Verify a quantum-random draw client-side: recompute it from its declared
| Name | Required | Description | Default |
|---|---|---|---|
| result_json | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. The incomplete sentence reveals almost nothing about side effects, permissions, error behavior, or what 'recompute it from its declared' implies. This is a significant transparency gap that makes the tool risky to invoke.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise but reads as a fragment, not a complete sentence. It lacks proper structure and ends abruptly, which is under-specification rather than effective brevity. Key information is missing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With only one parameter and no output schema visible, the description should fully explain the verification process and input requirements. It fails to do so, leaving the tool's purpose and usage unclear. The truncation makes it incomplete even for a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one parameter, result_json, with no description (0% schema description coverage). The description fails to explain what result_json should contain or how it relates to the verification logic. The tool name implies a quantum draw result, but the truncated description does not clarify expected format or provenance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description begins with 'Verify a quantum-random draw client-side' which gives a clear verb and resource, but the sentence is cut off after 'from its declared', leaving the intended meaning incomplete. This makes the purpose ambiguous and fails to distinguish it from sibling tools like animica_quantum_draw or animica_quantum_beacon_latest.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidance is provided. The description does not state when to use this tool versus the related quantum tools, nor any prerequisites or context for verification. The truncated sentence offers no actionable direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.