Model-watch events by lab name (paid, $0.003/req or pass).
Args:
lab: lab name fragment (e.g. "Anthropic", "OpenAI", "Meta").
limit: max events (1-20).
| Name | Required | Description | Default |
|---|---|---|---|
| lab | Yes | ||
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses pricing ($0.003/req or pass) and the limit range, which adds value. However, it does not describe the return format, error behavior, or confirm that this is a read-only operation, leaving some transparency gaps.
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 an Args block to structure parameter details. Every sentence adds useful information, with no wasted words or repetition.
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 simple two-parameter search tool, the description covers purpose, parameters, and pricing, making it largely complete. It does not mention return format or sibling alternatives, but given the low complexity and the absence of an output schema, this is acceptable.
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 description significantly enhances the parameters beyond the bare schema. It explains 'lab' as a name fragment with examples and 'limit' as max events with a range of 1-20. Since schema description coverage is 0%, this enrichment is crucial and well-executed.
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 that the tool retrieves model-watch events filtered by lab name, with concrete examples. The verb 'search' is in the name, and the resource and filtering scope are clear. It distinguishes from siblings like modelwatch and modelwatch_latest implicitly, though not by name.
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 implies this tool is for lab-specific queries via the required lab parameter, but it does not explicitly state when to prefer this over siblings like modelwatch or modelwatch_latest. No exclusions or alternatives are mentioned, so usage guidance is only implied.
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.