ContexteTech
PYPI · CONTEXTE · 2 COMPONENTS
Search and load AI datasets, prompts and few-shot contexts from the ContexteTech catalogue.
Available components
Last scanned (16 hours ago)
Scores higher than 64% 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
- No production dependencies, so there is no dependency health to assess. View diagnostics → Pass
Provenance & Transparency19
- Repository check failed: no source repository is declared. See how to fix → View diagnostics → Fail
- 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 6 days ago).Pass
- Security-disclosure policy not yet verified: we couldn't inspect the source repository.Unverified
Schema Quality & AI Usability72
- AI-judged instruction clarity (good).Pass
- Tool/resource definitions use about 585 tokens (~117/item across 5 items; 5 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management0
- Stability check failed: the tool surface changed between 0.4.2 and 0.5.1: 3 tool removals, 0 breaking changes, 5 additions. See how to fix → Fail
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
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 5 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 6 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.
- 19 Oct 26 0
- Package version: 0.4.2 → 0.5.1 functional
- 16 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- 15 Oct 26 −15
- Stability: unverified → fail ▼ security
- Malware scan: pass → unverified ▼ security
- Tool safety: pass → unverified ▼ security
- Stability: Stability not yet verified: we do not have a sandbox capture of the MCP schema this version of the package serves yet. security
- Schema quality: 380 → 585 ▼ functional
- Capabilities: pass → unverified ▼ functional
- Tool coverage: 100 → unverified ▼ functional
- Schema quality: pass → fail ▼ functional
- Package version: 0.4.2 → 0.5.1 functional
- Package version: 0.4.2 → 0.5.0 functional
- 14 Oct 26 +31
- Injection markers: unverified → pass ▲ security
- First check of Judged manipulation: pass security
- Stability: Stability not yet verified: not enough scan history yet (needs a 30-day window). security
- MCP protocol: unverified → pass ▲ functional
- Tool coverage: unverified → 100 ▲ functional
- First check of Tool coverage: 100 functional
- First check of Schema quality: good functional
- First check of Schema quality: pass functional
- First check of Destructive annotations: pass functional
- First check of Schema quality: fail functional
- 13 Oct 26 34
First indexed and scored.
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 22 Oct 2026 · Analysed pypi/contexte@0.5.1
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 0 packages
| Packages resolved | 0 |
|---|---|
| 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 →
download_dataset Download a dataset to a file ~121
Save a whole ContexteTech dataset to a local file, ready for fine-tuning or evaluation. Formats: jsonl (original), messages (OpenAI chat), sharegpt, alpaca, csv. Never overwrites an existing file.
| Name | Type | Req | Description |
|---|---|---|---|
| code | string | yes | Short code or author/name of the dataset; 'code#v2' for a fixed version |
| format | string | – | File format (default: jsonl) |
| path | string | – | File or folder to write to (default: current folder, file named after the dataset) |
No output schema declared.
No examples provided.
estimate_cost Estimate the cost ~66
Estimated cost of a ContexteTech resource with common AI models (euros): input and output tokens, cost per call or per pass, batch discount, cheapest model; for a dataset, fine-tuning on a rented GPU.
| Name | Type | Req | Description |
|---|---|---|---|
| code | string | yes | Short code or author/name |
No output schema declared.
No examples provided.
fill_prompt Fill a prompt ~56
Fill a ContexteTech prompt template ({{variable}} placeholders) and return the ready-to-use prompt.
| Name | Type | Req | Description |
|---|---|---|---|
| code | string | yes | Short code or author/name of the prompt |
| variables | object | yes | Values for each {{variable}} |
No output schema declared.
No examples provided.
load_resource Load a resource ~98
Load one ContexteTech resource by short code (e.g. 'jr6t0n') or 'author/name'. Dataset: description, licence and the first rows; prompt: template and variables; context: system prompt and examples.
| Name | Type | Req | Description |
|---|---|---|---|
| code | string | yes | Short code or author/name; 'code#v2' for a fixed version |
| rows | integer | – | Dataset rows to return (default 10, max 50) |
No output schema declared.
No examples provided.
search_resources Search the ContexteTech catalogue ~161
Search the ContexteTech catalogue of AI building blocks: fine-tuning and evaluation datasets (chat, DPO, agent traces), prompts, few-shot contexts, MCP servers, models and more, many grounded in official European sources, in 9 languages. Returns for each match: short code, title, type, language, licence, number of rows (datasets), description and page URL.
| Name | Type | Req | Description |
|---|---|---|---|
| language | string | – | Optional content language: fr, en, es, de, it, nl, pt, sv, pl |
| query | string | yes | Search words, e.g. 'customs code' or 'GDPR' |
| type | string | – | Optional type: dataset, prompt, context, tool, model, agent, eval, rag… |
No output schema declared.
No examples provided.
How do I install the ContexteTech MCP server?
ContexteTech runs locally as a PyPI package, launched with uvx contexte. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · contexte
claude mcp add contexte-tech-contexte -- uvx contexte
What is the ContexteTech MCP server?
ContexteTech is an MCP server listed in the public MCP registry as io.github.contexte-tech/contexte. Search and load AI datasets, prompts and few-shot contexts from the ContexteTech catalogue. This page covers its PyPI package (contexte).
Is the ContexteTech MCP server safe to use?
ContexteTech scores 65 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 22 October 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 ContexteTech MCP server expose?
ContexteTech exposes 5 tools: search_resources, load_resource, fill_prompt, download_dataset, estimate_cost. Their descriptions and schemas cost roughly 502 tokens of context every time the server is loaded.
Is the ContexteTech MCP server still maintained?
ContexteTech is still listed as active in the MCP registry. We last reached this channel on 22 October 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 ContexteTech MCP server under?
ContexteTech declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.