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ai-netcafe

com.ainetcafe/ai-netcafe
데이터 및 분석 개발자 도구 공개 · 연결 가능 MCP 2026-07-28

이 MCP로 할 수 있는 일

Runs model comparisons, web and PDF extraction, table cleaning and reconciliation, scheduled jobs, app deployment, configuration conversion, JWT inspection, memory, and diagram rendering.

기능

도구

34 도구
ai_visibility
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.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['url'], 'properties': {'url': {'type': 'string', 'description': 'Page to audit, e.g. https://example.com'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
ask_model
Run a prompt on a specific LLM
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
입력 스키마 
{'type': 'object', 'required': ['prompt'], 'properties': {'model': {'type': 'string', 'description': 'Model id. Call list_models for available ids. Defaults to a cheap capable model.'}, 'prompt': {'type': 'string', 'description': 'The prompt to send.'}, 'system': {'type': 'string', 'description': 'Optional system instruction.'}, 'max_tokens': {'type': 'integer', 'description': 'Optional output cap.'}}}
출력 스키마 
{'type': 'object', 'properties': {'model': {'type': 'string'}, 'answer': {'type': 'string'}, 'cost_usd': {'type': 'number'}, 'latency_ms': {'type': 'number'}}}
build_app
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
입력 스키마 
{'type': 'object', 'required': ['description'], 'properties': {'name': {'type': 'string', 'description': 'Optional short app name (defaults to the description).'}, '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`.'}, 'visibility': {'type': 'string', 'description': '"public" (default, listed in the store) or "unlisted" (URL-only, not in the store).'}, 'description': {'type': 'string', 'description': 'What the tool should do, in any language. Be specific about inputs/outputs.'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
check_job
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>
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['job_id'], 'properties': {'job_id': {'type': 'string', 'description': 'The job_id returned when the task was started.'}}}
출력 스키마 
{'type': 'object', 'required': ['job_id', 'status'], 'properties': {'kind': {'type': 'string'}, 'error': {'type': 'string'}, 'job_id': {'type': 'string'}, 'result': {}, 'status': {'type': 'string'}, 'is_terminal': {'type': 'boolean'}, 'next_action': {'type': ['object', 'null']}, 'structured_result': {}, 'retry_after_seconds': {'type': 'integer'}}}
china_reachability
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.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['url'], 'properties': {'url': {'type': 'string', 'description': 'Full URL to test, e.g. https://example.com'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
clean_table
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.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'properties': {'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).'}, 'url': {'type': 'string', 'description': 'Link to the CSV. Provide this or text.'}, 'keep': {'type': 'string', 'description': 'For wide_to_long: comma-separated id columns to keep as-is. Defaults to the first column.'}, 'text': {'type': 'string', 'description': 'The CSV content itself. Provide this or url.'}, 'split_by': {'type': 'string', 'description': 'Separator to split on, default a single space.'}, 'split_column': {'type': 'string', 'description': 'Column name to split (requires ops to include split_column).'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
compare_models
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 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
입력 스키마 
{'type': 'object', 'required': ['prompt'], 'properties': {'models': {'type': 'array', 'items': {'type': 'string'}, 'description': 'Model ids to compare (2-5). Defaults to a cheap/mid/strong spread.'}, 'prompt': {'type': 'string', 'description': 'The prompt to send to every model.'}, 'system': {'type': 'string', 'description': 'Optional system instruction applied to all.'}}}
출력 스키마 
{'type': 'object', 'required': ['results'], 'properties': {'results': {'type': 'array', 'items': {'type': 'object'}}, 'summary': {'type': ['object', 'null']}}}
create_task
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); 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.
입력 스키마 
{'type': 'object', 'required': ['kind', 'input'], '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.'}, 'notify_url': {'type': 'string', 'description': 'Optional https webhook to POST results to when they change.'}, 'interval_seconds': {'type': 'integer', 'description': 'How often to run. Minimum 900 (15 min), default 3600.'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
delete_task
Delete a scheduled task
Stop and remove a scheduled task and its run history.
파괴적 작업 멱등성
입력 스키마 
{'type': 'object', 'required': ['task_id'], 'properties': {'task_id': {'type': 'integer', 'description': 'From list_tasks.'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
diff_tables
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.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['key'], 'properties': {'key': {'type': 'string', 'description': 'Column that identifies a row, e.g. id.'}, '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.'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
diff_text
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.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['a', 'b'], 'properties': {'a': {'type': 'string', 'description': 'The first (before) text.'}, 'b': {'type': 'string', 'description': 'The second (after) text.'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
extract_invoices
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.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['urls'], 'properties': {'urls': {'type': 'string', 'description': 'Invoice URLs — comma-separated, or pass an array. Up to 20 per call.'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
extract_statement
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).
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['url'], 'properties': {'url': {'type': 'string', 'description': 'Public URL of the statement PDF.'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
extract_tables
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.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['url'], '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.'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
fetch_page
Fetch a web page as clean Markdown
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
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['url'], 'properties': {'url': {'type': 'string', 'description': 'The page URL to fetch.'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
get_app
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>
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['slug'], 'properties': {'slug': {'type': 'string', 'description': 'Application slug, from list_apps.'}}}
출력 스키마 
{'type': 'object', 'required': ['slug', 'name'], 'properties': {'name': {'type': 'string'}, 'slug': {'type': 'string'}, 'open_url': {'type': 'string'}}}
get_task_runs
See what a scheduled task has produced
Recent runs of one scheduled task: what it returned, whether the result changed, and measured platform cost metadata. User charge is $0.00.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['task_id'], 'properties': {'limit': {'type': 'integer', 'description': 'How many recent runs, max 20, default 5.'}, 'task_id': {'type': 'integer', 'description': 'From create_task or list_tasks.'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
json_yaml
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.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['text'], 'properties': {'to': {'type': 'string', 'description': 'Optional: "json" or "yaml" to force the direction.'}, 'text': {'type': 'string', 'description': 'The JSON or YAML content.'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
jwt_decode
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.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['token'], 'properties': {'token': {'type': 'string', 'description': 'The JWT string.'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
list_apps
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
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'properties': {'category': {'type': 'string', 'description': 'Optional filter, e.g. "office", "research", "chat".'}}}
출력 스키마 
{'type': 'object', 'required': ['apps'], 'properties': {'apps': {'type': 'array', 'items': {'type': 'object'}}, 'try_in_browser': {'type': 'string'}}}
list_models
List available models and capacity
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
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'properties': {'tier': {'enum': ['free', 'premium'], 'type': 'string', 'description': 'Optional reference tier filter. All currently healthy tiers are available without a user key during the beta.'}}}
출력 스키마 
{'type': 'object', 'required': ['models'], 'properties': {'models': {'type': 'array', 'items': {'type': 'object'}}}}
list_tasks
List your scheduled tasks
Show scheduled tasks, next run times, run counts, and measured platform cost metadata. User charge is $0.00 during the beta.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': [], 'properties': {}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
merge_tables
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.
읽기 전용 멱등성
입력 스키마 
{'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.'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
model_costs
Measured platform cost across models
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
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'properties': {'days': {'type': 'integer', 'description': 'Measurement window in days (default 30).'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
pdf_to_markdown
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.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['url'], 'properties': {'url': {'type': 'string', 'description': 'Public URL of the PDF, or of a page image (png/jpg) for scanned documents.'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
recall
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)
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'properties': {'limit': {'type': 'integer', 'description': 'Max results (default 8, up to 20).'}, 'query': {'type': 'string', 'description': 'Optional search terms; omit to list the most recent.'}, 'project': {'type': 'string', 'description': 'Optional project filter.'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
reconcile_ledger
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.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['key', 'amount'], 'properties': {'key': {'type': 'string', 'description': 'Column name to match rows on, e.g. order_id.'}, '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).'}, 'amount': {'type': 'string', 'description': 'Numeric column to compare, e.g. amount.'}, '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.'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
regex_test
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.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['pattern', 'text'], 'properties': {'text': {'type': 'string', 'description': 'The text to test against.'}, 'flags': {'type': 'string', 'description': 'Optional flags, e.g. "gi". Default "g".'}, 'pattern': {'type': 'string', 'description': 'The regular expression, without surrounding slashes.'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
remember
Store a memory (persists across sessions within your workspace)
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"}
입력 스키마 
{'type': 'object', 'required': ['content'], 'properties': {'kind': {'enum': ['decision', 'preference', 'bugfix', 'discovery', 'note'], 'type': 'string', 'description': 'Category; default "note".'}, 'content': {'type': 'string', 'description': 'The memory itself, self-contained (≤2000 chars).'}, 'project': {'type': 'string', 'description': 'Optional project name to scope recall later.'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
render_diagram
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"
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['source'], 'properties': {'type': {'type': 'string', 'description': 'Diagram language: mermaid (default), plantuml, graphviz, c4plantuml, excalidraw, blockdiag, erd…'}, 'format': {'type': 'string', 'description': '"svg" (default) or "png".'}, 'source': {'type': 'string', 'description': 'The diagram source code (e.g. a Mermaid flowchart).'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
transpile_sql
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.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['sql', 'write'], '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".'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
validate_json
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.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['text'], '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.'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
web_search
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
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['query'], 'properties': {'query': {'type': 'string', 'description': 'The search query.'}, 'max_results': {'type': 'integer', 'description': 'Max results (default 8, up to 20).'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
what_can_you_do
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.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', 'required': ['task'], '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"'}}}
출력 스키마 
{'type': 'object', 'additionalProperties': True}
기록

최근 도구 변경

추가됨
build_app
2026년 9월 17일 12:33 PM
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check_job
2026년 9월 17일 12:33 PM
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render_diagram
2026년 9월 17일 12:33 PM
추가됨
china_reachability
2026년 9월 17일 12:33 PM
추가됨
transpile_sql
2026년 9월 17일 12:33 PM
추가됨
delete_task
2026년 9월 17일 12:33 PM
추가됨
get_task_runs
2026년 9월 17일 12:33 PM
추가됨
list_tasks
2026년 9월 17일 12:33 PM
추가됨
create_task
2026년 9월 17일 12:33 PM
추가됨
extract_invoices
2026년 9월 17일 12:33 PM
추가됨
reconcile_ledger
2026년 9월 17일 12:33 PM
추가됨
merge_tables
2026년 9월 17일 12:33 PM
추가됨
clean_table
2026년 9월 17일 12:33 PM
추가됨
diff_tables
2026년 9월 17일 12:33 PM
추가됨
regex_test
2026년 9월 17일 12:33 PM
추가됨
jwt_decode
2026년 9월 17일 12:33 PM
추가됨
diff_text
2026년 9월 17일 12:33 PM
추가됨
validate_json
2026년 9월 17일 12:33 PM
추가됨
json_yaml
2026년 9월 17일 12:33 PM
추가됨
extract_statement
2026년 9월 17일 12:33 PM
추가됨
extract_tables
2026년 9월 17일 12:33 PM
추가됨
pdf_to_markdown
2026년 9월 17일 12:33 PM
추가됨
ai_visibility
2026년 9월 17일 12:33 PM
추가됨
model_costs
2026년 9월 17일 12:33 PM
추가됨
fetch_page
2026년 9월 17일 12:33 PM
추가됨
web_search
2026년 9월 17일 12:33 PM
추가됨
recall
2026년 9월 17일 12:33 PM
추가됨
remember
2026년 9월 17일 12:33 PM
추가됨
list_models
2026년 9월 17일 12:33 PM
추가됨
compare_models
2026년 9월 17일 12:33 PM
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