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The OtterStax MCP Server

The OtterStax MCP Server

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by otterstax
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search_objects

Search tables or columns by name across all databases using a LIKE pattern. Select names, summary, or full detail to control returned metadata.

Instructions

Search tables or columns by name across all databases with a LIKE pattern. detail_level: names (minimal), summary (tables: column and row counts; columns: type), full (tables: also columns and indexes). summary/full on tables run COUNT(*) on each match.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "object_type": "<object_type>"
    },
    "name": "search_objects"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax results
patternNoLIKE pattern (% = any chars, _ = one char), case-insensitive%
object_typeYesObject type to search
detail_levelNoDetail: names, summary, fullnames

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.0.0

TDQS

A4.1/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses a non-obvious cost trait — that summary/full on tables run COUNT(*) per match — which is exactly the kind of performance signal an agent needs. It does not restate safety/auth profile, but search is self-evidently a read.

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 action, then the detail_level semantics, then the cost caveat. Dense and mostly waste-free, though the detail_level sentence is a run-on that could be tightened.

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

Completeness4/5

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

With no output schema, the description must convey return content, and it largely does via the detail_level breakdown for both tables and columns. The main gap is the absent usage guidance versus sibling listing tools, which is a routing rather than a completeness defect.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description earns above baseline by expanding detail_level beyond the schema's terse "Detail: names, summary, full", spelling out what each level actually returns for tables vs columns. It does not add much for pattern or limit beyond the schema.

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 (search) and resource (tables or columns by name) plus scope (across all databases) and mechanism (LIKE pattern). This clearly separates it from list_tables/describe_table/table_stats siblings, which enumerate or describe rather than pattern-search.

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

Usage Guidelines3/5

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

Use case is implied by the pattern-based search framing, but the description never says when to prefer this over siblings like list_tables or describe_table, nor any exclusions or prerequisites. An agent must infer routing on its own.

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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