get_dataset_statsThe honesty tool: what this dataset actually contains and where it is thin. Live-computed per-field coverage for companies and deals, last_verified staleness distribution, supply-chain edge coverage, data source mode, and a plain-words list of known limitations. Call this before treating an absence of a company, deal, or edge as evidence it doesn't exist in the real market.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It mentions 'live-computed' and lists what it covers, but does not disclose performance implications, rate limits, or whether it modifies data. The read-only nature is implied but not confirmed.
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, no wasted words. The first sentence labels it 'the honesty tool,' front-loading purpose. Every sentence provides distinct value: what it contains and when to call it.
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?
The description covers what the output includes (coverage, staleness, limitations). Without an output schema, the agent relies on this list. It is complete enough, but could specify return format (e.g., object keys).
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 tool has zero parameters and schema coverage is 100%. The description adds no parameter detail because none are needed. Per guidelines, baseline 4 applies.
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: provides dataset stats like per-field coverage, staleness, limitations. It uses a specific verb 'get' on 'dataset stats', and the 'honesty tool' metaphor distinguishes it from sibling tools like get_company or get_deal.
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 gives explicit when-to-use: 'Call this before treating an absence... as evidence it doesn't exist.' It provides clear context for usage, though it doesn't explicitly state when not to use or name alternatives.
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.
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.