validate_cpfValidate a Brazilian CPF (Cadastro de Pessoas Físicas, the 11-digit personal tax ID) checksum. Pure computation, no lookup — returns whether the two check digits are correct and the formatted form. Punctuation (123.456.789-09) is accepted.
| Name | Required | Description | Default |
|---|---|---|---|
| cpf | Yes | The CPF, e.g. "123.456.789-09" or "12345678909". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "cpf": "123.456.789-09"
+ },
+ {
+ "cpf": "12345678909"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive. The description adds behavioral traits: it is a pure computation (no lookup), returns check-digit correctness and formatted form, and accepts punctuation. This adds value beyond annotations without contradiction.
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 concise: two efficient sentences that front-load purpose and behavior. Every sentence adds value with no redundancy or filler.
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's simplicity (one parameter, clear annotations, no output schema), the description covers the essential aspects: what it does, how it works, acceptable input, and what it returns. It could mention return structure more explicitly, but overall it is sufficiently 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?
The input schema covers the parameter fully with description and examples. The description confirms accepted formats (with punctuation) but adds no extra semantic information beyond what the schema already provides. With 100% schema coverage, baseline 3 is appropriate.
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 identifies the tool's purpose: validating the checksum of a Brazilian CPF. It specifies the resource (CPF), the verb (validate), and scope (checksum computation, no lookup). It distinguishes from sibling tools like validate_cnpj and validate_clabe by being CPF-specific.
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 context: use it when you need to validate CPF checksum computationally. It mentions 'no lookup' which hints at limitations, but does not explicitly state when not to use it or provide alternatives. Given sibling tools exist, the context is clear enough.
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.
Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route the same 5,702 tools with only subtle differences, and ai_visibility_check is essentially a single-entity subset of scan_competitor_ai_presence. The memory trio (remember/recall/forget) and subscription tools also sit awkwardly alongside the data-query tools, making selection genuinely ambiguous.
Naming mixes verb_noun (validate_cnpj, resolve_entity, compare_entities), noun_verb (bet_research, entity_profile, recent_changes), and bare nouns with inconsistent suffixes (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk). The ask_pipeworx family uses inconsistent qualifiers (beta vs grounded), and validate_* is used for both checksum-only tools and full lookups.
36 tools is excessive for a server ostensibly named 'Latam Validate' — only 5 tools relate to LATAM validation while the rest form a sprawling general-purpose data and prediction-market platform. Many tools could be consolidated (the ask_pipeworx family, the polymarket_* family, the memory trio), suggesting the set is over-scoped for any single agent's typical workflow.
For the stated LATAM validation purpose, coverage is thin: only Brazil (CPF/CNPJ/CEP/banks) and Mexico (CLABE) are covered, with no validators for other LATAM jurisdictions. For the broader Pipeworx data platform, the surface is extensive but has notable gaps — grounded retrieval, claim verification, and arbitrage tools exist, yet many LatAm-specific data sources and common validation formats (RFC, RUT, DNI, CURP) are absent.