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astra-knowledge-base-mcp

astra-knowledge-base-mcp

by alrcatraz
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

mgmt_query

Query operational data from mgmt schema tables like services, health_log, and api_keys. Use optional filters by column and value to narrow results.

Instructions

Query operational data from mgmt schema tables

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": {
      "table": "<table>"
    },
    "name": "mgmt_query"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax results (default 20)
tableYesTable to query
filter_colNoOptional column to filter by (e.g. 'name', 'type', 'provider')
filter_valNoOptional value to filter on

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description must disclose behavioral traits. It only states 'Query', implying a read operation, but does not confirm read-only behavior, potential side effects, or whether authentication is required.

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?

The description is a single short sentence with no unnecessary words. It is concise and front-loaded, though it could be slightly more descriptive.

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

Completeness2/5

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

Given no output schema, the description should clarify what is returned (e.g., rows from the queried table). It lacks return format, pagination details, or error conditions, making it incomplete for a query tool.

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?

Input schema covers 100% of parameters with descriptions, so baseline is 3. The description adds no additional meaning beyond the schema; it does not explain how filter_col and filter_val interact or provide usage examples.

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?

The description clearly states the verb 'Query' and the resource 'operational data from mgmt schema tables'. It distinguishes from sibling tools like kb_* (knowledge base operations) and mgmt_list_tables (listing tables) by specifying the action on mgmt tables.

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?

No guidance is provided on when to use this tool versus alternatives, such as mgmt_list_tables for listing tables or other query methods. No exclusions or prerequisites are mentioned.

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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