MCP-Server

Misata Studio: verified synthetic data

            studio.misata/misata
        

Daten & Analytik Entwicklertools Öffentlich und erreichbar MCP 2025-11-25

Was dieses MCP kann

Plans, generates, validates, queries, certifies, and exports verified relational synthetic datasets in multiple formats.

blueprint_guide

Blueprint reference

The reference for the engine's full design language (the `blueprint` argument of generate_dataset, start_generation and validate_blueprint): roles, distributions, formulas that read parent columns and draw noise, per-group sequences (seq, lag, cumsum, ar1 drift, random walk), aggregates, event windows, cause-and-effect, lifecycles, with patterns. Read it before writing a blueprint.

Nur Lesen Idempotent

Eingabeschema

{'type': 'object', 'title': 'blueprint_guideArguments', 'properties': {}}

Ausgabeschema

{'type': 'object', 'title': 'blueprint_guideOutput', 'required': ['result'], 'properties': {'result': {'type': 'string', 'title': 'Result'}}}

cancel_generation

Cancel a generation

Stop a running start_generation job. It stops at the next stage boundary and keeps nothing.

Idempotent

Eingabeschema

{'type': 'object', 'title': 'cancel_generationArguments', 'required': ['job_id'], 'properties': {'job_id': {'type': 'string', 'title': 'Job Id'}}}

export_dataset

Export a dataset

Export a generated dataset as a file. Returns a `download_url` the person can open (or you can fetch, e.g. with curl) for as long as the dataset is held, about 2 hours. Give the person the link rather than pasting file contents into the chat. Args: dataset_id: from a prior generate_dataset call. format: data: csv, parquet, jsonl, json, avro, xlsx, feather, orc, sqlite, duckdb, sql. code and docs: dbt, notebook, dictionary, dbml, mermaid, prisma, sqlalchemy, typescript, jsonschema, expectations, django, openapi, mockapi, demo. `sql` is schema.sql (DDL with keys) + data.sql (COPY/INSERT) â the way to seed a real database: run the returned SQL through your own database connection, since this server never holds a database credential itself. dialect: for `sql` only: postgres, mysql, sqlite, mssql, oracle, bigquery, snowflake. inline: also return the file itself as `base64` (only for files under a few MB). Use it when you must write the file yourself and cannot fetch a URL. Returns: filename, content_type, bytes, download_url, expires_at (unix seconds), and `base64` when `inline` and small enough.

Idempotent

Eingabeschema

{'type': 'object', 'title': 'export_datasetArguments', 'required': ['dataset_id'], 'properties': {'format': {'type': 'string', 'title': 'Format', 'default': 'csv'}, 'inline': {'type': 'boolean', 'title': 'Inline', 'default': False}, 'dialect': {'type': 'string', 'title': 'Dialect', 'default': 'postgres'}, 'dataset_id': {'type': 'string', 'title': 'Dataset Id'}}}

find_ready_dataset

Find a ready-made dataset

The ready-made datasets Misata publishes: free sample databases (direct download, public domain) and premium datasets with a full answer key (a free preview, then a one-off price). Check this first when someone wants sample, demo, practice or teaching data for a common scenario (retail, e-commerce, SaaS, manufacturing SPC, predictive maintenance, insurance claims, fraud/AML, clinical, network security): handing over a dataset that already exists is instant. If none fits their tables, generate one instead. Args: query: what the person needs, in their words. Only orders the list (closest first); every dataset is still returned, so judge the fit yourself from the tables and summary. Returns: datasets: [{slug, kind (free|premium), title, summary, rows, tables, page_url, and download_url (free) or free_preview_url + buy_url + price_usd (premium)}].

Nur Lesen Externer Zugriff Idempotent

Eingabeschema

{'type': 'object', 'title': 'find_ready_datasetArguments', 'properties': {'query': {'type': 'string', 'title': 'Query', 'default': ''}}}

generate_dataset

Generate a verified dataset

Make a verified relational dataset and wait for it: every foreign key checked, dates correctly ordered, declared aggregates and rates exact, before anything is returned. Deterministic for a seed. Use this when you give a `schema` or `ddl` (seconds). For a plain-English `request` the engine designs the tables with a model, which can take several minutes: call start_generation instead and poll get_status, so the call does not sit open and time out. Args: request: Plain-English description (needs an LLM key unless `schema`/`ddl` is also given). schema: A Misata schema dict for exact structural control. No key needed for structure. ddl: CREATE TABLE statements. No key needed for structure. seed: Reproducibility seed â the same request/schema and seed always produce the same rows. research: Ground realistic numbers (prices, growth rates) in real published facts via a web search. Off by default (an anonymous caller's request should not trigger external calls unless asked for); needs a key regardless of `schema`/`ddl`. blueprint: The engine's full design language (blueprint_guide has the reference): readings around a parent's nominal, drift, autocorrelation, cause-and-effect, event windows, exact aggregates. Use it whenever the data must behave like the real process. No key needed; run validate_blueprint on it first. Returns: dataset_id: pass this to get_certificate / query_dataset / export_dataset. passed: whether every check held. A dataset that did not pass is still returned, with `certificate.findings` saying what failed â inspect before trusting it. verification: a short, plain-language account of what was checked and what held â show it to the person as the proof, in place of asking them to take the data on trust. tables: {name: {columns, preview_rows (first 20), total_rows}} â the preview only; every row is in the stored dataset, reachable by query_dataset/export_dataset. certificate: the short form (claims, requirements, findings). get_certificate returns the rest (patterns, realism scorecard, every proof chart).

Externer Zugriff Idempotent

Eingabeschema

{'type': 'object', 'title': 'generate_datasetArguments', 'properties': {'ddl': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Ddl', 'default': None}, 'seed': {'type': 'integer', 'title': 'Seed', 'default': 42}, 'schema': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Schema', 'default': None}, 'request': {'type': 'string', 'title': 'Request', 'default': ''}, 'research': {'type': 'boolean', 'title': 'Research', 'default': False}, 'blueprint': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Blueprint', 'default': None}}}

get_certificate

Dataset certificate

The full certificate for a dataset made by generate_dataset: every claim (stated vs actual), every requirement's status and evidence, realism findings, planted defects/anomalies if any, and the pattern charts. This is the whole answer key, not the trimmed form generate_dataset returns.

Nur Lesen Idempotent

Eingabeschema

{'type': 'object', 'title': 'get_certificateArguments', 'required': ['dataset_id'], 'properties': {'dataset_id': {'type': 'string', 'title': 'Dataset Id'}}}

get_status

Generation status

Where a start_generation job is. While running: the stage it is in and how long it has run. When done: the same answer generate_dataset returns (dataset_id, verification, tables, certificate).

Nur Lesen Idempotent

Eingabeschema

{'type': 'object', 'title': 'get_statusArguments', 'required': ['job_id'], 'properties': {'job_id': {'type': 'string', 'title': 'Job Id'}}}

plan_dataset

Plan a dataset

See the tables, sizes and relationships the engine would build, before any rows exist. Free (no rows are made), so it is worth calling before generate_dataset on anything non-trivial: review what it understood and assumed, then adjust your request or schema before spending a real call. Args: request: Plain-English description (needs an LLM key, unless `schema`/`ddl` is also given â then it is still used to ground realism, e.g. locale and what columns mean). schema: A Misata schema dict (see the server instructions for the format). No key needed for structure. ddl: CREATE TABLE statements. No key needed for structure. Returns: route: "chat" (not a dataset request â see `reply`), "design" (the model designed the tables) or "pack" (matched a built-in shape). tables: name, estimated rows, columns, foreign keys for each table the engine would build. understanding: what the engine read the request as (business, archetype, assumptions). requirements: every specific thing the request asked for, so you can see what was understood.

Nur Lesen Idempotent

Eingabeschema

{'type': 'object', 'title': 'plan_datasetArguments', 'properties': {'ddl': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Ddl', 'default': None}, 'schema': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Schema', 'default': None}, 'request': {'type': 'string', 'title': 'Request', 'default': ''}}}

query_dataset

Query a dataset with SQL

Read-only SQL over a dataset's own tables (one SELECT/WITH statement; every table name is a view over that dataset's own files â nothing else on the server is reachable this way). Args: dataset_id: from a prior generate_dataset call. sql: a single SELECT (or WITH ... SELECT) statement. No semicolons, file paths, or statements that write or read outside the dataset (checked before running). limit: rows returned (capped at 5000). Returns: columns, rows, truncated (whether more rows existed than `limit`).

Nur Lesen Idempotent

Eingabeschema

{'type': 'object', 'title': 'query_datasetArguments', 'required': ['dataset_id', 'sql'], 'properties': {'sql': {'type': 'string', 'title': 'Sql'}, 'limit': {'type': 'integer', 'title': 'Limit', 'default': 1000}, 'dataset_id': {'type': 'string', 'title': 'Dataset Id'}}}

start_generation

Start a generation (background)

Start a generation in the background and return a job_id at once. Use it for any plain-English `request` (a model designs the tables, which takes minutes) â then call get_status(job_id) every 20-30 seconds until it says done. Arguments are the same as generate_dataset. Returns: job_id, status "running". get_status gives the stage and, when finished, the dataset.

Externer Zugriff Idempotent

Eingabeschema

{'type': 'object', 'title': 'start_generationArguments', 'properties': {'ddl': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Ddl', 'default': None}, 'seed': {'type': 'integer', 'title': 'Seed', 'default': 42}, 'schema': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Schema', 'default': None}, 'request': {'type': 'string', 'title': 'Request', 'default': ''}, 'research': {'type': 'boolean', 'title': 'Research', 'default': False}, 'blueprint': {'anyOf': [{'type': 'object', 'additionalProperties': True}, {'type': 'null'}], 'title': 'Blueprint', 'default': None}}}

validate_blueprint

Validate a blueprint

Check a blueprint before generating it: every error said as what to change, the design traps it falls into (a column that will round away, a copy of a value not made yet, a window that counts nothing), its size against your row cap, and a small preview run (a few thousand rows) with the verifier's findings and sample rows, so you can see the data behave before the real call. Free. Returns: valid, errors (fix these), warnings (read these), estimated_rows, row_cap, preview: {passed, findings, tables: {name: first rows}} when `preview`.

Nur Lesen Idempotent

Eingabeschema

{'type': 'object', 'title': 'validate_blueprintArguments', 'required': ['blueprint'], 'properties': {'preview': {'type': 'boolean', 'title': 'Preview', 'default': True}, 'blueprint': {'type': 'object', 'title': 'Blueprint', 'additionalProperties': True}}}

whoami

Who am I connected as

Which account this connection is, and what it may do: signed in with a Studio MCP key or anonymous, the row cap, and whether a model key is available for plain-English requests.

Nur Lesen Idempotent

Eingabeschema

{'type': 'object', 'title': 'whoamiArguments', 'properties': {}}

Hinzugefügt

                            find_ready_dataset
                        

2. October 2026 02:40

Hinzugefügt

                            export_dataset
                        

2. October 2026 02:40

Hinzugefügt

                            query_dataset
                        

2. October 2026 02:40

Hinzugefügt

                            get_certificate
                        

2. October 2026 02:40

Hinzugefügt

                            cancel_generation
                        

2. October 2026 02:40

Hinzugefügt

                            get_status
                        

2. October 2026 02:40

Hinzugefügt

                            start_generation
                        

2. October 2026 02:40

Hinzugefügt

                            generate_dataset
                        

2. October 2026 02:40

Hinzugefügt

                            validate_blueprint
                        

2. October 2026 02:40

Hinzugefügt

                            blueprint_guide
                        

2. October 2026 02:40

Hinzugefügt

                            plan_dataset
                        

2. October 2026 02:40

Hinzugefügt

                            whoami
                        

2. October 2026 02:40

Serverdetails

Version

1.0.0

Transport

Streamable HTTP

Tools

Latenz

870 ms

Letzter Scan

2. October 2026 02:40

Erstmals gesehen

2. October 2026 02:19

Endpunkt

https://api.misata.studio/mcp

Letzte Scans

Erfolgreich

2. October 2026 02:40

Misata Studio: verified synthetic data

Was dieses MCP kann

Tools

Letzte Tool-Änderungen

Ähnliche MCP-Server

Thousand API

HubVibe: Pay-per-Call Tools for AI Agents: Web Search, Email Verify, KYC, Stocks, Crypto, News, Data

Blixtworks

Qiniso

Scalix Cloud

RationalBloks

RationalBloks

Wakatime