PAID (~$0.005): verify a post-quantum signature (ML-DSA-65 / Dilithium3 / SPHINCS+).
| Name | Required | Description | Default |
|---|---|---|---|
| alg_id | Yes | ||
| credits | No | ||
| message_hex | Yes | ||
| signature_hex | Yes | ||
| public_key_hex | 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only reveals the paid nature and algorithms. It does not describe what happens on successful or failed verification, whether credits are consumed, or any side effects. The return value is not mentioned even though an output schema exists (not shown). This is insufficient for a paid tool with potentially irreversible credit usage.
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 extremely concise (one short sentence), which is good for brevity but severely under-specifies critical details for a tool with 5 undocumented parameters and no annotations. The cost is front-loaded, but essential usage and parameter information is missing, making it inadequate for an AI agent.
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?
Given the tool's complexity (5 parameters, no schema descriptions, no annotations, sibling tool confusion), the description is vastly incomplete. It fails to explain parameter values, algorithm selection, credit consumption, output format, or how it differs from similar verify tools. An agent would struggle to invoke this tool correctly based solely on this description.
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 provides almost no parameter guidance. It implies the signature uses message, signature, and public key hex strings, but does not explain formats, the meaning of 'alg_id' (integer), how to obtain or use 'credits', or constraints (e.g., hex encoding length). The agent cannot correctly populate parameters based solely on the description.
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 clearly states the tool's purpose: verify a post-quantum signature, and specifies the supported algorithms (ML-DSA-65, Dilithium3, SPHINCS+). However, it does not differentiate from the sibling tool 'animica_quantum_verify', which could cause confusion about which verification tool to use for different quantum algorithms.
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?
The description mentions the cost (~$0.005) but provides no guidance on when to use this tool versus alternatives like 'animica_quantum_verify' or 'animica_qdna_verify_gene'. It lacks context about prerequisites (e.g., need credits) or situations where this tool is not appropriate.
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.