usecortex-mcp

query_knowledge

Search stored knowledge using natural language questions to find answers based on your coding standards, architecture decisions, and project context.

Instructions

Search your knowledge base using AI. Ask a natural language question and get an answer based on your stored knowledge.

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": {
      "query": "<query>"
    },
    "name": "query_knowledge"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes	Natural language question or search term
`topic`	No	Filter by specific topic (optional)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Mar 17, 2026

TDQS

B3.1/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions AI-based search and natural language input, which adds some behavioral context, but lacks details on permissions, rate limits, response format, or error handling. For a search tool with no annotations, this is insufficient to fully inform agent behavior.

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?

The description is two concise sentences with zero waste: it states the action, method, and input type efficiently. It's front-loaded with the core purpose and appropriately sized for the tool's complexity.

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

Completeness3/5

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

Given no annotations, no output schema, and moderate complexity (2 parameters, AI-based search), the description is minimally adequate. It covers the basic purpose and input type but lacks details on output, error cases, or behavioral traits. It meets the minimum viable threshold but has clear gaps in completeness.

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 description coverage is 100%, so the schema already documents both parameters ('query' and 'topic'). The description adds marginal value by emphasizing 'natural language question' for 'query', but doesn't provide additional syntax, examples, or constraints beyond what the schema states. Baseline 3 is appropriate when schema does the heavy lifting.

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 tool's purpose: 'Search your knowledge base using AI' with a specific verb ('search') and resource ('knowledge base'). It distinguishes from siblings like 'add_knowledge' (write vs read) and 'search_knowledge' (similar but not identical naming), though the distinction from 'search_knowledge' isn't explicit. The natural language aspect adds specificity.

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 on when to use this tool versus alternatives like 'search_knowledge' or 'recall_memory' is provided. The description implies usage for AI-based natural language queries but doesn't specify contexts, exclusions, or prerequisites. It's a basic functional statement without comparative advice.

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

Deploy Server