Pesquisa contatos (contatos.pesquisa.php). Paginado. search busca por nome; filters (JSON) aceita cpf_cnpj, situacao, idVendedor etc.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | ||
| search | No | ||
| account | No | ||
| filters | 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 declare readOnlyHint=true and destructiveHint=false, indicating safe read operation. The description adds that it is paginated and that filters accept specific fields (cpf_cnpj, situacao, idVendedor), which is useful context beyond annotations. No 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?
Description is concise (two sentences) and front-loaded with the primary action and pagination info. No waste, but could be more structured with parameter details.
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 has 4 parameters, 0% schema coverage, no output schema, and no enum hints, the description is reasonably informative but incomplete. It covers key search and filter semantics but omits 'page' and 'account', and no return format is mentioned (though output schema is absent, so the description should at least hint at return behavior). The annotations mitigate some completeness needs.
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 has 0% description coverage, so the description must compensate. It explains 'search' (search by name) and 'filters' (JSON with accepted fields), but 'page' and 'account' are left undocumented. The description adds meaning for two of four parameters, but the other two remain unclear.
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 lists contacts with pagination, and the name 'tiny_list_contacts' is specific. It distinguishes from sibling tools like 'tiny_get_contact' by implying a list/search operation, but doesn't explicitly compare to 'tiny_get_contact' or other list 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 for searching contacts by name or filters, but provides no guidance on when to use this tool versus alternatives like 'tiny_get_contact' for single contact retrieval. No explicit when-to-use or when-not-to-use scenarios are given.
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.
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.