ai-netcafe

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.

{
  "type": "object",
  "properties": {
    "task": {
      "type": "string",
      "description": "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\""
    }
  },
  "required": [
    "task"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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

{
  "type": "object",
  "properties": {
    "category": {
      "type": "string",
      "description": "Optional filter, e.g. \"office\", \"research\", \"chat\"."
    }
  }
}

{
  "type": "object",
  "properties": {
    "apps": {
      "type": "array",
      "items": {
        "type": "object"
      }
    },
    "try_in_browser": {
      "type": "string"
    }
  },
  "required": [
    "apps"
  ]
}

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>

{
  "type": "object",
  "properties": {
    "slug": {
      "type": "string",
      "description": "Application slug, from list_apps."
    }
  },
  "required": [
    "slug"
  ]
}

{
  "type": "object",
  "properties": {
    "slug": {
      "type": "string"
    },
    "name": {
      "type": "string"
    },
    "open_url": {
      "type": "string"
    }
  },
  "required": [
    "slug",
    "name"
  ]
}

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

{
  "type": "object",
  "properties": {
    "prompt": {
      "type": "string",
      "description": "The prompt to send."
    },
    "model": {
      "type": "string",
      "description": "Model id. Call list_models for available ids. Defaults to a cheap capable model."
    },
    "system": {
      "type": "string",
      "description": "Optional system instruction."
    },
    "max_tokens": {
      "type": "integer",
      "description": "Optional output cap."
    }
  },
  "required": [
    "prompt"
  ]
}

{
  "type": "object",
  "properties": {
    "model": {
      "type": "string"
    },
    "answer": {
      "type": "string"
    },
    "cost_usd": {
      "type": "number"
    },
    "latency_ms": {
      "type": "number"
    }
  }
}

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

{
  "type": "object",
  "properties": {
    "prompt": {
      "type": "string",
      "description": "The prompt to send to every model."
    },
    "models": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Model ids to compare (2-5). Defaults to a cheap/mid/strong spread."
    },
    "system": {
      "type": "string",
      "description": "Optional system instruction applied to all."
    }
  },
  "required": [
    "prompt"
  ]
}

{
  "type": "object",
  "properties": {
    "results": {
      "type": "array",
      "items": {
        "type": "object"
      }
    },
    "summary": {
      "type": [
        "object",
        "null"
      ]
    }
  },
  "required": [
    "results"
  ]
}

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

{
  "type": "object",
  "properties": {
    "tier": {
      "type": "string",
      "enum": [
        "free",
        "premium"
      ],
      "description": "Optional reference tier filter. All currently healthy tiers are available without a user key during the beta."
    }
  }
}

{
  "type": "object",
  "properties": {
    "models": {
      "type": "array",
      "items": {
        "type": "object"
      }
    }
  },
  "required": [
    "models"
  ]
}

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"}

{
  "type": "object",
  "properties": {
    "content": {
      "type": "string",
      "description": "The memory itself, self-contained (≤2000 chars)."
    },
    "kind": {
      "type": "string",
      "enum": [
        "decision",
        "preference",
        "bugfix",
        "discovery",
        "note"
      ],
      "description": "Category; default \"note\"."
    },
    "project": {
      "type": "string",
      "description": "Optional project name to scope recall later."
    }
  },
  "required": [
    "content"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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)

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Optional search terms; omit to list the most recent."
    },
    "project": {
      "type": "string",
      "description": "Optional project filter."
    },
    "limit": {
      "type": "integer",
      "description": "Max results (default 8, up to 20)."
    }
  }
}

{
  "type": "object",
  "additionalProperties": true
}

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

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "The search query."
    },
    "max_results": {
      "type": "integer",
      "description": "Max results (default 8, up to 20)."
    }
  },
  "required": [
    "query"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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

{
  "type": "object",
  "properties": {
    "url": {
      "type": "string",
      "description": "The page URL to fetch."
    }
  },
  "required": [
    "url"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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

{
  "type": "object",
  "properties": {
    "days": {
      "type": "integer",
      "description": "Measurement window in days (default 30)."
    }
  }
}

{
  "type": "object",
  "additionalProperties": true
}

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.

{
  "type": "object",
  "properties": {
    "url": {
      "type": "string",
      "description": "Page to audit, e.g. https://example.com"
    }
  },
  "required": [
    "url"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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.

{
  "type": "object",
  "properties": {
    "url": {
      "type": "string",
      "description": "Public URL of the PDF, or of a page image (png/jpg) for scanned documents."
    }
  },
  "required": [
    "url"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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.

{
  "type": "object",
  "properties": {
    "url": {
      "type": "string",
      "description": "Public URL of the PDF."
    },
    "fields": {
      "type": "string",
      "description": "Optional comma-separated target columns, e.g. \"invoice_no,supplier,date,amount\". Omit to infer from the header."
    }
  },
  "required": [
    "url"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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).

{
  "type": "object",
  "properties": {
    "url": {
      "type": "string",
      "description": "Public URL of the statement PDF."
    }
  },
  "required": [
    "url"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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.

{
  "type": "object",
  "properties": {
    "text": {
      "type": "string",
      "description": "The JSON or YAML content."
    },
    "to": {
      "type": "string",
      "description": "Optional: \"json\" or \"yaml\" to force the direction."
    }
  },
  "required": [
    "text"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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.

{
  "type": "object",
  "properties": {
    "text": {
      "type": "string",
      "description": "The JSON to validate."
    },
    "schema": {
      "type": "string",
      "description": "Optional JSON Schema (as JSON text) — required[] and properties[].type are checked."
    }
  },
  "required": [
    "text"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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.

{
  "type": "object",
  "properties": {
    "a": {
      "type": "string",
      "description": "The first (before) text."
    },
    "b": {
      "type": "string",
      "description": "The second (after) text."
    }
  },
  "required": [
    "a",
    "b"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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.

{
  "type": "object",
  "properties": {
    "token": {
      "type": "string",
      "description": "The JWT string."
    }
  },
  "required": [
    "token"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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.

{
  "type": "object",
  "properties": {
    "pattern": {
      "type": "string",
      "description": "The regular expression, without surrounding slashes."
    },
    "text": {
      "type": "string",
      "description": "The text to test against."
    },
    "flags": {
      "type": "string",
      "description": "Optional flags, e.g. \"gi\". Default \"g\"."
    }
  },
  "required": [
    "pattern",
    "text"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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.

{
  "type": "object",
  "properties": {
    "url_a": {
      "type": "string",
      "description": "Link to the first CSV."
    },
    "url_b": {
      "type": "string",
      "description": "Link to the second CSV."
    },
    "text_a": {
      "type": "string",
      "description": "Or the first CSV content directly."
    },
    "text_b": {
      "type": "string",
      "description": "Or the second CSV content directly."
    },
    "key": {
      "type": "string",
      "description": "Column that identifies a row, e.g. id."
    }
  },
  "required": [
    "key"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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.

{
  "type": "object",
  "properties": {
    "url": {
      "type": "string",
      "description": "Link to the CSV. Provide this or text."
    },
    "text": {
      "type": "string",
      "description": "The CSV content itself. Provide this or url."
    },
    "ops": {
      "type": "string",
      "description": "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 expect)."
    },
    "keep": {
      "type": "string",
      "description": "For wide_to_long: comma-separated id columns to keep as-is. Defaults to the first column."
    },
    "split_column": {
      "type": "string",
      "description": "Column name to split (requires ops to include split_column)."
    },
    "split_by": {
      "type": "string",
      "description": "Separator to split on, default a single space."
    }
  }
}

{
  "type": "object",
  "additionalProperties": true
}

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.

{
  "type": "object",
  "properties": {
    "urls": {
      "type": "string",
      "description": "Comma-separated CSV links, at least two."
    },
    "texts": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Or pass the CSV contents directly as an array."
    }
  }
}

{
  "type": "object",
  "additionalProperties": true
}

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.

{
  "type": "object",
  "properties": {
    "url_a": {
      "type": "string",
      "description": "Link to side A (e.g. your books)."
    },
    "url_b": {
      "type": "string",
      "description": "Link to side B (e.g. the statement)."
    },
    "text_a": {
      "type": "string",
      "description": "Or the CSV content of side A directly."
    },
    "text_b": {
      "type": "string",
      "description": "Or the CSV content of side B directly."
    },
    "key": {
      "type": "string",
      "description": "Column name to match rows on, e.g. order_id."
    },
    "amount": {
      "type": "string",
      "description": "Numeric column to compare, e.g. amount."
    }
  },
  "required": [
    "key",
    "amount"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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.

{
  "type": "object",
  "properties": {
    "urls": {
      "type": "string",
      "description": "Invoice URLs — comma-separated, or pass an array. Up to 20 per call."
    }
  },
  "required": [
    "urls"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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.

{
  "type": "object",
  "properties": {
    "kind": {
      "type": "string",
      "description": "watch_page | daily_answer | watch_reachability | pipeline"
    },
    "input": {
      "type": "string",
      "description": "The URL to watch, or the question to re-research."
    },
    "interval_seconds": {
      "type": "integer",
      "description": "How often to run. Minimum 900 (15 min), default 3600."
    },
    "notify_url": {
      "type": "string",
      "description": "Optional https webhook to POST results to when they change."
    }
  },
  "required": [
    "kind",
    "input"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

Show scheduled tasks, next run times, run counts, and measured platform cost metadata. User charge is $0.00 during the beta.

{
  "type": "object",
  "properties": {},
  "required": []
}

{
  "type": "object",
  "additionalProperties": true
}

Recent runs of one scheduled task: what it returned, whether the result changed, and measured platform cost metadata. User charge is $0.00.

{
  "type": "object",
  "properties": {
    "task_id": {
      "type": "integer",
      "description": "From create_task or list_tasks."
    },
    "limit": {
      "type": "integer",
      "description": "How many recent runs, max 20, default 5."
    }
  },
  "required": [
    "task_id"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

Stop and remove a scheduled task and its run history.

{
  "type": "object",
  "properties": {
    "task_id": {
      "type": "integer",
      "description": "From list_tasks."
    }
  },
  "required": [
    "task_id"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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.

{
  "type": "object",
  "properties": {
    "sql": {
      "type": "string",
      "description": "The SQL statement (or several, separated by semicolons)."
    },
    "read": {
      "type": "string",
      "description": "Source dialect, e.g. \"mysql\". Omit to auto-detect from generic SQL."
    },
    "write": {
      "type": "string",
      "description": "Target dialect, e.g. \"postgres\", \"bigquery\", \"doris\"."
    }
  },
  "required": [
    "sql",
    "write"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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.

{
  "type": "object",
  "properties": {
    "url": {
      "type": "string",
      "description": "Full URL to test, e.g. https://example.com"
    }
  },
  "required": [
    "url"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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"

{
  "type": "object",
  "properties": {
    "source": {
      "type": "string",
      "description": "The diagram source code (e.g. a Mermaid flowchart)."
    },
    "type": {
      "type": "string",
      "description": "Diagram language: mermaid (default), plantuml, graphviz, c4plantuml, excalidraw, blockdiag, erd…"
    },
    "format": {
      "type": "string",
      "description": "\"svg\" (default) or \"png\"."
    }
  },
  "required": [
    "source"
  ]
}

{
  "type": "object",
  "additionalProperties": true
}

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>

{
  "type": "object",
  "properties": {
    "job_id": {
      "type": "string",
      "description": "The job_id returned when the task was started."
    }
  },
  "required": [
    "job_id"
  ]
}

{
  "type": "object",
  "properties": {
    "job_id": {
      "type": "string"
    },
    "kind": {
      "type": "string"
    },
    "status": {
      "type": "string"
    },
    "is_terminal": {
      "type": "boolean"
    },
    "retry_after_seconds": {
      "type": "integer"
    },
    "next_action": {
      "type": [
        "object",
        "null"
      ]
    },
    "result": {},
    "structured_result": {},
    "error": {
      "type": "string"
    }
  },
  "required": [
    "job_id",
    "status"
  ]
}

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

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

{
  "type": "object",
  "additionalProperties": true
}