PAID (~$0.005 per BATCH): embed up to 256 texts in one call (384-dim).
| Name | Required | Description | Default |
|---|---|---|---|
| credits | No | ||
| texts_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?
The description discloses the pricing model (PAID, ~$0.005 per BATCH) and the batch limit (up to 256 texts), which are behavioral traits beyond parameter or annotation constraints. Since annotations are absent, the description carries the full burden and addresses key operational constraints.
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, using a single sentence with 10 words, yet conveys essential purpose, cost, limit, and output dimension. Every element earns its place with no filler.
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 has an output schema, the description does not need to explain return values. The description covers purpose, cost, batch limit, and output dimension, which is sufficient for an embedding tool with moderate complexity. However, it could add more context about the typical use case versus alternative embedding methods among siblings.
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 2 parameters with 0% schema description coverage, placing the burden on the description. The description clarifies that 'texts_json' accepts multiple texts (up to 256) in a single call and produces 384-dimensional embeddings, but it does not describe the format, structure, or constraints of the other parameter ('credits'). A score of 3 is appropriate as the description compensates partially.
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 clear verb-resource combination ('embed up to 256 texts in one call') with specific technical detail (384-dim embeddings). Among siblings like animica_qdna_verify_gene, animica_web_fetch, and animica_ai_ask, this uniquely identifies the embedding service, distinguishing it effectively from other tools.
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 states it is a paid tool and quantifies the cost per batch ($0.005 per BATCH), which guides the agent on when to use it (embedding needs) and when to avoid (cost-sensitive scenarios). However, it does not explicitly contrast with non-embedding tools among siblings.
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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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.