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innovaassolutions

Innovaas KMS MCP Server

Official

kms_search

Search across text, audio, and video documents using vector similarity to find relevant content and retrieve full document details.

Instructions

Semantic search across documents using vector similarity. Searches through text documents, audio transcriptions, and video content with full document content retrieval.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results to return (default: 5)
queryYesSearch query to find relevant documents
thresholdNoSimilarity threshold (0.0-1.0, default: 0.3)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It mentions full document content retrieval but doesn't specify return format, pagination, performance, or how results are ranked beyond vector similarity.

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?

One effective sentence that front-loads the core purpose. Slightly redundant with 'searches through text documents' but overall efficient.

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 multiple similar siblings, the description is minimally viable but lacks differentiation and behavioral details (e.g., authentication, rate limits, result format).

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 coverage is 100%, so the schema already documents all parameters. The description adds no further meaning about query, limit, or threshold beyond what the schema provides.

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?

States a specific verb (semantic search) and resource (documents), and clarifies the search modality (vector similarity). However, it doesn't differentiate from close siblings like kms_multimodal_search or kms_intelligent_search, leaving the agent to guess which to use.

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 such as kms_multimodal_search or kms_intelligent_search. Only implied usage based on the name.

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