Cadastra um contato (cliente/fornecedor) — contato.incluir.php. data é um JSON string com os campos do contato conforme a doc oficial (comuns: nome, tipoPessoa 'F'|'J', cpf_cnpj, email, fone, endereco, cidade, uf, cep). Doc: tiny.com.br/api-docs (contato.incluir).
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| account | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already signal a non-read-only, non-idempotent write operation, and the description's creation semantics align with them. It adds the endpoint and doc reference, but does not disclose details like duplicate handling, auth requirements, or expected response shape. No contradiction with 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?
The description is one compact, front-loaded sentence that states the action first, names the endpoint, summarizes common fields, and points to the official docs. Every element earns its place with no 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?
For a two-parameter create tool with no output schema, the description covers the main 'data' parameter and connects to official documentation. But it omits the meaning of 'account', any expected return value, and prerequisites, leaving moderate gaps for correct invocation and result interpretation.
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 0%, so the description must compensate. It explains that 'data' is a JSON string and lists common accepted fields (nome, tipoPessoa, cpf_cnpj, etc.), but leaves 'account' undefined and does not specify JSON encoding or required field combinations.
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 opens with 'Cadastra um contato' (registers a contact), giving a specific verb and resource. It clarifies the contact can be a client or supplier and names the underlying endpoint (contato.incluir.php), clearly distinguishing it from sibling get/list contact 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 creation purpose is implied by 'Cadastra' and the resource type, so the intended context is understandable. However, there is no explicit when/when-not guidance or reference to alternatives, and the optional 'account' parameter is not given any usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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The tiny_* tools are clearly distinct by resource and action, but the generic platform tools blur together: marketplace also handles report_bug, install/uninstall, token/connect links, and toolkit state, overlapping with authenticate, connect, report_bug, and toolkit_info. An agent could easily call marketplace for something a dedicated tool already covers, or confuse connect with authenticate.
The Tiny ERP tools follow a clean verb_noun pattern (tiny_list_orders, tiny_get_product, tiny_create_contact), but the platform tools use short unprefixed verbs (authenticate, connect, marketplace, report_bug, show_version, toolkit_info). Two internally consistent naming schemes coexist, so the overall set feels mixed rather than chaotic.
At 20 tools, the count is borderline heavy and mixes two distinct concerns: Tiny ERP operations and mcp.ai platform administration. The ERP tools are each reasonably scoped, but the generic tools could likely be consolidated, especially marketplace, which already absorbs several capabilities.
The ERP surface is heavily read-oriented: contacts, orders, invoices, products, payables, and receivables all support list/get, but only contacts have a create operation. There are no update/delete operations for any entity and no way to create orders, invoices, or products, which creates significant dead ends for real business workflows.