PAID (~$0.005, or free with a credit token): fetch a public web page as clean text.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| credits | No | ||
| max_chars | 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 provided, the description must fully disclose behavioral traits. It states that the output is 'clean text' from a public web page, but it fails to mention critical details like rate limits, error handling (e.g., for unreachable pages, redirects, or large pages), authentication needs, or any side effects. The cost mention is a positive, but for a tool that fetches external content, more behavioral context is needed.
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 very concise at 10 words (excluding the cost hint in parentheses), and it front-loads the key action and resource. The cost information is a slight digression but is short and relevant. Every word earns its place, though the clarity of 'clean text' could be improved with a brief qualifier.
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 simplicity (one required param, no nested objects), the description covers core purpose and cost context. However, with no output schema provided (though context says it exists), a brief note on what 'clean text' means or how output is structured would improve completeness. It is adequate but leaves gaps in usage and error behavior.
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?
Schema description coverage is 0%, so the description must compensate. It mentions 'a public web page,' implying the URL parameter, and 'clean text' hints at the output, but it doesn't explain the 'credits' or 'max_chars' parameters. The cost note ('$0.005, or free with a credit token') partially clarifies the 'credits' field, but 'max_chars' is left unexplained. The description adds some value beyond the schema but is incomplete.
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 what the tool does: 'fetch a public web page as clean text.' It uses the specific verb 'fetch' and the resource 'public web page,' making the purpose unmistakable. However, it does not explicitly distinguish this tool from siblings that might also involve web content, like 'animica_web_ask', but the clear action and resource set it apart well.
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 cost ('PAID (~$0.005, or free with a credit token)'), which gives some context about when this tool might be used vs. free alternatives. However, it doesn't specify when to use this tool versus other web-related siblings, nor does it detail any prerequisites or exclusions for the URL input. The cost hint implies a usage condition, but overall guidance is minimal.
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.