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animica_qdna_verify_gene

Verify a qDNA training-genome gene seal: recompute its content-address

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gene_jsonYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description must carry the full burden of behavioral disclosure. It explains the mechanism ('recompute its content-address') but does not state whether the operation is read-only, what happens on failure, or the format of the result. It does not disclose any side effects or error behavior, which is a significant gap for a verification tool with zero annotation coverage.

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 a single, front-loaded sentence that efficiently captures the tool's purpose. Every word contributes value, with no wasted phrases or redundant details. It is appropriately concise for a simple verification tool.

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

Completeness2/5

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

Given the simple schema (one parameter) and the presence of an output schema, the description need not explain return values. However, it is incomplete regarding the required input format ('gene_json') and does not offer any behavioral or usage context. For a tool with zero annotations, the description fails to cover the essential details a user needs to invoke it correctly.

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

Parameters1/5

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

Schema description coverage is 0%, and the description does not mention the 'gene_json' parameter at all. It refers to a 'gene seal' but does not explain that gene_json should be the seal to verify, nor does it provide format, structure, or examples. The description adds no semantic value beyond the parameter name itself.

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 tool's function: 'Verify a qDNA training-genome gene seal: recompute its content-address'. It uses a specific verb ('Verify'), names the resource ('qDNA training-genome gene seal'), and explains the method ('recompute its content-address'). This distinguishes it from sibling tools like animica_quantum_verify, which is more generic.

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?

The description provides no guidance on when to use this tool versus alternatives, nor any exclusions or preconditions. The only implied usage is that it verifies a gene seal, but there is no mention of when this is appropriate or how it differs from other verification tools. No explicit context or alternative naming.

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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TDQS

B3.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes (AI inference, blockchain queries, notarization, quantum randomness, web fetching, etc.). The only minor overlap is between animica_ai_ask and animica_web_ask (both answer questions), but the former is a general AI query and the latter specifically about a single web page, so they are reasonably disambiguated.

Naming Consistency5/5

All tools follow a consistent animica_{domain}_{action} pattern (e.g., animica_ai_ask, animica_chain_block, animica_quantum_beacon_latest). The snake_case convention is uniform, and each name clearly indicates the sub-system and the operation.

Tool Count4/5

22 tools is on the higher side but still reasonable given the broad scope (blockchain, AI, notarization, quantum, web, studio). Each tool serves a distinct purpose, and the count reflects the diverse feature set without being excessive.

Completeness3/5

The tool surface covers the main advertised capabilities (AI inference, blockchain reading, notarization, quantum randomness, web fetching, studio). However, there are notable gaps: no tool for writing to the chain (e.g., sending a transaction), no tool for listing/creating credit tokens, and no AI model management beyond listing. The read-only blockchain tools are thorough but lack write operations.