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rasinmuhammed

misata-mcp

by rasinmuhammed
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Seed a real database

seed_database
Destructive

Generate realistic, schema-based test data for Postgres or SQLite using only a connection string. It reads the live schema, plans before writing, seeds respecting foreign keys, and verifies integrity.

Instructions

Fill a live Postgres or SQLite database with realistic, connected data, read from the database's own schema.

Reads the tables, columns, and foreign keys directly from the target database, generates data that respects them, inserts parents before children, then queries the database back to confirm every foreign key resolves. No schema file and no ORM are needed: a connection string is enough.

SAFETY — this is the only Misata tool that writes to a user's database:

  • It plans by default. With apply=False (the default) nothing is written; you get the table list, insert order, existing row counts, and what would be inserted. Show that plan to the user.

  • Only call again with apply=True after the user has seen the plan and agreed. Never pass apply=True on a first call.

  • If any target table already has rows, the write is refused unless the user chooses truncate=True (wipe and reseed) or append=True (keep existing rows, seed only empty tables, and draw foreign keys from the rows already there). Never guess between these.

  • truncate=True DESTROYS existing data. Only use it on a throwaway development database and only when the user explicitly asks.

Args: db_url: Connection string, e.g. postgresql://localhost/myapp_dev or sqlite:///dev.db. rows: Base row count; reference and transaction tables scale from it. apply: False (default) plans only. True performs the write. truncate: Wipe target tables (children first) before seeding. append: Keep populated tables and seed only the empty ones. tables: Optional allow-list of table names to seed. skip_tables: Tables to leave untouched (migrations, auth, etc.). seed: Random seed; the same seed reproduces the same data.

Returns: A plan (applied: false) or a result with per-table row counts and a per-relationship integrity proof (integrity.verified).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsNo
seedNo
applyNo
appendNo
db_urlYes
tablesNo
truncateNo
skip_tablesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description goes beyond the annotations by detailing the destructive nature (truncate=True destroys data), the write behavior, and the safety flow (plan first, then apply). It transparently discloses side effects and the conditions under which writes are allowed, adding crucial context that annotations alone do not provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Although lengthy, the description is well-structured with clear sections (summary, safety, args) and every sentence adds value. It avoids redundancy and uses formatting (bold, bullet points) to improve readability, making it efficient despite its length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the 8 parameters and output schema, the description thoroughly covers all aspects: parameter usage, safety considerations, and the return value (plan vs result with integrity verification). It is complete and self-contained, leaving no critical gaps for an agent to misuse the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, but the description's Args section explains every parameter in detail, including examples, defaults, and how they interact (e.g., rows scaling, tables/skip_tables filtering, apply vs truncate/append). This fully compensates for the missing schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: filling a live Postgres or SQLite database with realistic, connected data based on the database's own schema. It distinguishes itself from sibling tools like generate_from_schema by emphasizing that no schema file or ORM is needed, and it is the only tool that writes to a database.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly explains when to use this tool (to populate a database) and differentiates it from alternatives by noting it is the only one that writes. It also provides clear instructions on the safe default (apply=False) and when to use truncate/append, making it obvious when to call it versus other tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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