Real-time regulatory news synthesis for a topic or agency (SEC, FTC, OCC, CFPB, FDA). Returns current enforcement and rule-change developments synthesized from live sources with a signed receipt proving which sources were consulted.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | ||
| agency | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds valuable context: results are real-time, synthesized from live sources, and include a signed receipt for source verification. This goes beyond the annotations and helps set expectations.
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 a single sentence that packs in purpose, agency examples, output details, and the signed receipt feature. It is free of fluff and front-loads the core function, though it could be split into two sentences for easier scanning.
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 has only two simple parameters, no output schema, and strong read-only annotations, the description adequately covers what it returns (enforcement/rule-change developments), how it works (live sources, synthesis), and a distinguishing feature (signed receipt). It does not explain the return format, but this is not a major gap for a tool of this complexity.
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 names example agency values (SEC, FTC, OCC, CFPB, FDA) and implies the topic is a free-text subject. However, the phrase 'for a topic or agency' is ambiguous regarding whether topic is always required, and it doesn't detail the relationship between the two parameters.
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 synthesizes real-time regulatory news for a topic or specific agency, listing examples (SEC, FTC, OCC, CFPB, FDA). It distinguishes itself from sibling tools by focusing specifically on regulatory enforcement and rule-change developments.
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 provides clear context for when this tool is appropriate: when regulatory news on a topic or agency is needed. It lists relevant agencies, which helps with selection. However, it does not explicitly mention alternatives or exclusions, so it stops short of full guidance.
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.
Multiple tools overlap significantly: get_vendor_benchmark and get_vendor_market_rate both return pricing benchmarks with median/low/high; get_industry_spend_benchmark, get_industry_spend_profile, get_category_spend_benchmark, and get_spend_by_company_size all address spend benchmarking; get_saas_market_intelligence, get_category_ai_leaders, get_sector_ai_intelligence, and get_market_intelligence_brief all cover AI citation and market themes. These overlapping purposes make tool selection ambiguous.
All tools follow the 'get_' prefix consistently, creating a predictable pattern. However, the object naming is inconsistent in ordering (e.g., get_category_ai_leaders vs get_top_vendors_by_category) and some use 'synthesis' vs 'signal' vs 'benchmark' without a clear rule. Overall, the pattern is readable and consistent.
With 45 tools, the surface is extremely large. While the server's scope is broad (market intelligence, vendor benchmarks, regulatory data, etc.), this count overwhelms an agent and dilutes focus. Many related tools could be consolidated (e.g., vendor benchmarking into one tool with modes). A typical well-scoped server would be 3-15 tools.
The server covers numerous domains with read-only intelligence, including market trends, vendor pricing, compensation, regulatory, and patent data. However, there are gaps within those domains: no historical trend comparison, no side-by-side vendor comparison across multiple metrics beyond alternatives, and no write or action capabilities. The breadth is impressive, but the depth is uneven.