Compute a verifiable quantum-random draw off a beacon round.
| Name | Required | Description | Default |
|---|---|---|---|
| kind | Yes | ||
| round_id | No | ||
| beacon_hex | No | ||
| request_id | Yes | ||
| params_json | No | {} |
| 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?
With no annotations, the description bears full responsibility for disclosing behavior. It only mentions 'compute' and 'verifiable', but does not explain whether the operation is synchronous, whether it creates any side effects, how round_id and beacon_hex interact, or what permissions/rate limits apply. Significant behavioral ambiguity remains.
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 consists of a single, front-loaded sentence: 'Compute a verifiable quantum-random draw off a beacon round.' Every word contributes to the core purpose, with zero filler or redundancy.
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?
The tool has 5 parameters, 2 required, and no field descriptions in the schema, yet the description is only one sentence. While an output schema exists, the absence of input semantics and usage context leaves the agent under-equipped to construct a correct invocation.
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 input schema has 5 parameters with 0% description coverage, and the tool description adds no parameter information. Parameters like 'kind', 'request_id', and 'params_json' remain cryptic, and the relationship between round_id and beacon_hex is entirely unexplained.
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 uses a specific verb 'Compute' and a specific resource 'a verifiable quantum-random draw off a beacon round', which clearly distinguishes this tool from siblings like animica_quantum_beacon_latest and animica_quantum_verify. It is not fully explicit about whether it consumes an existing beacon round or initiates a new one, but the core purpose is clear.
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 guidance is provided on when to use this tool versus alternives such as animica_quantum_beacon_latest or animica_quantum_verify. There are no stated prerequisites, exclusions, or context cues beyond the innate implication that you would use it to compute a draw.
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.