Ollama / vLLM Bridge
PYPI · MCP-OLLAMA-VLLM
Call your local Ollama or vLLM model over MCP with schema-validated JSON output
Available components
Last scanned (4 hours ago)
Scores higher than 76% of PyPI servers
Category AI and Machine Learning
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. How we score → Why this is hard to score →
Supply Chain Security100
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs setuptools.build_meta at install time, a recognised build step with no custom scripting around it. View diagnostics → Pass
- 0 of 28 dependencies flagged as unhealthy. View diagnostics → Pass
Provenance & Transparency48
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 13 days ago).Pass
- Publishes a security disclosure policy (SECURITY.md).Pass
Schema Quality & AI Usability66
- AI-judged instruction clarity (good).Pass
- 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. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management77
- Stability observed for 23 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage71
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 0% of tool parameters carry a description.Fail
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 4 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 5 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 5 Nov 26 −3
- Stability: pass → 0.77 functional
- 30 Oct 26 0
- Stability: 0.97 → pass security
- 29 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 93 to 97. That category is still filling its 30-day observation window: 28 days of observed history at the previous scan, 29 at this one. The score rises as the window fills, whether or not the server changes.
- 28 Oct 26 −1
- Stability: pass → 0.93 functional
- 25 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- 22 Oct 26 +5
- CVE-2026-59951 no longer affects this package ▲ security
- Known CVEs: fail → pass ▲ security
- Package version: 1.0.3 → 1.0.6 functional
- 16 Oct 26 −5
- CVE-2026-59951 affects this package: high ▼ security
- Known CVEs: pass → fail ▼ security
- Stability: 0.97 → pass security
- 15 Oct 26 +41
- Injection markers: unverified → pass ▲ security
- Tool coverage: unverified → 100 ▲ functional
- MCP protocol: unverified → pass ▲ functional
- Stability: unverified → 0.97 ▲ functional
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 5 Nov 2026 · Analysed pypi/mcp-ollama-vllm@1.0.6
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
| Reason | No attestation published |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | setuptools.build_meta |
Background: Why install scripts are a supply-chain risk →
Dependencies 28 packages
| Packages resolved | 28 |
|---|---|
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
list_models ~84
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.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
local_ask ~247
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.
| Name | Type | Req | Description |
|---|---|---|---|
| context | – | – | – |
| max_tokens | – | – | – |
| model | string | yes | – |
| prompt | string | yes | – |
| system | – | – | – |
| temperature | number | – | – |
| think | boolean | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
local_embed ~83
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'.
| Name | Type | Req | Description |
|---|---|---|---|
| model | string | – | – |
| texts | array | yes | – |
Structured output declared, but exposes no named fields.
No examples provided.
local_structured ~245
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.
| Name | Type | Req | Description |
|---|---|---|---|
| model | string | yes | – |
| prompt | string | yes | – |
| schema | object | yes | – |
| system | – | – | – |
| think | boolean | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
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.
pypi · mcp-ollama-vllm
claude mcp add setheerwagen-mcp-ollama-vllm -- uvx mcp-ollama-vllm
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 78 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 5 November 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 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the Ollama / vLLM Bridge MCP server under?
Ollama / vLLM Bridge declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.