validate_cusipValidate a CUSIP (9-char US/Canada securities identifier, e.g. "037833100"). Checks structure + the check digit.
| Name | Required | Description | Default |
|---|---|---|---|
| cusip | Yes | A 9-character CUSIP, e.g. "037833100". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "cusip": "037833100"
+ },
+ {
+ "cusip": "594918104"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description adds behavioral detail about checking structure and check digit. It doesn't disclose the return format or error behavior, but with annotations covering safety, a 3 is appropriate.
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 one-sentence description is front-loaded with the action and includes an example. It is concise and every word contributes to understanding, with no wasted text.
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 validation tool with one parameter and no output schema, the description conveys the core behavior. It doesn't explicitly state the return value but implies a validity check, which is sufficient given the low complexity and annotations.
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 schema covers the cusip parameter fully with 100% coverage, including length and example. The description reinforces the format but doesn't add substantial new semantics beyond what the schema already provides.
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 the specific verb 'Validate' with the resource 'CUSIP' and details that it checks structure and the check digit. This clearly distinguishes it from sibling tools like validate_isin and cusip_to_isin.
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 clearly indicates the tool's purpose for validating a CUSIP identifier. However, it does not explicitly mention alternatives or when not to use it, so it provides clear context but lacks exclusions.
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.
Most tools have clearly distinct purposes, but there are overlapping clusters: the three ask_pipeworx variants and multiple polymarket analysis tools can cause selection ambiguity. Descriptions help, yet boundaries between entity_profile, compare_entities, recent_changes, and ask_pipeworx require careful reading.
Tool names almost all follow snake_case with verb-noun or verb-phrase structure (ask_pipeworx, validate_isin, list_subscriptions), and family prefixes like ask_pipeworx_* and polymarket_* are consistent. Minor deviations include one-word verbs (remember, recall, forget) and adjective-noun names (recent_alerts, recent_changes, entity_profile).
With 34 tools, the surface is well above the 25-tool threshold that typically feels heavy, even though the Pipeworx platform is broad in scope. The server named 'Isin' exposes a large toolkit far beyond its apparent identifier-focused purpose, making the count feel excessive.
The broader data-access, research, subscription, and utility workflows are well covered, including discovery, grounded queries, entity profiles, comparisons, and claim validation. Minor gaps include the lack of direct pipeworx:// URI reading and ISIN issuer resolution, but agents can work around these.