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Read-only SQLite MCP

Read-only SQLite MCP

by Deependra12349
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

describe_table

Inspect a SQLite table's columns, data types, keys, and indexes to understand its schema before querying or analyzing data.

Instructions

Show a table's columns, types, keys and indexes.

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": {
      "database": "<database>",
      "table": "<table>"
    },
    "name": "describe_table"
  }
}

Input Schema

NameRequiredDescriptionDefault
tableYes
databaseYesDatabase alias or path given at server startup

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full burden. The verb "Show" plus the enumerated output fields imply a non-destructive read, which is useful, but nothing is said about behavior when the table is missing, permission requirements, or whether indexes/keys may be omitted for some engines.

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?

A single front-loaded sentence with zero filler; every listed noun (columns, types, keys, indexes) carries information about the result.

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?

For a simple two-parameter introspection tool with no output schema, enumerating the returned facets substitutes well for a return-value description. The remaining gap is sibling differentiation against table_stats rather than anything about calling the tool correctly.

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

Parameters2/5

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

Schema coverage is only 50%: "database" is documented as an alias or path, but "table" has no schema description. The description adds no parameter meaning at all, so it fails to compensate for the undocumented half of the input.

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

Purpose4/5

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

"Show a table's columns, types, keys and indexes" names a specific verb and resource and even enumerates the returned facets, so the agent knows exactly what it gets back. It does not, however, distinguish itself from the sibling table_stats, which plausibly overlaps.

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

Usage Guidelines2/5

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

There is no when-to-use guidance, no statement of prerequisites, and no mention of when to prefer table_stats, list_tables, or read_query instead. The agent must infer the routing decision from the sibling names alone.

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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