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usecortex-mcp

usecortex-mcp

by usecortex-official
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

search_knowledge

Search stored coding standards, architecture decisions, and project context by keyword to find relevant knowledge entries for AI coding agents.

Instructions

Search knowledge entries by keyword (text match). Use query_knowledge for AI-powered answers.

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": "search_knowledge"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesSearch term

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries full burden. It describes the search method ('keyword (text match)') but doesn't disclose behavioral traits like pagination, result limits, sorting, or error conditions. The description adds some context about the search type but lacks operational details.

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?

Two sentences with zero waste - first states purpose and method, second provides clear alternative. Perfectly front-loaded with essential information in compact form.

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 search tool with 100% schema coverage and no output schema, the description is reasonably complete. It covers purpose, differentiation from alternatives, and search method. However, without annotations or output schema, it could benefit from more behavioral context about what results look like.

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 the single 'query' parameter. The description adds minimal semantic context by mentioning 'keyword' and 'text match', but doesn't provide additional syntax, format, or usage details beyond what the schema provides.

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?

The description clearly states the specific action ('Search knowledge entries') and resource ('knowledge entries') with the method ('by keyword (text match)'). It distinguishes from sibling 'query_knowledge' by specifying this is keyword-based search versus AI-powered answers.

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?

Explicitly provides when to use this tool ('Search knowledge entries by keyword') versus when to use an alternative ('Use query_knowledge for AI-powered answers'). This gives clear guidance on tool selection between two similar search functions.

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

Deploy Server

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