io.github.gabrielmahia/offline-mcp
PYPI · OFFLINE-MCP
Local AI over Ollama for low connectivity: status, local inference, model and deployment guides.
Available components
Last scanned (22 hours ago)
Scores higher than 80% 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 Security50
- Malware scan not yet available for this package.Unverified
- 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
- 1 of 15 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency100
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Cryptographically verified build provenance (signed, bound to gabrielmahia/offline-mcp). View diagnostics → Pass
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 1 days ago).Pass
- Publishes a security disclosure policy (SECURITY.md).Pass
Schema Quality & AI Usability74
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 865 tokens (~144/item across 6 items; 6 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 Management100
- No destabilizing schema changes in the last 30 days.Pass
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
- 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 6 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 7 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (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.
- 2 Nov 26 −15
- Malware scan: pass → unverified ▼ security
- Package version: 0.1.10 → 0.1.11 functional
- 1 Nov 26 +15
- Malware scan: unverified → pass ▲ security
- 31 Oct 26 −15
- Malware scan: pass → unverified ▼ security
- Package version: 0.1.7 → 0.1.10 functional
- 30 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- Stability: 0.97 → pass security
- 29 Oct 26 −15
- Malware scan: pass → unverified ▼ security
- Stability: pass → 0.97 functional
- 24 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- 23 Oct 26 −15
- Malware scan: pass → unverified ▼ security
- Stability: 0.97 → pass security
- Package version: 0.1.9 → 0.1.10 functional
- 22 Oct 26 +25
- Malware scan: unverified → pass ▲ security
- Provenance: unverified → pass ▲ security
- The attested source repository moved: gabrielmahia/offline-mcp security
- Stability: pass → 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 4 Nov 2026 · Analysed pypi/offline-mcp@0.1.11
Provenance Verified
A signed build attestation was found and verified, binding this exact artifact to the source repository it claims to come from.
| Result | Verified |
|---|---|
| Ecosystem | pypi |
| Reason | Verified |
| Discovered via | Registry attestation endpoint |
| Source repo | gabrielmahia/offline-mcp |
| Certificate issuer | https://token.actions.githubusercontent.com |
| Certificate SAN | https://github.com/gabrielmahia/offline-mcp/.github/workflows/publish.yml@refs/tags/v0.1.11 |
| Rekor log index | 3642063156 |
| Predicate type | PyPI publish attestation https://docs.pypi.org/attestations/publish/v1 |
| Subject digest | sha256:c6bb334aecda0d7cb9fca06e3b815e665c5d9753d96e0828d2c119bbf608b644 |
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 15 packages
| Packages resolved | 15 |
|---|---|
| Stale | 1 |
| 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 →
check_ollama_status Check Ollama Status ~102
Check whether a local Ollama server is running at localhost:11434 and which models it has. Use before run_local_inference, or when someone asks whether offline AI is ready. Takes no arguments and contacts only your own machine (3 second timeout). Returns ollama_running, the model names, a count and the endpoint, or ollama_running false with the error and a link to the official download page. Read-only: it only lists models.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
degraded_mode_guide Degraded Mode Guide ~108
Guide to running AI when connectivity or power is unreliable: why it matters, how to install Ollama, pull a small model, test it and point MCP clients at localhost, with hardware minimums and a Raspberry Pi note. Use when planning an offline or low-bandwidth deployment. Takes no arguments. Returns the explanation, numbered setup steps, hardware minimums and deployment tips. A static guide: follow the vendor's own install instructions at ollama.com. Read-only; no side effects.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
list_recommended_models List Recommended Models ~151
Recommend open-weights models that run locally through Ollama, with size, RAM need, use case and language fit for Africa. Use when someone asks which model to install for their hardware or task. max_ram_gb keeps models that fit; use_case filters by matching text and, if nothing matches, ALL models are returned. Returns the source label, the recommended models, their install commands and a note that they run locally. A curated list labelled by the author, not a benchmark: check model cards. Read-only; no side effects.
| Name | Type | Req | Description |
|---|---|---|---|
| max_ram_gb | – | – | Optional RAM limit in GB; keeps models that fit. |
| use_case | – | – | Optional use case such as translation or coding. |
Structured output declared, but exposes no named fields.
No examples provided.
local_deployment_guide Local Deployment Guide ~122
Setup guide for running a local model on a device type: laptop, Raspberry Pi and others in the table, with hardware, install steps and a model suggestion. Use when someone asks how to deploy offline AI on specific hardware. An unrecognised device_type returns the laptop guide. Returns the source label, the device_type, the guide, all_options and a solar note. A static guide: follow the vendor's install instructions. Read-only; no side effects.
| Name | Type | Req | Description |
|---|---|---|---|
| device_type | – | – | laptop (default), raspberry_pi or another device in the table. |
Structured output declared, but exposes no named fields.
No examples provided.
open_weights_directory Open Weights Directory ~178
Directory of open-weights model families with sizes, languages and licences, including African-language support such as Aya and Masakhane work, plus speech and translation resources (WAXAL, Paza, AfriNLLB, TranslateGemma) with their licences. Use when someone asks which open models or datasets cover Swahili or other African languages, or what the licence allows. use_case filters the language models by matching text; if nothing matches, ALL models are returned. Returns the source label, the language models, an Africa note, how to deploy them locally and the speech and translation resources with licence and coverage caveats. A curated starting list, not exhaustive or benchmarked: check each model card for languages and licence. Read-only; no side effects.
| Name | Type | Req | Description |
|---|---|---|---|
| use_case | – | – | Optional filter text such as swahili. |
Structured output declared, but exposes no named fields.
No examples provided.
run_local_inference Run Local Inference ~179
Send a prompt to a local Ollama model and return its answer, with no internet needed once the model is downloaded. Use when no cloud model is reachable or the text must stay on the machine. The prompt goes only to Ollama at localhost:11434 (60 second timeout, non-streaming) and nothing is stored by this tool. Returns the response text, the model used, the token count and offline true, or an error with a hint to start Ollama if it is not running. Read-only with respect to your data; it uses local compute, and answers from small models can be wrong.
| Name | Type | Req | Description |
|---|---|---|---|
| model | – | – | Ollama model name (default llama3.2:3b); it must already be pulled. |
| prompt | string | yes | The text to send to the local model. It is sent only to localhost. |
Structured output declared, but exposes no named fields.
No examples provided.
How do I install the io.github.gabrielmahia/offline-mcp server?
io.github.gabrielmahia/offline-mcp runs locally as a PyPI package, launched with uvx offline-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · offline-mcp
claude mcp add gabrielmahia-offline-mcp -- uvx offline-mcp
What is the io.github.gabrielmahia/offline-mcp server?
io.github.gabrielmahia/offline-mcp is listed in the public MCP registry as io.github.gabrielmahia/offline-mcp. Local AI over Ollama for low connectivity: status, local inference, model and deployment guides. This page covers its PyPI package (offline-mcp).
Is the io.github.gabrielmahia/offline-mcp server safe to use?
io.github.gabrielmahia/offline-mcp scores 80 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 4 November 2026. Its build provenance is signed and verified. 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 io.github.gabrielmahia/offline-mcp server expose?
io.github.gabrielmahia/offline-mcp exposes 6 tools: check_ollama_status, run_local_inference, list_recommended_models, degraded_mode_guide, open_weights_directory, local_deployment_guide. Their descriptions and schemas cost roughly 840 tokens of context every time the server is loaded.
Is the io.github.gabrielmahia/offline-mcp server still maintained?
io.github.gabrielmahia/offline-mcp is still listed as active in the MCP registry. We last reached this channel on 4 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 io.github.gabrielmahia/offline-mcp server under?
io.github.gabrielmahia/offline-mcp declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.