Profile CSV, TSV, JSON, JSON Lines, or YAML records for field types, nulls, uniqueness, and representative samples.
| Name | Required | Description | Default |
|---|---|---|---|
| format | Yes | Source format; use jsonl for JSON Lines or NDJSON | |
| content | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | Structured Dataset profile result | |
| meta | Yes | ||
| service | Yes | ||
| version | Yes | ||
| request_id | Yes | Unique request identifier |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds clarity about what the tool produces (field types, nulls, uniqueness, samples), which is useful behavioral context beyond the annotations.
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?
One sentence, 15 words, front-loaded with the verb and resource. Every word earns its place.
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 read-only profiling tool with good annotations and an output schema, the description is sufficient. It would benefit from noting any sampling limits or behavior with malformed input, but it's broadly complete.
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 50% (format is described; content is not). The description implicitly covers the format parameter by listing supported formats, but gives no additional detail about content (e.g., how to provide data or limits beyond schema maxLength).
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 uses a specific verb ('Profile') and clearly identifies the resource (CSV, TSV, JSON, JSON Lines, YAML records) and the outputs (field types, nulls, uniqueness, representative samples). It distinguishes itself from sibling tools like data.schema (schema inference) and data.clean (cleaning).
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 it should be used when you need to understand the structure and quality of tabular or JSON data. It does not explicitly mention alternatives or exclusions, but the purpose is clear enough for selection.
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 into clear domain prefixes (crypto, data, developer, document, research, web) and each tool name describes a specific function; however, a few umbrella tools like web.full-audit and data.contract overlap with their more targeted counterparts, creating minor ambiguity.
All tool names follow a consistent pattern: a domain prefix, a dot, and a hyphenated lowercase compound name (e.g., crypto.base-block-inspect, web.seo-audit). This makes naming predictable and easy to scan.
At 63 tools, the surface area is very large and exceeds the 50+ threshold for extreme mismatch. While the tools are organized into six domains, the sheer number makes it difficult for an agent to select efficiently, and some tools are bundled combinations of others.
Each domain offers a thorough set of operations: crypto covers address, account, block, contract, events, gas, and transaction inspection; data covers cleaning, conversion, schema, and validation; developer covers code review, dependency/license audits, and test generation; research covers SEC, OFAC, GLEIF, and USAspending; web covers extraction, SEO, security, and performance. No obvious dead ends exist for the read-only/inspection purpose.