# Ollama / vLLM Bridge (pypi · mcp-ollama-vllm)

Call your local Ollama or vLLM model over MCP with schema-validated JSON output

- Trust score: 73/100 (medium)
- Change this week: +18
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-09-21

## Components

- pypi · `mcp-ollama-vllm`: 73/100 (this document), [markdown](https://verifymcp.io/servers/setheerwagen-mcp-ollama-vllm/mcp-ollama-vllm.md), [page](https://verifymcp.io/servers/setheerwagen-mcp-ollama-vllm/mcp-ollama-vllm)

## Channel facts

- Registry: `pypi`
- Package: `mcp-ollama-vllm`
- Version: `1.0.2`
- Transport: `stdio`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-09-21.

- **Supply Chain Security**: 100/100
  - No malware found by supply-chain analysis.
  - No known CVEs affecting this package version or its production dependencies.
  - Runs setuptools.build_meta at install time, a recognised native-build step with no shell scripting around it.
  - 0 of 29 dependencies flagged as unhealthy.
- **Provenance & Transparency**: 35/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - License check failed: no license is declared.
  - Actively maintained (last published 12 days ago).
  - Publishes a security disclosure policy (SECURITY.md).
- **Schema Quality & AI Usability**: 66/100
  - AI-judged instruction clarity (good).
  - Context-footprint check failed: tool/resource definitions use about 741 tokens (~185/item across 4 items; 4 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 63/100
  - Stability observed for 19 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 71/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 0% of tool parameters carry a description.
  - Structured output schemas are declared (100% of tools); any adoption earns full credit.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 4 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 5 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### How do I install the Ollama / vLLM Bridge MCP server?

Ollama / vLLM Bridge runs locally as a PyPI package, launched with uvx mcp-ollama-vllm. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

### Claude

```bash
claude mcp add setheerwagen-mcp-ollama-vllm -- uvx mcp-ollama-vllm
```

### Cursor

```json
{
  "mcpServers": {
    "setheerwagen-mcp-ollama-vllm": {
      "command": "uvx",
      "args": [
        "mcp-ollama-vllm"
      ]
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "setheerwagen-mcp-ollama-vllm": {
      "command": "uvx",
      "args": [
        "mcp-ollama-vllm"
      ]
    }
  }
}
```

### Codex

```bash
codex mcp add setheerwagen-mcp-ollama-vllm -- uvx mcp-ollama-vllm
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "setheerwagen-mcp-ollama-vllm": {
      "type": "local",
      "command": [
        "uvx",
        "mcp-ollama-vllm"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add setheerwagen-mcp-ollama-vllm --command uvx --arg mcp-ollama-vllm
```

### Hermes

```yaml
mcp_servers:
  setheerwagen-mcp-ollama-vllm:
    command: "uvx"
    args: ["mcp-ollama-vllm"]
```

### Netclaw

```json
{
  "McpServers": {
    "setheerwagen-mcp-ollama-vllm": {
      "Transport": "stdio",
      "Command": "uvx",
      "Arguments": [
        "mcp-ollama-vllm"
      ]
    }
  }
}
```

### Vellum

```bash
assistant mcp add setheerwagen-mcp-ollama-vllm -t stdio -c uvx -a mcp-ollama-vllm
```

### Other

```json
{
  "mcpServers": {
    "setheerwagen-mcp-ollama-vllm": {
      "command": "uvx",
      "args": [
        "mcp-ollama-vllm"
      ]
    }
  }
}
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-09-19 (score 73, +1)

No change was recorded against any check on this day. Stability & Change Management went from 53 to 57. That category is still filling its 30-day observation window: 16 days of observed history at the previous scan, 17 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-17 (score 72, +1)

No change was recorded against any check on this day. Stability & Change Management went from 47 to 50. That category is still filling its 30-day observation window: 14 days of observed history at the previous scan, 15 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-15 (score 71, +16)

- [security improvement] Malware scan: unverified → pass

### 2026-09-14 (score 55, −15)

- [security regression] Malware scan: pass → unverified

### 2026-09-13 (score 70, +1)

No change was recorded against any check on this day. Stability & Change Management went from 33 to 37. That category is still filling its 30-day observation window: 10 days of observed history at the previous scan, 11 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-11 (score 69, +1)

No change was recorded against any check on this day. Stability & Change Management went from 27 to 30. That category is still filling its 30-day observation window: 8 days of observed history at the previous scan, 9 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-09 (score 68, +1)

No change was recorded against any check on this day. Stability & Change Management went from 20 to 23. That category is still filling its 30-day observation window: 6 days of observed history at the previous scan, 7 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-08 (score 67, +2)

- [functional improvement] Stability: unverified → 0.20
- [functional] Package version: 1.0.1 → 1.0.2

## MCP tools (4)

### `list_models` (~84 tokens)

Lists the models available on the local instance.

Returns per model the name, parameter size, quantization and on-disk size,
as far as the backend reports them. With vLLM it additionally shows whether
an entry is a LoRA adapter and which base model it belongs to. Sensible
before any other tool, to pick a fitting and actually present model name.

### `local_ask` (~247 tokens)

Asks a local model a question and returns the answer as text.

For free-text tasks: writing, rewriting, summarizing, explaining. If a
machine-processable result is needed, use 'local_structured' instead.

Args:
    model: Model name as in 'list_models', for example 'llama3.2:3b'.
    prompt: The actual instruction for the model.
    system: Optional role/behavior instruction.
    temperature: 0 means as deterministic as possible (default), higher means more creative.
    max_tokens: Optional cap on the number of generated tokens.
    context: Optional text prepended to the prompt (source, excerpt, data).
    think: Enables the model's reasoning trace. Off by default, because the
        reasoning trace otherwise consumes the answer's token budget (with a
        tight 'max_tokens' the answer then comes back empty). Only for models
        with the 'thinking' capability.

Input parameters:

- `context`
- `max_tokens`
- `model` (string, required)
- `prompt` (string, required)
- `system`
- `temperature` (number)
- `think` (boolean)

### `local_structured` (~245 tokens)

Has a local model return a result that conforms to a JSON schema.

Uses the respective backend's schema enforcement (Ollama's 'format' field,
vLLM's 'response_format' with 'json_schema') and then additionally validates
the answer against the schema itself. If the output does not satisfy the
schema, it retries up to twice, passing the model the concrete violations.
Only then an error, but then with the invalid raw output, so it is visible
what went wrong.

Args:
    model: Model name, for example 'llama3.2:3b'.
    prompt: Instruction on what should be extracted from which text.
    schema: JSON schema of the desired result (object with 'type', 'properties', ...).
    system: Optional role/behavior instruction.
    think: Enables the model's reasoning trace. Off by default, because the
        reasoning trace otherwise consumes the answer's token budget. Only
        for models with the 'thinking' capability.

Input parameters:

- `model` (string, required)
- `prompt` (string, required)
- `schema` (object, required)
- `system`
- `think` (boolean)

### `local_embed` (~83 tokens)

Computes embedding vectors for a list of texts.

Useful for similarity comparisons, duplicate detection or a rough sort by
topic, without troubling a large language model.

Args:
    texts: List of texts to embed.
    model: Embedding model, with Ollama preset to 'nomic-embed-text'.

Input parameters:

- `model` (string)
- `texts` (array, required)

## Diagnostics

Captured diagnostic sections: Provenance, Install scripts, Dependencies. The full working is on the page: https://verifymcp.io/servers/setheerwagen-mcp-ollama-vllm/mcp-ollama-vllm#diagnostics

## Score history

- 2026-09-21: 73
- 2026-09-20: 73
- 2026-09-19: 73
- 2026-09-18: 72
- 2026-09-17: 72
- 2026-09-16: 71
- 2026-09-15: 71
- 2026-09-14: 55
- 2026-09-13: 70
- 2026-09-12: 69
- 2026-09-11: 69
- 2026-09-10: 68
- 2026-09-09: 68
- 2026-09-08: 67
- 2026-09-07: 65
- 2026-09-06: 65
- 2026-09-05: 65
- 2026-09-04: 65
- 2026-09-03: 65
- 2026-09-02: 50

## Common questions

### What is the Ollama / vLLM Bridge MCP server?

Ollama / vLLM Bridge is an MCP server listed in the public MCP registry as io.github.setheerwagen/mcp-ollama-vllm. Call your local Ollama or vLLM model over MCP with schema-validated JSON output. This page covers its PyPI package (mcp-ollama-vllm).

### Is the Ollama / vLLM Bridge MCP server safe to use?

Ollama / vLLM Bridge scores 73 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 21 September 2026. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

### What tools does the Ollama / vLLM Bridge MCP server expose?

Ollama / vLLM Bridge exposes 4 tools: list_models, local_ask, local_structured, local_embed. Their descriptions and schemas cost roughly 659 tokens of context every time the server is loaded.

### Is the Ollama / vLLM Bridge MCP server still maintained?

Ollama / vLLM Bridge is still listed as active in the MCP registry. We last reached this channel on 21 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

## Links

- PyPI project: https://pypi.org/project/mcp-ollama-vllm/
- Socket report: https://socket.dev/pypi/package/mcp-ollama-vllm
- Repository: https://github.com/setheerwagen/local-ai-mcp-servers
- Changelog RSS feed: https://verifymcp.io/servers/setheerwagen-mcp-ollama-vllm/mcp-ollama-vllm.xml
- Changelog JSON feed: https://verifymcp.io/servers/setheerwagen-mcp-ollama-vllm/mcp-ollama-vllm.json
- HTML version of this page: https://verifymcp.io/servers/setheerwagen-mcp-ollama-vllm/mcp-ollama-vllm