get_dealsSearch wafergraph's semiconductor & AI supply-chain M&A corpus (74 acquisitions/mergers, including notable terminated attempts) by title/summary substring and/or segment. Returns a compact list capped at 30 with a total match count.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Case-insensitive substring match against deal title and summary. | |
| segment | No | Filter to deals where at least one named party is a company in this taxonomy segment id. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the corpus size, result limit (30), and that it returns a total match count. It implies read-only behavior via 'Search'. No contradicts or omissions for a search tool, though it doesn't mention authentication or rate limits.
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?
Two sentences, precise and front-loaded. Every word adds value: resource scope, corpus size, search criteria, result limit, and output features. No redundancy.
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?
Despite no output schema, the description covers return format (compact list, capped at 30, total match count). It explains both parameters adequately. Given the tool's simplicity, completeness is high.
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 coverage is 100%: both parameters have clear schema descriptions. The description adds context about 'title/summary substring' and 'segment' but does not significantly enhance what the schema already provides. A baseline score of 3 is appropriate.
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 verb 'Search' and identifies the specific resource: 'wafergraph's semiconductor & AI supply-chain M&A corpus (74 acquisitions/mergers, including notable terminated attempts)'. This provides a precise, differentiated purpose from sibling tools like get_company or search_companies, which focus on companies or supply chains.
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 explains that the tool searches by 'title/summary substring and/or segment', and notes result constraints ('capped at 30 with a total match count'). It implicitly guides when to use (M&A searches) but does not explicitly contrast with siblings or mention when not to use it.
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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Each tool has a clearly distinct purpose: analyzing portfolio exposure, comparing companies, finding chokepoints, getting company details, country exposure, deals, segments, supply chain walk, and company search. No overlap in functionality.
All tool names follow a consistent verb_noun pattern using snake_case (e.g., 'get_company', 'search_companies', 'find_chokepoints'). The convention is uniform and predictable.
9 tools is well-scoped for a specialized supply chain analysis server. Each tool serves a distinct query or analysis need without being too few or too many.
The tool set covers core operations: search, detail, comparison, portfolio analysis, chokepoint identification, country exposure, deals, and supply chain graph traversal. Minor gaps like historical trends or alerts exist but do not hinder primary use cases.