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detect_text_genre

Identify text genres like historical_narrative or prophetic to apply accurate extraction rules, using structural patterns or domain-specific vocabulary for enhanced detection.

Instructions

Detect text genre to apply correct extraction rules.

Genres: historical_narrative, narrative_poetry, prayer_praise, recapitulation, prophetic.

DOMAIN-AGNOSTIC: Uses structural patterns by default. Provide domainVocabulary for domain-specific enhanced detection.

Args: segment_id: ID of the segment to analyze. domain_vocabulary: Optional DomainVocabulary for enhanced detection.

Returns: Text genre detection result.

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": {
      "segment_id": "<segment_id>"
    },
    "name": "detect_text_genre"
  }
}

Input Schema

Name	Required	Description	Default
`segment_id`	Yes
`domain_vocabulary`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jan 10, 2026

TDQS

B3.3/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool's approach ('structural patterns by default') and the optional enhancement with domain vocabulary, but doesn't describe what the detection result looks like, potential limitations, error conditions, or performance characteristics. For a detection tool with zero annotation coverage, this leaves significant behavioral gaps.

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 well-structured with clear sections: purpose statement, genre list, behavioral note, parameters, and return value. It's appropriately sized at 8 sentences. The information is front-loaded with the core purpose first. Minor redundancy exists with 'Returns:' section when there's an output schema.

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?

Given the tool's moderate complexity (genre detection with optional enhancement), no annotations, 2 parameters with 0% schema coverage, but with an output schema present, the description does a reasonably complete job. It covers purpose, approach, parameters, and return value. The output schema means the description doesn't need to detail the return structure, which helps 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?

The description explicitly documents both parameters in the 'Args' section, explaining that segment_id is required and domain_vocabulary is optional for enhanced detection. With 0% schema description coverage, this documentation is essential. However, it doesn't provide format details for domain_vocabulary or explain what a segment_id represents in context, leaving some semantic gaps.

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: 'Detect text genre to apply correct extraction rules.' It specifies the verb (detect), resource (text genre), and lists the specific genres. However, it doesn't explicitly differentiate this tool from its many siblings, which appear to be various text analysis tools in the same domain.

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

Usage Guidelines3/5

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

The description provides some implied usage guidance by mentioning 'DOMAIN-AGNOSTIC: Uses structural patterns by default' and suggesting to 'Provide domainVocabulary for domain-specific enhanced detection.' However, it doesn't explicitly state when to use this tool versus alternatives among the many sibling tools, nor does it provide clear exclusions or prerequisites.

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