_query' for index-related queries. It states what the tool does but offers no context about when it's the appropriate choice among available options.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all views in a schema.
Args:
ctx: FastMCP context for progress reporting and logging
schema: Schema name (default: public)
Returns:
View information as a string
| Name | Required | Description | Default |
|---|---|---|---|
| schema | No | public |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool returns 'View information as a string' but doesn't specify format, structure, or any behavioral traits like error handling, performance characteristics, or whether it's read-only (though implied by 'List'). This leaves significant gaps for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and well-structured with clear sections for Args and Returns. It uses minimal sentences to convey essential information without unnecessary elaboration, though the 'ctx' parameter documentation is somewhat vague ('FastMCP context for progress reporting and logging').
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is adequate but has clear gaps. It explains the parameter and return type but lacks details on output format, error conditions, or usage context relative to siblings. For a simple list tool, this is minimally viable but could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context for the single parameter 'schema' by explaining it's the 'Schema name' with a default of 'public'. Since schema description coverage is 0% (the input schema only provides title and type without description), this compensates well by clarifying what the parameter represents and its default behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and resource 'views in a schema', making the purpose specific and understandable. However, it doesn't explicitly distinguish this tool from sibling tools like 'list_indexes' or 'get_table_structure', which might also list database objects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There's no mention of when this tool is appropriate compared to sibling tools like 'list_indexes' or 'get_table_structure', nor any prerequisites or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Execute a SQL query and return the results in batches as a single string.
Args:
ctx: FastMCP context for progress reporting and logging
query: SQL query to execute
batch_size: Number of rows to fetch at once
Returns:
Query results as a concatenated string
| Name | Required | Description | Default |
|---|---|---|---|
| batch_size | No | ||
| query | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly describes the batching behavior and output format (concatenated string), which are valuable. However, it doesn't mention critical aspects like error handling, performance implications, memory usage with large results, or whether the query is read-only or can modify data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with a clear purpose statement followed by organized sections for Args and Returns. Every sentence adds value: the first explains the core functionality, and the subsequent lines document parameters and return value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (SQL execution with batching), lack of annotations, and no output schema, the description is moderately complete. It covers the basic operation and parameters but misses important context like error conditions, performance characteristics, and how it differs from sibling tools. The return format is described, but without an output schema, details about the concatenated string structure are lacking.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant semantic value beyond the input schema, which has 0% description coverage. It explains that 'batch_size' controls 'Number of rows to fetch at once' and 'query' is the 'SQL query to execute', providing context that the bare schema lacks. The 'ctx' parameter is also documented in the description but missing from the schema, though its purpose is somewhat vague.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Execute a SQL query and return the results in batches as a single string.' It specifies the verb (execute), resource (SQL query), and key behavioral characteristic (batched streaming). However, it doesn't explicitly differentiate from sibling tools like 'execute_query', which likely has similar functionality without batching.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'execute_query' and 'copy_data' available, there's no indication of when batch streaming is preferred over other query execution methods, nor any mention of prerequisites or constraints.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0copy_dataexecute_queryget_table_structurelist_indexeslist_viewsstream_queryEach tool has a clearly distinct purpose with no ambiguity: copy_data handles data insertion, execute_query and stream_query handle query execution with different output methods, get_table_structure inspects table schema, list_indexes lists indexes, and list_views lists views. The descriptions clearly differentiate their functions, making misselection unlikely.
All tools follow a consistent verb_noun naming pattern (copy_data, execute_query, get_table_structure, list_indexes, list_views, stream_query). The naming is uniform throughout, using snake_case and clear action-object pairs, making the tool set predictable and easy to understand.
With 6 tools, the count is well-scoped for a database server, covering core operations like querying, data insertion, and schema inspection. It feels slightly lean but reasonable, as it includes essential functions without being overwhelming. A few more tools for updates or deletions might enhance coverage, but it's not a significant gap.
The tool set covers query execution, data copying, and schema inspection well, but there are notable gaps in CRUD operations. It lacks tools for updating or deleting data, creating or modifying tables, or managing users/roles. This could cause agent failures for common database workflows beyond basic querying and insertion.
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