io.github.farssaadeldin7-ux/neural-link-intention-layer
MCPB · NEURAL-LINK-INTENTION-LAYER.MCPB
Learn a designer's repetitive command patterns and surface the next likely action, cutting the...
Available components
Last scanned (2 hours ago)
Scores higher than 73% of MCPB servers
Category Workflow Automation
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 Security0
- No malware scan is available for this kind of package: the supply-chain vendors we use do not cover it. This is a permanent gap in our coverage, not a finding about the package.Unverified
- Known CVEs could not be checked: we found no dependency list to check in this bundle: either it runs a compiled binary, whose bundled manifests we do not read, or none of the package.json, requirements.txt, pyproject.toml or uv.lock files it ships for its runtime declares a dependency we can read (a requirements.txt that only points at a setup.py project, a local path, or a nested requirements file we cannot follow counts as none).Unverified
- Install-script risk not yet assessed.Unverified
- Dependency health could not be checked: we found no dependency list to check in this bundle: either it runs a compiled binary, whose bundled manifests we do not read, or none of the package.json, requirements.txt, pyproject.toml or uv.lock files it ships for its runtime declares a dependency we can read (a requirements.txt that only points at a setup.py project, a local path, or a nested requirements file we cannot follow counts as none).Unverified
Provenance & Transparency32
- 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
- License check failed: no license is declared. See how to fix → Fail
- Actively maintained (last published 3 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
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
- Context-footprint check failed: tool/resource definitions use about 2043 tokens (~136/item across 15 items; 15 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 Management0
- Stability not yet verified: not enough scan history yet (needs a 30-day window).Unverified
Tool Coverage98
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 93% of tool parameters carry a description.Partial
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 15 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 16 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities20
- Spec-recency check failed: implements MCP spec 2024-11-05; the latest is 2026-07-28. See how to fix → Fail
Unverified: 2 categories
Categories scored 0 because we could not verify them: a data source with nothing on this package, evidence we could not reach, or a check we could not run. We only credit what we can confirm.
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.
- 21 Oct 26 35
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 24 Oct 2026 · Analysed mcpb/https://github.com/farssaadeldin7-ux/claude-plugin-suite/releases/download/mcpb-v0.1.3/neural-link-intention-layer.mcpb@0.1.3
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | mcpb |
| Reason | No attestation published |
Background: How many MCP packages publish verified provenance →
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 →
analyse_log ~175
The sequence audit over a supplied command log: normalisation (runs collapsed, sequences cut at 3-minute idle gaps), top actions, top bigrams and trigrams, recurring sequences of length 4 or more, the undo diagnostic and the navigation share, with the reading bands attached. Counting only — it does not decide what to automate, and it refuses logs under 500 actions rather than reporting numbers they cannot support. Requires a paid plan.
| Name | Type | Req | Description |
|---|---|---|---|
| log | string | yes | One action per line: "timestamp application action", timestamp and application optional. Action names, counts and timestamps only — never document names, layer names or file paths. |
| navigation_actions | array | – | Optional exact action names to count as navigation, on top of the default zoom/pan/navigate/visibility name patterns (tool toggles, for instance). |
No output schema declared.
No examples provided.
automation_catalogue ~101
What each automation mechanism can and cannot do — Photoshop and Illustrator Actions, Figma components and the plugin API, Blender keymaps, After Effects expressions and scripting, OS-level macro tools, hardware — with setup costs and break-even frequencies. Reference data only — choosing between them is the skill's job.
| Name | Type | Req | Description |
|---|---|---|---|
| application | string | – | Optional: photoshop, illustrator, figma, blender, after_effects, os_level or hardware. Omit to list everything. |
No output schema declared.
No examples provided.
billing_portal ~32
Open the Stripe billing portal to change plan, update the card, or cancel the Neural-Link Intention Layer subscription.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
fit_predictor ~271
Fit the n-gram predictor on a supplied log and report its accuracy honestly: chronological split with the last 20% held out, baseline top-1, model top-1 with self-transitions excluded, top-3, and the log's date range and size — plus the contexts that clear the confidence floor (default 0.80), with destructive continuations suppressed. Requires at least 2,000 normalised actions; under 5,000 the trigram figures are provisional. With save: true, the fitted counts are also persisted locally so predict_next can answer later without refitting. It predicts the user's own recorded habits, not intention, and nothing is auto-executed. Requires a paid plan.
| Name | Type | Req | Description |
|---|---|---|---|
| k | number | – | Optional cost asymmetry: how many times worse a wrong suggestion is than a right one is good. Floor is k / (1 + k). Default 4, giving 0.80. |
| log | string | yes | One action per line: "timestamp application action", timestamp and application optional. |
| save | boolean | – | Optional: persist the fitted model (counts, fit metadata, holdout accuracy) to the local config directory, replacing any previously saved model, so predict_next and model_status can use it. The model… |
No output schema declared.
No examples provided.
instrumentation_guide ~114
How to get a command history out of an application: the three tiers of source with their coverage and blind spots, per-application methods (Photoshop, Blender, Figma, After Effects, Illustrator), OS-level hotkey logging, the capture rules that keep a log private, and the exact log format the analysis tools expect. Reference data only — it measures nothing itself.
| Name | Type | Req | Description |
|---|---|---|---|
| application | string | – | Optional: photoshop, blender, figma, after_effects, illustrator or other. Omit for the full guide. |
No output schema declared.
No examples provided.
license_activate ~69
Store and activate a Neural-Link Intention Layer licence key on this machine. Use when the user pastes a licence key, or after a purchase completes.
| Name | Type | Req | Description |
|---|---|---|---|
| license_key | string | yes | The licence key, e.g. PS-XXX-00000-00000-00000-0000 |
No output schema declared.
No examples provided.
license_status ~52
Check the Neural-Link Intention Layer licence on this machine: plan, included capabilities, remaining quota and seat usage. Call this when a paid tool reports a licence problem, or when the user asks what their plan includes.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
list_plans ~26
List the available Neural-Link Intention Layer plans, prices and what each one includes.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
method_reference ~69
The method's fixed numbers: normalisation rules, minimum log sizes, the evaluation protocol and expected accuracy ranges, the undo and navigation reading bands, the payback formula with its constants, and the confidence-floor table. Everything the licensed tools compute with, stated openly so the approach can be judged before buying.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
model_status ~64
The saved predictor model's vital signs: fit date, log size and date range, vocabulary size, holdout accuracy, and a staleness verdict against the refit rule's eight weeks. Reads the local model file only; nothing is refitted. Requires a paid plan.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
predict_next ~221
The top-k next-action predictions from the saved model, given the most recent actions: each with its probability and whether it clears the confidence floor (default 0.80), destructive continuations listed separately and never surfaced, and the model's holdout accuracy attached so the caller knows how far to trust it. Same trigram-with-backoff arithmetic the holdout evaluation measured — it predicts the user's own recorded habits, not intention, and nothing is auto-executed. Needs a model saved by fit_predictor with save: true. Requires a paid plan.
| Name | Type | Req | Description |
|---|---|---|---|
| k | number | – | Optional cost asymmetry: how many times worse a wrong suggestion is than a right one is good. Floor is k / (1 + k). Default 4, giving 0.80. |
| recent_actions | array | yes | The most recent actions, oldest first, as normalised action names from the same vocabulary as the fitted log. The last one or two are the prediction context. |
| top_k | integer | – | How many predictions to return. Default 3. |
No output schema declared.
No examples provided.
record_build ~245
Record an automation that was actually built — mechanism, sequence, predicted weekly firing rate, payback — in the local build log, so the two-week re-measure has something to check against. Given a build_id and observed_f_per_week instead, records the follow-up measurement on an existing build. Requires a paid plan; the licence check is the only thing this tool sends anywhere.
| Name | Type | Req | Description |
|---|---|---|---|
| application | string | – | – |
| build_id | string | – | Existing build to record a follow-up on. Omit when recording a new build. |
| mechanism | string | – | E.g. "Photoshop Action", "Blender keymap entry", "Stream Deck button". |
| notes | string | – | – |
| observed_f_per_week | number | – | Observed firing rate at the re-measure, from a fresh log. Only with build_id. |
| payback_weeks | number | – | The scored payback, from score_candidate. |
| predicted_f_per_week | number | – | Predicted weekly firing rate, from the log. |
| sequence | string | – | The action sequence automated, e.g. "new_layer -> fill_50_grey -> blend_mode:soft_light". |
No output schema declared.
No examples provided.
review_builds ~59
The record of past builds with predicted against observed firing rates. Counting only — it flags nothing and removes nothing; whether an underfiring macro comes out is the user's call. Requires a paid plan.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | number | – | Default 20. |
No output schema declared.
No examples provided.
score_candidate ~327
The payback arithmetic for one automation candidate: value = F x (K + C) / (S + R) and payback_weeks = S x 1.3 / (F x (K + C)), against the 8-week build threshold. Arithmetic over supplied terms only — F is only meaningful if it was read from a log, and the result says so. Requires a paid plan.
| Name | Type | Req | Description |
|---|---|---|---|
| c_seconds | number | – | Context-switch cost in seconds: 0 hand stays put, 1.5 mouse-to-menu, 4 dialogue, 15 leaving the app. Default 0. |
| correct_fire_p | number | – | Optional probability the automation fires correctly, 0-1. Supply with wrong_fire_severity_seconds to include R. |
| f_per_week | number | yes | Occurrences per week, from the log. |
| k_seconds | number | yes | Seconds saved per occurrence: keystroke 0.3, menu trip 1.5, modal dialogue 4. |
| setup_seconds | number | yes | Setup seconds: ~180 keymap, ~300 action, ~900 component set, ~1200 OS macro, ~5400 plugin. Maintenance (30% annualised) is added by the formula. |
| stable_across_log | boolean | – | Whether the sequence is stable across the whole log. Without it the verdict stays unconfirmed. |
| wrong_fire_severity_seconds | number | – | Optional severity of a wrong fire: 2 if easily undone, 30 if silently wrong. If destructive, do not build — R is unbounded. |
No output schema declared.
No examples provided.
start_checkout ~76
Begin a Neural-Link Intention Layer purchase. Returns a Stripe Checkout link. Call list_plans first if the user has not chosen a plan.
| Name | Type | Req | Description |
|---|---|---|---|
| string | – | Email address for the receipt and licence. | |
| plan | string | yes | Plan id: "pro" or "team". Use list_plans to see the options. |
No output schema declared.
No examples provided.
How do I install the io.github.farssaadeldin7-ux/neural-link-intention-layer MCP server?
io.github.farssaadeldin7-ux/neural-link-intention-layer ships as an MCPB bundle, a single file you download and open in an MCP host that supports MCPB bundles, such as Claude Desktop. The host reads the launch command from the bundle's own manifest, so there is no command to copy. Before opening it, check the file's SHA-256 matches the one the MCP registry declares for it, 93db2f2cd060a8a52a42785c128bd1703e182986b55d5fb7cb05816797f8d845; the commands to do that are on this page. If it does not match, do not open it.
mcpb · neural-link-intention-layer.mcpb
Download bundleCheck the download matches the SHA-256 the MCP registry declares before you open it. If it does not match, do not open the file.
SHA-256
93db2f2cd060a8a52a42785c128bd1703e182986b55d5fb7cb05816797f8d845
macOS / Linux
echo "93db2f2cd060a8a52a42785c128bd1703e182986b55d5fb7cb05816797f8d845 neural-link-intention-layer.mcpb" | shasum -a 256 -c
Windows (PowerShell)
(Get-FileHash -Algorithm SHA256 .\neural-link-intention-layer.mcpb).Hash -eq "93db2f2cd060a8a52a42785c128bd1703e182986b55d5fb7cb05816797f8d845"
Run it in the folder you saved the bundle to, then open the file with an MCPB-capable host such as Claude Desktop.
What is the io.github.farssaadeldin7-ux/neural-link-intention-layer MCP server?
io.github.farssaadeldin7-ux/neural-link-intention-layer is an MCP server listed in the public MCP registry as io.github.farssaadeldin7-ux/neural-link-intention-layer. Learn a designer's repetitive command patterns and surface the next likely action, cutting the... This page covers its MCPB bundle (https://github.com/farssaadeldin7-ux/claude-plugin-suite/releases/download/mcpb-v0.1.3/neural-link-intention-layer.mcpb).
Is the io.github.farssaadeldin7-ux/neural-link-intention-layer MCP server safe to use?
io.github.farssaadeldin7-ux/neural-link-intention-layer scores 35 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 io.github.farssaadeldin7-ux/neural-link-intention-layer MCP server expose?
io.github.farssaadeldin7-ux/neural-link-intention-layer exposes 15 tools: instrumentation_guide, automation_catalogue, method_reference, analyse_log, fit_predictor, and 10 more. Their descriptions and schemas cost roughly 1,901 tokens of context every time the server is loaded.