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kortex-mcp

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by ibkortex
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Python
Remote

retrieve

Search enterprise collections with natural-language queries and retrieve relevant passages with relevance scores and source URIs.

Instructions

Semantic search across accessible collections using Amazon Bedrock Knowledge Bases.

Args: query: Natural-language question or search phrase. collections: Which collections (and optionally which version) to search. Each entry is either: - "collection_id" → search that collection at its default version - "collection_id:label" → search that collection at the given version label Omit (or pass null) to search across all collections accessible to the API key, each at its default version. Use list_collections to discover IDs and labels. number_of_results: Maximum number of passages to return (default 10).

Returns a list of results, each with:

  • collectionId / versionLabel: which collection and version the passage came from

  • content: the retrieved text passage

  • score: relevance score (higher is more relevant)

  • sourceUri: original document URI when available

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
collectionsNo
number_of_resultsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses read-only search behavior, 'accessible to the API key', default version handling, default result count, and the exact return structure. It stops short of mentioning potential error conditions or rate limits, but the provided behavioral detail is substantial and honest.

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 well-structured with a clear one-line purpose, an Args section for parameters, and a Returns section. Every sentence adds value and no space is wasted on repetition or filler.

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

Completeness5/5

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

Given the tool's moderate complexity (3 params, no output schema, no annotations), the description is complete. It covers all parameter semantics, default behaviors, return fields, and even cross-references the sibling tool. An agent has enough information to select and invoke this tool correctly.

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

Parameters5/5

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

Although schema description coverage is 0%, the description fully compensates by explaining each parameter: query (natural-language question), collections (including the 'collection_id:label' version syntax and null behavior), and number_of_results (default 10). This adds significant meaning beyond the bare schema types.

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 opens with a specific verb+resource combination: 'Semantic search across accessible collections using Amazon Bedrock Knowledge Bases.' It clearly distinguishes this search tool from the sibling list_collections by explaining that list_collections is for discovering IDs/labels while this tool performs retrieval.

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

Usage Guidelines4/5

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

The description provides clear context for when to use the tool: when you need semantic search over collections, optionally scoped to specific collections/versions. It explicitly directs users to list_collections for discovery, which is an alternative tool reference, though it doesn't enumerate exclusions for when not to use retrieve.

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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