list_equity_valuationsHistórico completo de avaliações (EquityValuation: value/cost ao longo do tempo) de um investimento (id = id do investimento, não da avaliação), ordenado do mais recente para o mais antigo (asOf desc). Não filtra por período nem pagina.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Id do investimento (Equity), não da avaliação |
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / id / descriptionAdded value: +"Id do investimento (Equity), não da avaliação"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the operation as read-only and non-destructive. The description adds useful behavioral detail beyond annotations: full history, ordering by `asOf desc`, no period filtering, and no pagination. This is meaningful context for an agent deciding whether the call will return everything at once.
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 a single well-structured sentence that front-loads the core purpose, then adds ordering and limitation details. Every clause carries meaningful information and there is no redundant or filler content.
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 one-parameter read-only list operation with an output schema available, the description covers the essential aspects: what is returned, for which entity, in what order, and with what limitations. No critical behavioral or usage information is missing.
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 100% and the only parameter `id` already has a clear description in the schema. The tool description repeats the same clarification that `id` is the investment id, not the valuation id, so it adds little beyond the structured schema.
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 names a specific verb and resource: it lists the complete valuation history (EquityValuation) for an investment, sorted newest first. It also clarifies that the `id` refers to the investment, not the valuation, and the sibling context (add_equity_valuation, list_equities) makes the distinction clear.
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 makes the use case clear: retrieve the full historical valuation series for a specific investment. It also states boundaries by saying there is no period filtering and no pagination, which helps set expectations, though it does not explicitly name alternative tools or list when-not-to-use conditions.
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 tools are individually well-described and many cross-reference their closest neighbors, but the set contains several easily confused clusters: create_transaction/confirm_new_transaction, update_equity/add_equity_valuation, the invoice tools (current_invoice, next_invoice, list_pending_invoices, get_invoice), and the many analytics/projection tools. The descriptions help a careful reader, but with 81 tools an agent is likely to misselect among these overlapping surfaces.
CRUD operations consistently use create_/list_/update_/delete_ plus a resource noun, and all names are snake_case. However, there is a large second group of noun-phrase analytics tools (cashflow_forecast, spending_projection, categories_insights, transport_routine) plus one-off verbs such as can_afford, pay_invoice, and validate_current_invoices, so the naming convention is mixed even though it remains readable.
81 tools is far beyond the practical MCP tool surface and exceeds the rubric's 50+ extreme-mismatch threshold. Even if each tool maps to a real finance endpoint, the volume overwhelms an agent's context window and makes selection much harder.
The server covers the finance lifecycle extensively: accounts, cards, invoices, transactions, recurring rules, budgets, goals, debts, equities, categories, tags, cost centers, profile, projections, and insights all have working read/write paths. Minor gaps remain, such as no update/delete for tags and no direct update/delete for system-generated invoices, but agents can usually work around these.