AI-capex / model-economy metrics: raises, cloud growth, memory prices, token costs (paid, $0.002/req or pass).
Args:
metric: optional metric family filter (e.g. "cloud_growth", "memory_price", "capital_raise").
limit: max metrics (1-50).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| metric | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds a cost-related behavioral note ('paid, $0.002/req or pass') and specifies the limit range, which is useful. However, with no annotations, it does not disclose the return format, pagination, or the nature of the operation beyond being a metrics lookup. It provides some context but not full transparency.
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, with a clear first sentence conveying the purpose and pricing, followed by a bulleted parameter list. No redundant information is present, and the structure front-loads the key message.
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?
For a tool with only two parameters and no output schema, the description covers purpose and parameters well. However, it lacks usage context, such as when to prefer capex over capex_summary, and does not describe the response structure, leaving the agent to infer behavior. It is moderately complete but not comprehensive.
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 schema has zero description coverage for parameters, but the description fully compensates by explaining both parameters with concrete examples ('cloud_growth', 'memory_price', 'capital_raise') and constraints ('1-50'). This adds significant meaning beyond the bare schema.
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 domain (AI-capex / model-economy metrics) and enumerates specific metric families, making the purpose understandable. However, it lacks an explicit verb like 'get' or 'list' and does not directly distinguish itself from the sibling tool 'capex_summary'.
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 about when to use this tool versus alternatives. The description only explains the parameters, with no mention of suitable scenarios, prerequisites, or exclusions. This is a clear gap given the large sibling toolset.
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.
Tools are grouped by domain (funding, deals, transcripts, etc.) and each has a specific focus: basic list, latest, search, or summary. While some pairs like deals/deals_search and funding/funding_latest could be confused, the descriptions clearly differentiate them. The boundaries are mostly clear, but the sheer number of tools requires careful reading.
Naming is inconsistent across the set. Some tools use bare nouns (funding, deals, catalogues), some use verb prefixes (get_article, list_threads, search_wire), and many use suffixes (_latest, _search, _summary). The position and style of modifiers vary between domains, making it difficult to predict tool names.
With 27 tools, the server is on the heavy end, which aligns with its terminal-style scope covering many distinct data domains (news, transcripts, funding, retail, model watch). The count is justified by the breadth, but it feels dense and could be split into smaller, more focused servers.
The server provides comprehensive coverage for most domains: listing, retrieving details, searching, and domain-specific variants (latest, hot, sentiment). Minor gaps exist, such as no way to fetch a specific funding event by ID or a latest deals tool, but these are easy workarounds.