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Gulliver

Gulliver

by laaielichter
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

gulliver_lint

Read-onlyIdempotent

Run a static SQL scale check to catch patterns like SELECT *, missing partition filters, and cross joins; get severity and suggested fixes without SQL rewrites.

Instructions

Static scale lint, no database needed: report SQL patterns that are fine on small data but expensive or wrong on big data (rules GV001-GV012: SELECT *, missing partition filter, cross joins, functions on partition columns, ...). Each finding has a severity and a suggested fix as text; nothing is rewritten. Use it for a quick check while writing SQL; use gulliver_fix to get the rewritten SQL, gulliver_preflight for cost on a real connection.

Input Schema

NameRequiredDescriptionDefault
sqlYesExactly one SQL statement (SELECT/WITH) in the connection's dialect. Several statements are rejected.
dialectNoDialect used to parse the SQL. Some rules only apply to some engines (e.g. partition filters on BigQuery).duckdb

Output Schema

NameRequiredDescriptionDefault
statusYes
next_stepYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.3.1
    • changedInput schema / properties / dialect / description
      Previous value: -"SQL dialect."New value: +"Dialect used to parse the SQL. Some rules only apply to some engines (e.g. partition filters on BigQuery)."
    • changedInput schema / properties / sql / description
      Previous value: -"One SQL statement in the connection's dialect."New value: +"Exactly one SQL statement (SELECT/WITH) in the connection's dialect. Several statements are rejected."
  2. Changed3 schema fields changedv0.1.1
    • addedOutput schema / properties
      Added value: +{
+  "next_step": {
+    "type": "string"
+  },
+  "status": {
+    "enum": [
+      "ok",
+      "warn",
+      "block",
+      "needs_rehearsal",
+      "needs_approval",
+      "skipped",
+      "fail",
+      "error"
+    ],
+    "type": "string"
+  }
+}
    • addedOutput schema / required
      Added value: +[
+  "status",
+  "next_step"
+]
    • changedOutput schema / title
      Previous value: -"gulliver_lintDictOutput"New value: +"GulliverResult"
  3. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), so the description's job is to add context. It does: no database connection is used, nothing is rewritten, and findings carry a severity plus a textual suggested fix. It stops short of describing pagination or the finding count, but the output schema presumably covers the shape.

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

Conciseness4/5

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

Front-loaded with the core purpose and alternatives in three dense sentences with no filler. The rule enumeration in the parenthetical is long but justifies itself by showing rule scope; slight density is the only cost.

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?

For a two-parameter read-only lint tool with an output schema and full annotations, the description covers purpose, when to use, what is and isn't produced, and routing to the right sibling. Nothing an agent needs to invoke it correctly is missing.

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

Parameters3/5

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

Schema coverage is 100%, so the sql and dialect parameters are already fully documented, including the enum and the note that some rules are engine-specific. The description adds only indirect signal (naming 'missing partition filter' as a rule), which the dialect description already explains. Baseline 3 for a schema that does the work.

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?

States a specific verb and resource: static scale lint of SQL reporting patterns that are expensive or wrong on big data, with the concrete rule set GV001-GV012 enumerated. The phrase 'no database needed' immediately separates it from siblings that touch a real connection.

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?

Gives an explicit use case ('quick check while writing SQL') and names two alternatives with the conditions that select them: gulliver_fix for rewritten SQL, gulliver_preflight for cost on a real connection. No inference required.

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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