com.synqtune/mcp
PYPI · SYNQTUNE-MCP
Fine-tune a small model on your data and benchmark it against your LLM API, in your AWS account.
Available components
Last scanned (2 hours ago)
Scores higher than 57% 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 & Transparency6
- Repository check failed: no source repository is declared. See how to fix → View diagnostics → Fail
- Build provenance not yet verified. View diagnostics → Unverified
- License check failed: the license (LicenseRef-SynqTune-Proprietary) isn't a recognized OSI-approved license. See how to fix → Fail
- Actively maintained (last published 0 days ago).Pass
- Security-disclosure policy not yet verified: we couldn't inspect the source repository.Unverified
Schema Quality & AI Usability56
- AI-judged instruction clarity (fair).Partial
- Context-footprint check failed: tool/resource definitions use about 1055 tokens (~131/item across 8 items; 8 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 Management13
- Stability observed for 4 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 8 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 8 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 +16
- Malware scan: unverified → pass ▲ security
- 4 Nov 26 −15
- Malware scan: pass → unverified ▼ security
- Package version: 0.1.5 → 0.1.6 functional
- 2 Nov 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 0 to 3. That category is still filling its 30-day observation window: 0 days of observed history at the previous scan, 1 at this one. The score rises as the window fills, whether or not the server changes.
- 1 Nov 26 57
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 5 Nov 2026 · Analysed pypi/synqtune-mcp@0.1.6
Provenance Inconclusive
We could not complete the check, so nothing is claimed either way. This is a gap on our side, not a finding about the package.
| Result | Inconclusive |
|---|---|
| Ecosystem | pypi |
| Reason | Unsupported forge |
| Discovered via | Registry attestation endpoint |
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 →
estimate_cost ~123
GPU cost of a training run (per hour, per day, rough total for the run) and, if you give current LLM usage and prices, the monthly API spend vs serving the model on the same GPU.
| Name | Type | Req | Description |
|---|---|---|---|
| input_tokens | – | – | – |
| instance | string | – | – |
| monthly_requests | – | – | – |
| output_tokens | – | – | – |
| price_in_per_mtok | – | – | – |
| price_out_per_mtok | – | – | – |
| serving_hours_per_day | number | – | – |
| template | string | – | – |
| with_comparison | boolean | – | – |
| Name | Type | Req | Description |
|---|---|---|---|
| result | object | yes | – |
No examples provided.
evaluate_model ~298
Score a saved model version again on a held-out file in S3, optionally against an LLM. Nothing is trained. version_id: a model version (latest_version_id from list_models). eval_uri: .jsonl, .json or .parquet in the format the model was trained on, with rows it never trained on. Leave compare_* blank to compare with the LLM the training run compared with, if any. compare_system_prompt: the prompt the LLM gets in production; blank keeps the training run's, and with none the LLM is sent the task instruction. inference_engine: what serves the fine-tuned model in the comparison, huggingface or vllm (blank keeps the training run's); vllm also times the examples sent 8 and 32 at a time and needs a g5 instance or newer. Without confirm=true this only returns the plan and cost estimate: show it to the user and call again with confirm=true once they agree; it starts a GPU instance billed to the user's AWS account.
| Name | Type | Req | Description |
|---|---|---|---|
| compare_max_samples | – | – | – |
| compare_model | string | – | – |
| compare_provider | string | – | – |
| compare_system_prompt | string | – | – |
| confirm | boolean | – | – |
| eval_uri | string | yes | – |
| inference_engine | string | – | – |
| instance | string | – | – |
| version_id | string | yes | – |
| Name | Type | Req | Description |
|---|---|---|---|
| result | object | yes | – |
No examples provided.
get_comparison_report ~67
The comparison between the fine-tuned model and the LLM it should replace: verdict (ready / close / not_ready), reasons, scores, the weakest labels or fields, and the examples where the fine-tuned model was worse.
| Name | Type | Req | Description |
|---|---|---|---|
| run_id | string | yes | – |
| Name | Type | Req | Description |
|---|---|---|---|
| result | object | yes | – |
No examples provided.
get_run ~37
Status of one run: current step, progress, error, and a summary of its evaluation and comparison.
| Name | Type | Req | Description |
|---|---|---|---|
| run_id | string | yes | – |
| Name | Type | Req | Description |
|---|---|---|---|
| result | object | yes | – |
No examples provided.
launch_pipeline ~444
Train a model from a task template on data in S3, in the user's AWS account. Without confirm=true this only returns the plan and cost estimate: show it to the user and call again with confirm=true once they agree. Launching starts GPU instances billed to the user's AWS account. inputs: the template's inputs (see list_templates), e.g. {"labels": "billing, refund, shipping"}. compare_*: optionally compare with the LLM the model should replace (provider anthropic, openai or gemini). The provider key must already be saved in SynqTune under Settings > LLM provider keys; keys are never passed here. holdout_uri: a held-out file (same format as the source) to test on instead of a random split of the source. compare_max_samples: how many held-out examples to send to the LLM (default 300). compare_price_input_per_mtok / compare_price_output_per_mtok: the LLM's USD price per 1M tokens, so the report shows its cost next to the fine-tuned model's. max_steps, batch_size, base_model: override the template's training. inference_engine: what serves the fine-tuned model in the comparison, huggingface (default) or vllm. vllm gives production-like speed and cost, also times requests sent 8 and 32 at a time, and needs a g5 instance or newer.
| Name | Type | Req | Description |
|---|---|---|---|
| base_model | string | – | – |
| batch_size | – | – | – |
| cloud_account_id | string | – | – |
| compare_max_samples | – | – | – |
| compare_model | string | – | – |
| compare_price_input_per_mtok | – | – | – |
| compare_price_output_per_mtok | – | – | – |
| compare_provider | string | – | – |
| compare_system_prompt | string | – | – |
| confirm | boolean | – | – |
| holdout_uri | string | – | – |
| inference_engine | string | – | – |
| inputs | – | – | – |
| instance | string | – | – |
| max_steps | – | – | – |
| run_name | string | yes | – |
| source_uri | string | yes | – |
| template | string | yes | – |
| Name | Type | Req | Description |
|---|---|---|---|
| result | object | yes | – |
No examples provided.
list_models ~17
Saved models with their latest and production versions.
Input schema present but exposes no named parameters.
| Name | Type | Req | Description |
|---|---|---|---|
| result | array | yes | – |
No examples provided.
list_runs ~41
Recent pipeline runs, newest first. status: all, running, completed, failed or cancelled.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | – |
| status | string | – | – |
| Name | Type | Req | Description |
|---|---|---|---|
| result | array | yes | – |
No examples provided.
list_templates ~28
Task templates a pipeline can start from, with the inputs each needs and the data format it expects.
Input schema present but exposes no named parameters.
| Name | Type | Req | Description |
|---|---|---|---|
| result | array | yes | – |
No examples provided.
How do I install the com.synqtune/mcp server?
com.synqtune/mcp runs locally as a PyPI package, launched with uvx synqtune-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 · synqtune-mcp
claude mcp add com-synqtune-mcp -- uvx synqtune-mcp
What is the com.synqtune/mcp server?
com.synqtune/mcp is listed in the public MCP registry as com.synqtune/mcp. Fine-tune a small model on your data and benchmark it against your LLM API, in your AWS account. This page covers its PyPI package (synqtune-mcp).
Is the com.synqtune/mcp server safe to use?
com.synqtune/mcp scores 59 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 com.synqtune/mcp server expose?
com.synqtune/mcp exposes 8 tools: list_templates, list_runs, get_run, get_comparison_report, list_models, and 3 more. Their descriptions and schemas cost roughly 1,055 tokens of context every time the server is loaded.
Is the com.synqtune/mcp server still maintained?
com.synqtune/mcp 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 com.synqtune/mcp server under?
com.synqtune/mcp declares the LicenseRef-SynqTune-Proprietary licence, which is not on the OSI-approved list. Read the terms before using it at work, and note this covers the source only, not the cost of any service it calls.