AI NetCafé
Server Details
Compare LLMs on one prompt and get every answer with its actually-metered cost and latency — not vendor list prices. Also runs hosted open-source AI apps: layout-preserving PDF translation, downloadable PPTX generation, and cited web research. Nine tools over Streamable HTTP. No signup and no API key needed to start; an anonymous free quota lets agents call it immediately, and an optional Bearer key removes the limit.
- Status
- Healthy
- Uptime
- 100.0% over the last 24 hours
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-06-18
- URL
- ChatGPT
- –
- Claude
- –
TDQS
Scored across 34 tools
Despite 34 tools, most have clearly distinct purposes and the descriptions actively disambiguate overlapping clusters (e.g. diff_tables explicitly contrasts itself with reconcile_ledger; ask_model vs compare_models; list_apps vs get_app). The main residual ambiguity is within the table/extraction families (extract_invoices vs extract_statement vs extract_tables) and model-tool clusters, but descriptions keep boundaries readable.
All names are snake_case, and the dominant pattern is verb_noun (build_app, create_task, fetch_page, list_apps). A minority are noun/phrase-based (ai_visibility, china_reachability, model_costs, json_yaml, pdf_to_markdown), which is a minor deviation but still consistent and readable.
At 34 tools this is well past the 'heavy' band and into kitchen-sink territory for a single server, spanning models, tables, extraction, tasks, apps, memory, and web. Each tool is individually justified, but the sheer breadth makes discovery and selection costly, which is exactly what what_can_you_do is forced to compensate for.
Coverage is broad and lifecycle-aware: tasks have create/list/get_runs/delete, apps have list/get/build, models have list/ask/compare/costs, plus a full data-cleaning/diffing/reconciliation and format-conversion surface. Minor gaps exist (no task update/edit, no memory forget/delete), but these are edge cases an agent can work around.
Available Tools
34 toolsai_visibilityCan AI assistants read and cite this site?ARead-onlyIdempotentInspect
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.
Inputs: url (required). Returns: object
ask_modelRun a prompt on a specific LLMAInspect
Send a prompt to one specific large language model and get the answer plus measured platform cost metadata. The beta platform covers the user charge ($0.00); capacity limits still apply. Example — GET https://ainetcafe.com/t/ask_model?prompt=Say+hi&model=deepseek-v4-flash
Inputs: prompt (required), model, system, max_tokens. Returns: model, answer, cost_usd, latency_ms
build_appBuild and deploy a web app from a descriptionAInspect
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
Inputs: description (required), name, refine, visibility. Returns: object
check_jobCheck a long-running jobARead-onlyIdempotentInspect
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=
Inputs: job_id (required). Returns: kind, error, job_id, result, +5 more
china_reachabilityTest if a URL is reachable from mainland ChinaARead-onlyIdempotentInspect
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.
Inputs: url (required). Returns: object
clean_tableMessy CSV → tidy CSV, with a report of every changeARead-onlyIdempotentInspect
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.
Inputs: ops, url, keep, text, +2 more. Returns: object
compare_modelsRun the same prompt on several models and compareAInspect
Run one prompt across multiple LLMs in parallel and return every answer side by side with measured platform cost metadata and latency. The beta platform covers the user charge ($0.00). This answers "which model should I actually use for this kind of task?" with data instead of guesswork. Example — GET https://ainetcafe.com/t/compare_models?prompt=Explain+CAP+theorem+in+1+line
Inputs: prompt (required), models, system. Returns: results, summary
create_taskSchedule a recurring task that runs on our serversAInspect
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); pipeline (Run one of your production lines (create_pipeline) on a schedule; every run leaves a proof-carrying work order). Needs a workspace token (?w=ws_... on your MCP URL) so you can manage it later. Application and model calls are subsidized during the free beta; your charge is $0.00 and capacity limits apply.
Inputs: kind (required), input (required), notify_url, interval_seconds. Returns: object
delete_taskDelete a scheduled taskADestructiveIdempotentInspect
Stop and remove a scheduled task and its run history.
Inputs: task_id (required). Returns: object
diff_tablesTwo tables → what differs (the VLOOKUP job, no amounts needed)ARead-onlyIdempotentInspect
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.
Inputs: key (required), url_a, url_b, text_a, +1 more. Returns: object
diff_textWhat changed between two texts, line by lineARead-onlyIdempotentInspect
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.
Inputs: a (required), b (required). Returns: object
extract_invoicesA batch of invoices → one ledger-ready table (arithmetic-checked)ARead-onlyIdempotentInspect
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.
Inputs: urls (required). Returns: object
extract_statementBank statement PDF → transactions + reconciliation checkARead-onlyIdempotentInspect
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).
Inputs: url (required). Returns: object
extract_tablesPDF tables → structured rows (with schema alignment)ARead-onlyIdempotentInspect
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.
Inputs: url (required), fields. Returns: object
fetch_pageFetch a web page as clean MarkdownARead-onlyIdempotentInspect
Fetch a public URL and return clean LLM-ready Markdown from the server-rendered response. This tool does not execute browser JavaScript; for SPA or empty-text pages, use web_search, a browser, or the site's API. Use it after web_search to read a reachable public source, or to ingest a static page for analysis. Example — GET https://ainetcafe.com/t/fetch_page?url=https://example.com
Inputs: url (required). Returns: object
get_appGet details of one applicationARead-onlyIdempotentInspect
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>
Inputs: slug (required). Returns: name, slug, open_url
get_task_runsSee what a scheduled task has producedARead-onlyIdempotentInspect
Recent runs of one scheduled task: what it returned, whether the result changed, and measured platform cost metadata. User charge is $0.00.
Inputs: task_id (required), limit. Returns: object
json_yamlJSON ↔ YAML, either direction, auto-detectedARead-onlyIdempotentInspect
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.
Inputs: text (required), to. Returns: object
jwt_decodeSee inside a JWT — header, payload, and whether it has expiredARead-onlyIdempotentInspect
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.
Inputs: token (required). Returns: object
list_appsList hosted open-source AI applicationsARead-onlyIdempotentInspect
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
Inputs: category. Returns: apps, try_in_browser
list_modelsList available models and capacityARead-onlyIdempotentInspect
List every model currently available in the free beta with reference input/output rates and health metadata. Those rates are platform cost metadata only; every user charge is $0.00 during the beta. Example — GET https://ainetcafe.com/t/list_models
Inputs: tier. Returns: models
list_tasksList your scheduled tasksBRead-onlyIdempotentInspect
Show scheduled tasks, next run times, run counts, and measured platform cost metadata. User charge is $0.00 during the beta.
Inputs: no named parameters. Returns: object
merge_tablesSeveral CSVs → one, columns unioned, row counts provenARead-onlyIdempotentInspect
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.
Inputs: urls, texts. Returns: object
model_costsMeasured platform cost across modelsARead-onlyIdempotentInspect
Measured platform cost metadata for one call on each model; your charge is $0.00 during the free beta. 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
Inputs: days. Returns: object
pdf_to_markdownPDF or scanned page → structured MarkdownARead-onlyIdempotentInspect
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.
Inputs: url (required). Returns: object
recallRecall stored memoriesARead-onlyIdempotentInspect
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)
Inputs: limit, query, project. Returns: object
reconcile_ledgerTwo tables → what does not match (the VLOOKUP job), with the arithmetic proofARead-onlyIdempotentInspect
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.
Inputs: key (required), amount (required), url_a, url_b, +2 more. Returns: object
regex_testDoes this regex match — and what does it capture?ARead-onlyIdempotentInspect
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.
Inputs: text (required), pattern (required), flags. Returns: object
rememberStore a memory (persists across sessions within your workspace)AInspect
Persist a durable memory: an architecture decision, a stable user preference, a verified bug fix, or an important discovery. The free beta provides a bounded per-caller/workspace memory pool; no personal API key is required. Do not store secrets or raw logs. Example — tools/call remember {"content":"Deploy key rotates monthly"}
Inputs: content (required), kind, project. Returns: object
render_diagramRender a diagram from textARead-onlyIdempotentInspect
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"
Inputs: source (required), type, format. Returns: object
transpile_sqlTranslate SQL between dialectsARead-onlyIdempotentInspect
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.
Inputs: sql (required), write (required), read. Returns: object
validate_jsonIs this JSON valid — and does it have the keys you need?ARead-onlyIdempotentInspect
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.
Inputs: text (required), schema. Returns: object
web_searchSearch the web (meta-search)ARead-onlyIdempotentInspect
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
Inputs: query (required), max_results. Returns: object
what_can_you_doFind the right tool for a taskARead-onlyIdempotentInspect
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.
Inputs: task (required). Returns: object
Tool Schema Changelog
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