Governed web research synthesis with cryptographic receipt. Agent submits a research question; returns an AI synthesis across live web sources with every source domain bound into an ML-DSA-65 signed receipt anchored on Base.
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, covering safety. The description adds valuable context: the synthesis is 'governed', every source domain is bound into an ML-DSA-65 signed receipt, and the receipt is anchored on Base—non-obvious implementation details beyond the safe-read annotation.
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 two sentences, efficiently packed. The first sentence introduces the core concept and the second explains the flow. Some jargon (ML-DSA-65, Base) may be unfamiliar but isn't wasted; overall it's appropriately sized.
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 tool with one parameter and no output schema, the description covers the input (research question) and the output behavior (AI synthesis plus signed receipt). It doesn't specify the return format or what 'governed' means, but it is sufficiently complete for the tool's 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?
With a single parameter and schema coverage 0%, the description clarifies that the parameter is a 'research question', adding meaning beyond the bare `question` field in the schema. However, it doesn't mention the 5-500 character constraints or provide guidance on question specificity, so compensation is partial.
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 function: 'Agent submits a research question; returns an AI synthesis across live web sources with every source domain bound into an ML-DSA-65 signed receipt.' This is a specific action (web research synthesis) with a distinctive output (signed receipt), distinguishing it from sibling synthesis tools.
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 usage: when a governed web research synthesis with a signed receipt is needed. It does not explicitly mention alternatives or exclusions among the many sibling synthesis tools, so the guidance is only implicit, not explicit.
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.