animica_info
What Animica is and how to use it: the OpenAI-compatible AI API, the
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Animica
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
C2.6/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose behavioral traits. It does not mention whether the tool makes external calls, returns static content, requires authentication, or what kind of output to expect. The truncated sentence leaves much undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a fragment, apparently cut off, and lacks the completeness of a well-formed sentence. It is under-specified rather than concise, and the structure is poor.
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?
Despite having an output schema and no parameters, the description is incomplete due to truncation. It does not explain what the output contains or how to use the tool, so the agent cannot fully understand its purpose or expected result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so there is nothing for the description to explain. Baseline for zero-param tools is 4, which is appropriate here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description begins to indicate that the tool provides general information about Animica and its OpenAI-compatible API, but it is truncated ('...the') and does not clearly state the action or the resource returned. It is not a tautology, but it lacks specificity.
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 implies the tool is for learning about Animica, but gives no explicit context on when to use it versus sibling tools. There are no alternatives mentioned or exclusion criteria, leaving the agent to infer usage.
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.