# com.ainetcafe/ai-netcafe (remote · ainetcafe.com)

Tables and ledgers checked by arithmetic, not by a model. 24 tools. MCP 2026-07-28 ready.

- Trust score: 65/100 (medium)
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-17

> **Recent critical change**: Authorization (2026-08-17). See the changelog below before you install this server.

## Components

- remote · `ainetcafe.com`: 65/100 (this document), [markdown](https://verifymcp.io/servers/com-ainetcafe-ai-netcafe/ainetcafe-2.md), [page](https://verifymcp.io/servers/com-ainetcafe-ai-netcafe/ainetcafe-2)
- npm · `ai-netcafe`: 39/100, [markdown](https://verifymcp.io/servers/com-ainetcafe-ai-netcafe/ai-netcafe.md), [page](https://verifymcp.io/servers/com-ainetcafe-ai-netcafe/ai-netcafe)
- pypi · `ai-netcafe`: 39/100, [markdown](https://verifymcp.io/servers/com-ainetcafe-ai-netcafe/ai-netcafe-2.md), [page](https://verifymcp.io/servers/com-ainetcafe-ai-netcafe/ai-netcafe-2)

## Channel facts

- Endpoint: `https://ainetcafe.com/mcp?s=registry`
- Transports: `streamable-http`
- Auth: `none`
- Version: `1.7.0`

## Trust breakdown

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. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-17.

- **Endpoint Security**: 57/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - Authorisation check failed: no authorisation is required to call this server, and it exposes a tool marked destructive (delete_task).
  - HTTPS is enforced; there's no plaintext access path.
  - HSTS check failed: the Strict-Transport-Security header is absent.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 75/100
  - AI-judged instruction clarity (excellent).
  - Context-footprint check failed: tool/resource definitions use about 4731 tokens (~139/item across 34 items; 34 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 13/100
  - Stability observed for 4 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
  - Structured output schemas are declared (100% of tools); any adoption earns full credit.
- **Capabilities**: 100/100
  - Implements a current MCP spec version (2026-07-28).

## Install

### Claude

```bash
claude mcp add --transport http com-ainetcafe-ai-netcafe https://ainetcafe.com/mcp?s=registry
```

### Codex

```toml
[mcp_servers.com-ainetcafe-ai-netcafe]
url = "https://ainetcafe.com/mcp?s=registry"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "com-ainetcafe-ai-netcafe": {
      "type": "remote",
      "url": "https://ainetcafe.com/mcp?s=registry",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add com-ainetcafe-ai-netcafe --url https://ainetcafe.com/mcp?s=registry --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  com-ainetcafe-ai-netcafe:
    url: "https://ainetcafe.com/mcp?s=registry"
```

### Other

```json
{
  "mcpServers": {
    "com-ainetcafe-ai-netcafe": {
      "type": "http",
      "url": "https://ainetcafe.com/mcp?s=registry"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-17 (score 65, −6)

- [critical regression] Authorization: partial → fail
- [security] Tool “what_can_you_do” rewrote its description, which is the text the model reads
- [security] Tool “delete_task” is now declared destructive

### 2026-08-16 (score 71, +48)

- [security improvement] Authorization: unverified → partial
- [security improvement] Transport: fail → pass
- [security] Tool “reconcile_ledger” rewrote its description, which is the text the model reads
- [security] Tool “clean_table” rewrote its description, which is the text the model reads
- [functional regression] Schema quality: 3833 → 4734
- [functional improvement] Endpoint reachability: not serving MCP → reachable
- [functional improvement] Tool coverage: unverified → 100
- [functional improvement] MCP protocol: unverified → pass
- [functional improvement] Stability: unverified → 0.10
- [functional] New tool “what_can_you_do”
- [functional] New tool “validate_json”
- [functional] New tool “regex_test”
- [functional] New tool “jwt_decode”
- [functional] New tool “json_yaml”
- [functional] New tool “diff_text”
- [functional] New tool “diff_tables”
- [cosmetic] “clean_table” added an optional parameter “keep”
- [cosmetic] “clean_table” reworded the description of “ops”
- [cosmetic] Tool “reconcile_ledger” changed its title: Two ledgers → what does not match, with the arithmetic proof → Two tables → what does not match (the VLOOKUP job), with the arithmetic proof

### 2026-08-15 (score 23, −47)

- [security regression] Endpoint reachability: reachable → not serving MCP
- [security regression] Authorization: partial → unverified
- [security regression] Stability: 0.03 → unverified
- [security regression] Transport: pass → fail
- [functional regression] Capabilities: pass → unverified
- [functional regression] Tool coverage: 100 → unverified
- [functional] First check of Schema quality: unverified

### 2026-08-14 (score 70, +12)

- [security improvement] Transport: fail → pass
- [functional regression] Schema quality: 3067 → 3833
- [functional improvement] Stability: unverified → 0.03
- [functional improvement] MCP protocol: fail → pass
- [functional] MCP protocol version: 2025-06-18 → 2026-07-28
- [functional] New tool “clean_table”
- [functional] New tool “reconcile_ledger”
- [functional] New tool “merge_tables”
- [functional] New tool “extract_invoices”

### 2026-08-13 (score 58)

First indexed and scored.

## MCP tools (34)

### `what_can_you_do` (~159 tokens)

Find the right tool for a task

Describe a task in plain language (any language) and get back exactly which tools on this server do it, with ready-to-run example calls — instead of reading the whole catalogue and guessing. Also returns multi-step recipes when a task needs several tools chained (invoices to a ledger, a bank statement reconciled, a messy CSV turned into a deliverable). Deterministic and free: it calls no model, costs nothing, and never runs out of quota. Call this FIRST when you are not sure what this server offers.

Input parameters:

- `task` (string, required): What you are trying to do, e.g. "reconcile a bank statement against my books" or "把一堆发票整理成能入账的表格"

### `list_apps` (~155 tokens)

List hosted open-source AI applications

List the open-source AI applications hosted and ready to run at AI NetCafé (ainetcafe.com). Each one normally requires local setup (Docker/Python + your own model API key); here they run pre-configured. Use this to find a tool for a task like translating a PDF with formulas intact, generating a PowerPoint file, polishing an academic paper, or running an autonomous research report. Do not call this first when the request already clearly matches compare_models, translate_pdf, deep_research, or make_slides; call that task tool directly. Example — GET https://ainetcafe.com/t/list_apps

Input parameters:

- `category` (string): Optional filter, e.g. "office", "research", "chat".

Output parameters:

- `apps` (array)
- `try_in_browser` (string)

### `get_app` (~75 tokens)

Get details of one application

Full details of one hosted application: what it does, how to use it, measured benchmark scores, source repository, and the URL a human can open to run it. Example — GET https://ainetcafe.com/t/get_app?slug=<slug-from-list_apps>

Input parameters:

- `slug` (string, required): Application slug, from list_apps.

Output parameters:

- `name` (string)
- `open_url` (string)
- `slug` (string)

### `ask_model` (~134 tokens)

Run a prompt on a specific LLM

Send a prompt to one specific large language model and get the answer plus its exact cost in USD. Useful when you want a second opinion from a different model, or a cheaper model for a bulk subtask. Example — GET https://ainetcafe.com/t/ask_model?prompt=Say+hi&model=deepseek-v4-flash

Input parameters:

- `max_tokens` (integer): Optional output cap.
- `model` (string): Model id. Call list_models for available ids. Defaults to a cheap capable model.
- `prompt` (string, required): The prompt to send.
- `system` (string): Optional system instruction.

Output parameters:

- `answer` (string)
- `cost_usd` (number)
- `latency_ms` (number)
- `model` (string)

### `compare_models` (~147 tokens)

Run the same prompt on several models and compare

Run one prompt across multiple LLMs in parallel and return every answer side by side with its real measured cost and latency. This answers "which model should I actually use for this kind of task?" with data instead of guesswork — useful before committing a long job to an expensive model. Example — GET https://ainetcafe.com/t/compare_models?prompt=Explain+CAP+theorem+in+1+line

Input parameters:

- `models` (array): Model ids to compare (2-5). Defaults to a cheap/mid/strong spread.
- `prompt` (string, required): The prompt to send to every model.
- `system` (string): Optional system instruction applied to all.

Output parameters:

- `results` (array)
- `summary` (object|null)

### `list_models` (~79 tokens)

List available models with prices

List every model callable through AI NetCafé with its input/output price per million tokens, so you can pick by cost as well as capability. Example — GET https://ainetcafe.com/t/list_models

Input parameters:

- `tier` (string): Optional filter. "free" models run on the anonymous quota; "premium" needs your own AllRouter key.

Output parameters:

- `models` (array)

### `remember` (~153 tokens)

Store a memory (persists across sessions; with a key, across machines & agents)

Persist a durable memory: an architecture decision, a stable user preference, a verified bug fix, or an important discovery. Anonymous callers get a small per-network memory pool; callers sending an AllRouter key (Authorization: Bearer sk-...) get a large pool shared across ALL their machines and agents — the same key on a laptop's Claude Code and a desktop's Codex recalls the same memories. Do not store secrets or raw logs. Example — tools/call remember {"content":"Deploy key rotates monthly"}

Input parameters:

- `content` (string, required): The memory itself, self-contained (≤2000 chars).
- `kind` (string): Category; default "note".
- `project` (string): Optional project name to scope recall later.

### `recall` (~122 tokens)

Recall stored memories

Retrieve previously stored memories, optionally filtered by search query and/or project. Call at the start of work on a known project to restore context: why decisions were made, known fixes, preferences. Example — GET https://ainetcafe.com/t/recall?query=<what+to+remember>  (needs a workspace/key for durable memory)

Input parameters:

- `limit` (integer): Max results (default 8, up to 20).
- `project` (string): Optional project filter.
- `query` (string): Optional search terms; omit to list the most recent.

### `web_search` (~100 tokens)

Search the web (meta-search)

Search the live web through a self-hosted SearXNG meta-search (aggregates dozens of engines, no tracking). Returns titles, URLs and snippets. Use when you need current information or sources. Example — GET https://ainetcafe.com/t/web_search?query=latest+MCP+spec

Input parameters:

- `max_results` (integer): Max results (default 8, up to 20).
- `query` (string, required): The search query.

### `fetch_page` (~80 tokens)

Fetch a web page as clean Markdown

Fetch any public URL and return LLM-ready clean Markdown (rendered via Crawl4AI, handles JS pages). Use after web_search to read a source, or to ingest any page for analysis. Example — GET https://ainetcafe.com/t/fetch_page?url=https://example.com

Input parameters:

- `url` (string, required): The page URL to fetch.

### `model_costs` (~142 tokens)

Measured per-call cost across models

What one call actually costs on each model, measured. Vendors publish per-million-token list prices, but a call's cost depends on how many tokens the model chooses to emit — models differ by an order of magnitude on the same prompt. standard_bench sends an IDENTICAL prompt to every model, so the difference is the model, not the workload — use that to choose a model before bulk work. production_mixed is real traffic and is NOT comparable across models. Free to cite, CC BY 4.0. Example — GET https://ainetcafe.com/t/model_costs

Input parameters:

- `days` (integer): Measurement window in days (default 30).

### `ai_visibility` (~92 tokens)

Can AI assistants read and cite this site?

Audit a URL for AI visibility: which AI crawlers robots.txt actually allows (parsed per user-agent group, not keyword-matched), whether llms.txt / sitemap / JSON-LD / canonical exist, and how much real text an agent gets without running JavaScript. Returns a score plus the specific fixes, ordered by impact.

Input parameters:

- `url` (string, required): Page to audit, e.g. https://example.com

### `pdf_to_markdown` (~86 tokens)

PDF or scanned page → structured Markdown

Convert a PDF (or a scanned page image) into clean Markdown that keeps headings, lists and tables, and puts multi-column pages in the right reading order. Text-layer PDFs are read exactly and cost far less; images go through a vision model.

Input parameters:

- `url` (string, required): Public URL of the PDF, or of a page image (png/jpg) for scanned documents.

### `extract_tables` (~109 tokens)

PDF tables → structured rows (with schema alignment)

Extract tables from a PDF into structured rows (JSON + CSV). Pass fields to force a fixed set of columns — that aligns a pile of documents that each name their headers differently into one consistent table. Rows the model was unsure about are flagged rather than guessed. Text-layer PDFs only.

Input parameters:

- `fields` (string): Optional comma-separated target columns, e.g. "invoice_no,supplier,date,amount". Omit to infer from the header.
- `url` (string, required): Public URL of the PDF.

### `extract_statement` (~101 tokens)

Bank statement PDF → transactions + reconciliation check

Turn a bank statement or transaction PDF into a clean transaction table (JSON + CSV), then cross-check it: opening + credits - debits must equal the stated closing balance. If it does not balance you get the exact difference and which row the running balance first breaks at — so you know whether the table is safe to use for accounting. Text-layer PDFs only (scanned images not yet supported).

Input parameters:

- `url` (string, required): Public URL of the statement PDF.

### `json_yaml` (~108 tokens)

JSON ↔ YAML, either direction, auto-detected

Converts JSON to YAML or YAML to JSON. It works out which one you gave it, so you do not have to say. A parse failure comes back with the parser message instead of silently producing something that looks fine and is not. Use when a config, a CI file, or a Kubernetes manifest needs to be in the other format.

Input parameters:

- `text` (string, required): The JSON or YAML content.
- `to` (string): Optional: "json" or "yaml" to force the direction.

### `validate_json` (~107 tokens)

Is this JSON valid — and does it have the keys you need?

Checks that text parses as JSON, and optionally that required keys are present with the right top-level types. Returns the specific violations, not just true/false. Checks required + types only — not full JSON Schema, and it says so rather than pretending. Use before feeding generated JSON into something that will fail on it.

Input parameters:

- `schema` (string): Optional JSON Schema (as JSON text) — required[] and properties[].type are checked.
- `text` (string, required): The JSON to validate.

### `diff_text` (~95 tokens)

What changed between two texts, line by line

Returns which lines were added and which were removed, with line numbers — computed with a longest-common-subsequence, not guessed by a model. Use to compare two versions of a config, a document, or any command output, instead of asking an LLM to eyeball two blobs and hoping it notices.

Input parameters:

- `a` (string, required): The first (before) text.
- `b` (string, required): The second (after) text.

### `jwt_decode` (~83 tokens)

See inside a JWT — header, payload, and whether it has expired

Decodes the header and payload of a JWT and reports issued-at / expiry as readable timestamps plus seconds remaining. The signature is NOT verified and the response says so — decoding is fine for debugging a token you already hold, but never treat these values as proof of anything; verification needs the secret and belongs in your own service.

Input parameters:

- `token` (string, required): The JWT string.

### `regex_test` (~99 tokens)

Does this regex match — and what does it capture?

Runs a regular expression against sample text and returns every match with its position and capture groups (named groups included). Use before wiring a pattern into code, instead of guessing whether the escaping survived the trip through JSON and the shell.

Input parameters:

- `flags` (string): Optional flags, e.g. "gi". Default "g".
- `pattern` (string, required): The regular expression, without surrounding slashes.
- `text` (string, required): The text to test against.

### `diff_tables` (~155 tokens)

Two tables → what differs (the VLOOKUP job, no amounts needed)

Matches rows across two CSVs on a key column and reports three things: keys only in A, keys only in B, and keys in both whose other columns disagree — naming the exact column and both values. Unlike reconcile_ledger this needs no amount column, so it also fits name lists, inventory counts, permission tables, and any "these two exports should match" check.

Input parameters:

- `key` (string, required): Column that identifies a row, e.g. id.
- `text_a` (string): Or the first CSV content directly.
- `text_b` (string): Or the second CSV content directly.
- `url_a` (string): Link to the first CSV.
- `url_b` (string): Link to the second CSV.

### `clean_table` (~308 tokens)

Messy CSV → tidy CSV, with a report of every change

Tidies a spreadsheet export: removes duplicate rows, trims whitespace (half-width and full-width — Chinese exports are full of 　), unifies the half-dozen ways a cell can say "empty" (NA / null / - / 无), drops empty rows and columns, and can split one column into several. Returns the cleaned CSV plus exactly what changed: rows in, rows out, duplicates removed, cells trimmed per column. It can also transpose rows/columns and unpivot a wide table into a long one. The row arithmetic is verified in code — if in − removed ≠ out, the response says so instead of handing back a table nobody can check. Use when a CSV came out of Excel or an export and needs cleaning before analysis.

Input parameters:

- `keep` (string): For wide_to_long: comma-separated id columns to keep as-is. Defaults to the first column.
- `ops` (string): Comma-separated, default "dedupe,trim,drop_empty,unify_blank". Also available: split_column, transpose (swap rows/columns), wide_to_long (unpivot a wide table into the long format analysis tools expe…
- `split_by` (string): Separator to split on, default a single space.
- `split_column` (string): Column name to split (requires ops to include split_column).
- `text` (string): The CSV content itself. Provide this or url.
- `url` (string): Link to the CSV. Provide this or text.

### `merge_tables` (~134 tokens)

Several CSVs → one, columns unioned, row counts proven

Combines up to 20 CSVs into a single table. Headers do not have to match: columns are unioned and a file missing a column contributes blanks for it, so rows never shift silently — the failure mode that makes hand-merged spreadsheets untrustworthy. Reports each source file row count and checks in code that they sum to the merged total. Use for monthly exports, per-store sheets, or any set of files with the same subject but drifting headers.

Input parameters:

- `texts` (array): Or pass the CSV contents directly as an array.
- `urls` (string): Comma-separated CSV links, at least two.

### `reconcile_ledger` (~269 tokens)

Two tables → what does not match (the VLOOKUP job), with the arithmetic proof

Reconciles two sets of records — your books against a bank, platform, or supplier statement. Matches rows on a key column, compares an amount column, and returns three lists: only in A, only in B, and same key but different amount. Amounts are compared in integer cents, so 0.1 + 0.2 never invents a phantom difference for someone to chase. The response also proves the result: the listed differences are re-added and must equal the gap between the two totals, checked in code. Use for month-end close, platform payouts vs orders, or any "these two numbers should agree and do not" problem. This is the job people do by hand with VLOOKUP or a groupby and then cannot prove they got right.

Input parameters:

- `amount` (string, required): Numeric column to compare, e.g. amount.
- `key` (string, required): Column name to match rows on, e.g. order_id.
- `text_a` (string): Or the CSV content of side A directly.
- `text_b` (string): Or the CSV content of side B directly.
- `url_a` (string): Link to side A (e.g. your books).
- `url_b` (string): Link to side B (e.g. the statement).

### `extract_invoices` (~147 tokens)

A batch of invoices → one ledger-ready table (arithmetic-checked)

Give it up to 20 invoice URLs (PDF or page images) and get back one table ready to post: number, date, seller, buyer, net / tax / gross, currency. Every row is checked in code — net + tax must equal gross — and the batch total is re-added independently, so a row the model misread is flagged with the exact difference instead of quietly landing in your books. Mixed currencies get no batch total on purpose: adding them together would be an accounting error. CSV is UTF-8 with BOM so Excel opens it right.

Input parameters:

- `urls` (string, required): Invoice URLs — comma-separated, or pass an array. Up to 20 per call.

### `create_task` (~190 tokens)

Schedule a recurring task that runs on our servers

Create a task that runs on a schedule in our cloud — you do not keep anything running. It only notifies you when the result actually changes. Kinds: watch_page (Watch a web page and report when its content changes); daily_answer (Re-run a web-researched question on a schedule and report when the answer changes); watch_reachability (Track whether a site stays reachable from mainland China). Needs a workspace token (?w=ws_... on your MCP URL) so you can manage it later.

Input parameters:

- `input` (string, required): The URL to watch, or the question to re-research.
- `interval_seconds` (integer): How often to run. Minimum 900 (15 min), default 3600.
- `kind` (string, required): watch_page | daily_answer | watch_reachability
- `notify_url` (string): Optional https webhook to POST results to when they change.

### `list_tasks` (~35 tokens)

List your scheduled tasks

Show the scheduled tasks on this workspace, when each runs next, how many times it has run and what it has cost so far.

### `get_task_runs` (~64 tokens)

See what a scheduled task has produced

Recent runs of one scheduled task: what it returned, whether the result changed, and what each run cost.

Input parameters:

- `limit` (integer): How many recent runs, max 20, default 5.
- `task_id` (integer, required): From create_task or list_tasks.

### `delete_task` (~30 tokens)

Delete a scheduled task

Stop and remove a scheduled task and its run history.

Input parameters:

- `task_id` (integer, required): From list_tasks.

### `transpile_sql` (~181 tokens)

Translate SQL between dialects

Convert a SQL statement from one dialect to another — mysql, postgres, sqlite, tsql, oracle, snowflake, bigquery, redshift, spark, hive, presto, trino, duckdb, clickhouse, databricks, doris, starrocks and more. Deterministic parser (sqlglot), not an LLM: the same input always produces the same output, and syntax errors come back with the exact line and column. Use it when migrating queries between databases or debugging dialect-specific syntax.

Input parameters:

- `read` (string): Source dialect, e.g. "mysql". Omit to auto-detect from generic SQL.
- `sql` (string, required): The SQL statement (or several, separated by semicolons).
- `write` (string, required): Target dialect, e.g. "postgres", "bigquery", "doris".

### `china_reachability` (~78 tokens)

Test if a URL is reachable from mainland China

Fetch a URL from a real mainland-China network egress and report HTTP status, latency and China DNS resolution. Answers "is my site/API usable from China?" with a measurement instead of a guess — you cannot get this from a VPS abroad.

Input parameters:

- `url` (string, required): Full URL to test, e.g. https://example.com

### `render_diagram` (~163 tokens)

Render a diagram from text

Turn diagram-as-code into an image: Mermaid, PlantUML, Graphviz/DOT, C4, Excalidraw and 20+ more (self-hosted Kroki). Returns a hosted SVG/PNG URL you can embed directly in Markdown or HTML. Example — GET "https://ainetcafe.com/t/render_diagram?source=graph TD;A--%3EB&format=png"

Input parameters:

- `format` (string): "svg" (default) or "png".
- `source` (string, required): The diagram source code (e.g. a Mermaid flowchart).
- `type` (string): Diagram language: mermaid (default), plantuml, graphviz, c4plantuml, excalidraw, blockdiag, erd…

### `check_job` (~108 tokens)

Check a long-running job

Get the status or result of a job started by deep_research, translate_pdf, or make_slides. Poll every 15-30 seconds until status is "done" or "error". While work is pending, follow retry_after_seconds and next_action; when complete, prefer structured_result when present. Example — GET https://ainetcafe.com/t/check_job?job_id=<id-from-a-job-tool>

Input parameters:

- `job_id` (string, required): The job_id returned when the task was started.

Output parameters:

- `error` (string)
- `is_terminal` (boolean)
- `job_id` (string)
- `kind` (string)
- `next_action` (object|null)
- `result`
- `retry_after_seconds` (integer)
- `status` (string)
- `structured_result`

### `build_app` (~208 tokens)

Build and deploy a web app from a description

Turn one plain-language description into a LIVE single-page web tool: code is generated, deployed to managed hosting with HTTPS, and listed — you get the public URL in ~1-2 minutes. Best for tool-style apps: calculators, converters, checklists, timers, generators, small games. Async — poll with check_job. Example — tools/call build_app {"description":"a tip calculator web app"} → poll check_job

Input parameters:

- `description` (string, required): What the tool should do, in any language. Be specific about inputs/outputs.
- `name` (string): Optional short app name (defaults to the description).
- `refine` (string): Slug of an app you built earlier (e.g. "u-1a23e679") to modify instead of building from scratch — describe only the change in `description`.
- `visibility` (string): "public" (default, listed in the store) or "unlisted" (URL-only, not in the store).

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/com-ainetcafe-ai-netcafe/ainetcafe-2#diagnostics

## Score history

- 2026-08-17: 65
- 2026-08-16: 71
- 2026-08-15: 23
- 2026-08-14: 70
- 2026-08-13: 58

## Links

- Remote endpoint: https://ainetcafe.com/mcp?s=registry
- Repository: https://github.com/mario03690/ai-netcafe
- Website: https://ainetcafe.com/mcp.html
- Changelog RSS feed: https://verifymcp.io/servers/com-ainetcafe-ai-netcafe/ainetcafe-2.xml
- Changelog JSON feed: https://verifymcp.io/servers/com-ainetcafe-ai-netcafe/ainetcafe-2.json
- HTML version of this page: https://verifymcp.io/servers/com-ainetcafe-ai-netcafe/ainetcafe-2