Dictionary of Applied Machine Learning
REMOTE · DICTIONARYOFML.ORG
Look up, search and cross-reference machine-learning terms from dictionaryofml.org.
Available components
Last scanned (21 hours ago)
Scores higher than 54% of remote servers
Reachable. Last reached 4 Nov 2026.
Category Education and Research
How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. How we score → Why this is hard to score →
Endpoint Security63
- The endpoint's TLS certificate is valid, in date, and uses a strong key. View diagnostics → Pass
- Authorisation not fully verified: no authorisation is required to call this server, and 6 tool(s) never declared a destructiveHint. The MCP spec treats an absent hint as destructive by default, so we cannot call this surface safe. See how to fix → View diagnostics → Unverified
- HTTPS is enforced; there's no plaintext access path. View diagnostics → Pass
- The HSTS (Strict-Transport-Security) header is present. View diagnostics → Pass
- DNSSEC check failed: this domain isn't protected by DNSSEC. See how to fix → View diagnostics → Fail
Transport & Reachability100
- Verified streamable-http transport via a live MCP handshake. View diagnostics → Pass
Schema Quality & AI Usability80
- 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).Pass
- AI-judged instruction clarity (good).Pass
- Tool/resource definitions use about 4253 tokens (~33/item across 126 items; 6 tools + 120 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management30
- Stability observed for 9 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage67
- 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
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 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.
- 3 Nov 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.
- 2 Nov 26 0
- New resource “base learner” functional
- New resource “correlation” functional
- New resource “decision boundary” functional
- 1 Nov 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.
- 31 Oct 26 0
- New resource “inverse” functional
- 30 Oct 26 +1
- New resource “effective dimension” functional
- 29 Oct 26 +3
- The server rewrote its instructions, which are the text every model session reads security
- Schema quality: 133 → 4097 ▼ functional
- The server now declares the “resources” capability functional
- First check of Schema quality: 100 functional
- Schema quality: fair → good functional
- New resource “Gaussian mixture model (GMM)” functional
- New resource “Gaussian random variable (Gaussian RV)” functional
- New resource “Hilbert space” functional
- New resource “Reading order: Erklärbare & vertrauenswürdige KI” functional
- New resource “Reading order: KI in der Praxis (ML-Systeme)” functional
- New resource “Reading order: KI, Recht & AI Act” functional
- New resource “Reading order: KI-Grundlagen” functional
- New resource “Reading order: Mathematik der KI” functional
- New resource “Reading order: Wie funktioniert ChatGPT? (LLMs)” functional
- New resource “Sobolev space” functional
- New resource “accuracy” functional
- New resource “artificial neural network (ANN)” functional
- New resource “attention” functional
- New resource “autoencoder” functional
- New resource “backpropagation” functional
- New resource “bagging” functional
- New resource “baseline” functional
- New resource “binary classification” functional
- New resource “boosting” functional
- New resource “bootstrap” functional
- New resource “classification” functional
- New resource “clustering” functional
- New resource “concept activation vector (CAV)” functional
- New resource “confusion matrix” functional
- New resource “convex” functional
- New resource “covariance matrix” functional
- New resource “data” functional
- New resource “data augmentation” functional
- New resource “data imputation” functional
- New resource “data leakage” functional
- New resource “data point” functional
- New resource “dataset” functional
- New resource “decision tree” functional
- New resource “dimensionality reduction” functional
- New resource “distribution shift” functional
- New resource “eigenvalue” functional
- New resource “eigenvalue decomposition (EVD)” functional
- New resource “empirical risk minimization (ERM)” functional
- New resource “ensemble” functional
- New resource “expectation–maximization (EM)” functional
- New resource “explainability” functional
- New resource “explainable artificial intelligence (XAI)” functional
- New resource “explanation” functional
- New resource “fairness (machine learning)” functional
- New resource “feature” functional
- New resource “feature importance” functional
- New resource “feature learning” functional
- New resource “feature matrix” functional
- New resource “feature vector” functional
- New resource “fine-tuning” functional
- New resource “generalization” functional
- New resource “gradient” functional
- New resource “gradient descent (GD)” functional
- New resource “hyperparameter” functional
- New resource “hypothesis” functional
- New resource “hypothesis space” functional
- New resource “independent and identically distributed (i.i.d.)” functional
- New resource “independent and identically distributed assumption (i.i.d. assumption)” functional
- New resource “inner product” functional
- New resource “interpretability” functional
- New resource “interpretable machine learning (interpretable ML)” functional
- New resource “k-fold cross-validation (k-fold CV)” functional
- New resource “k-means” functional
- New resource “kernel method” functional
- New resource “kernel ridge regression” functional
- New resource “label” functional
- New resource “large language model (LLM)” functional
- New resource “learning rate” functional
- New resource “linear classifier” functional
- New resource “linear regression” functional
- New resource “logistic loss” functional
- New resource “logistic regression” functional
- New resource “loss” functional
- New resource “machine learning pipeline (ML pipeline)” functional
- New resource “matrix” functional
- New resource “mean” functional
- New resource “membership inference attack” functional
- New resource “model” functional
- New resource “model selection” functional
- New resource “norm” functional
- New resource “online learning” functional
- New resource “overfitting” functional
- New resource “policy (reinforcement learning)” functional
- New resource “pretraining” functional
- New resource “principal component analysis (PCA)” functional
- New resource “probability distribution” functional
- New resource “projection” functional
- New resource “random experiment” functional
- New resource “random forest” functional
- New resource “random variable (RV)” functional
- New resource “regularization” functional
- New resource “reinforcement learning (RL)” functional
- New resource “reward” functional
- New resource “robust principal component analysis (robust PCA)” functional
- New resource “sample size” functional
- New resource “self-supervised learning” functional
- New resource “sigmoid function” functional
- New resource “singular value decomposition (SVD)” functional
- New resource “stochastic gradient descent (SGD)” functional
- New resource “supervised learning” functional
- New resource “support vector machine (SVM)” functional
- New resource “tabular data” functional
- New resource “test set” functional
- New resource “training” functional
- New resource “training error” functional
- New resource “training set” functional
- New resource “transfer learning” functional
- New resource “transformer” functional
- New resource “transparency” functional
- New resource “trusted execution environment (TEE)” functional
- New resource “trustworthy artificial intelligence (trustworthy AI)” functional
- New resource “unsupervised learning” functional
- New resource “validation error” functional
- New resource “validation set” functional
- New resource “variance” functional
- New tool “get_learning_path” functional
- New tool “list_learning_paths” functional
- 28 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 3 to 7. That category is still filling its 30-day observation window: 1 days of observed history at the previous scan, 2 at this one. The score rises as the window fills, whether or not the server changes.
- 27 Oct 26 0
- Stability: unverified → 0.03 ▲ 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 · Probed https://dictionaryofml.org/mcp
TLS valid
Negotiated TLS 1.3 with TLS_AES_128_GCM_SHA256 .
| Subject | Issuer | Valid from | Valid until | Key | Signature | Serial |
|---|---|---|---|---|---|---|
| CN=dictionaryofml.org | CN=YE2,O=Let's Encrypt,C=US | 20 Oct 2026 | 18 Jan 2027 | ECDSA 256 | ECDSA-SHA384 | 50ab8262d8ee4e40a29f7b7301d06e0b33f |
| SANs: dictionaryofml.org, www.dictionaryofml.org | ||||||
| CN=YE2,O=Let's Encrypt,C=US (CA) | CN=Root YE,O=ISRG,C=US | 3 Sept 2025 | 2 Sept 2028 | ECDSA 384 | ECDSA-SHA384 | 4df3b15dd6c0784c507cd37b58e6f115 |
| CN=Root YE,O=ISRG,C=US (CA) | CN=ISRG Root X2,O=Internet Security Research Group,C=US | 13 May 2026 | 2 Sept 2032 | ECDSA 384 | ECDSA-SHA384 | 872165fc34b6e5fba8add5b3705fb53a |
| CN=ISRG Root X2,O=Internet Security Research Group,C=US (CA) | CN=ISRG Root X1,O=Internet Security Research Group,C=US | 13 May 2026 | 2 Sept 2032 | ECDSA 384 | SHA256-RSA | 6c8f1dc727c7117f7baf853ac980f9cd |
Background: What to check on a remote MCP endpoint →
DNSSEC insecure
Validation of dictionaryofml.org. — Not signed
| Zone | DS | Keys | Algorithms | Outcome |
|---|---|---|---|---|
| . | trust_anchor | 20326, 38696 | 8, 8 | Verified |
| org. | present | 26974 | 8 | Verified |
| dictionaryofml.org. | absent | Unsigned (proven) parent-signed NSEC/NSEC3 proves an unsigned delegation |
Authentication No authorisation required
The endpoint answered without asking for a token. Anyone who knows the URL can reach it.
| Result | No authorisation required |
|---|---|
| HTTP status | 200 |
| Header | Value |
|---|---|
| strict-transport-security | max-age=31536000; includeSubDomains |
Background: How OAuth 2.1 works in the 2026 MCP spec →
Transports 2 probes
| Transport | URL | Outcome | Status | Location |
|---|---|---|---|---|
| streamable-http | https://dictionaryofml.org/mcp | Verified | 200 | |
| http (plaintext) | http://dictionaryofml.org/mcp | HTTPS enforced | 301 | https://dictionaryofml.org/mcp |
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 →
get_learning_path ~86
One reading order with the abstract of every term in it. Give `topic` (a path id or words from its title or audience), or `terms` (an explicit list of term keys or names, kept in the given order) — the form a selection on alexjung.at/baukasten/ takes.
| Name | Type | Req | Description |
|---|---|---|---|
| terms | array | – | – |
| topic | string | – | – |
No output schema declared.
No examples provided.
get_related_terms ~35
The terms a given entry cross-references (its See-also list), with their abstracts.
| Name | Type | Req | Description |
|---|---|---|---|
| term | string | yes | – |
No output schema declared.
No examples provided.
list_all_terms ~24
List every term of the Dictionary of Applied Machine Learning (name and key).
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
list_learning_paths ~81
The author's curated reading orders through the dictionary: one per topic (how ChatGPT works, AI foundations, explainable and trustworthy AI, mathematics of AI, ...), each with its audience and its terms in the order to read them. Use when someone asks what to learn, read, or teach about a topic, or how to structure a course or seminar.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
lookup_term ~36
Full dictionary entry (abstract, definition, cross-references) for a term, by key or name.
| Name | Type | Req | Description |
|---|---|---|---|
| term | string | yes | – |
No output schema declared.
No examples provided.
search_terms ~38
Search term names and definitions for a keyword or phrase; returns the best matches.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | – |
| query | string | yes | – |
No output schema declared.
No examples provided.
How do I install the Dictionary of Applied Machine Learning MCP server?
Dictionary of Applied Machine Learning is a hosted endpoint at https://dictionaryofml.org/mcp, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
remote · dictionaryofml.org
claude mcp add --transport http org-dictionaryofml-dictionary 'https://dictionaryofml.org/mcp'
What is the Dictionary of Applied Machine Learning MCP server?
Dictionary of Applied Machine Learning is an MCP server listed in the public MCP registry as org.dictionaryofml/dictionary. Look up, search and cross-reference machine-learning terms from dictionaryofml.org. This page covers its hosted endpoint (https://dictionaryofml.org/mcp).
Is the Dictionary of Applied Machine Learning MCP server safe to use?
Dictionary of Applied Machine Learning scores 69 out of 100 on VerifyMCP. 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 Dictionary of Applied Machine Learning MCP server expose?
Dictionary of Applied Machine Learning exposes 6 tools: list_all_terms, lookup_term, search_terms, get_related_terms, list_learning_paths, get_learning_path. Their descriptions and schemas cost roughly 300 tokens of context every time the server is loaded.
Does the Dictionary of Applied Machine Learning MCP server require authentication?
No. We connected to Dictionary of Applied Machine Learning without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.
Is the Dictionary of Applied Machine Learning MCP server still maintained?
Dictionary of Applied Machine Learning 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.