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Glama

CDF Finance

by io.github.VessellTech

Insights de comportamento

behavior_insights
Read-only

Observações financeiras já analisadas e prontas para narrar (cada item já vem com um texto pronto em text): padrão de dia da semana por categoria, aumento de gasto nos dias após o recebimento de renda, e categorias que sobem nos fins de semana. Pode retornar lista vazia se nenhum padrão for detectado. Distinto de insights_highlight (só 1 destaque, prioriza atraso/orçamento) e das tools de anomalia específica (bill_anomalies, transport_routine, bill_concentration, best_card_day, subscriptions_overview), que olham sinais isolados diferentes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds valuable non-obvious behavior: it can return an empty list when no pattern is detected, and each result item comes with a pre-written narrative `text`. It also discloses the analytical scope, which goes beyond what the annotations or schema provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded with the core value ('already analyzed and ready to narrate'), then covers empty-list behavior and sibling differentiation. Every sentence earns its place, and the structure is easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero parameters, read-only annotations, an output schema, and a description that explains return behavior and distinguishes it from related tools, the agent has everything needed to invoke and interpret this tool correctly. The empty-list case and ready-made `text` field are explicitly addressed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters and schema description coverage is 100%, so there are no parameter semantics for the description to clarify. The baseline of 4 applies because there is nothing meaningful to add.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific purpose: returning pre-analyzed financial observations ready to narrate, with each item containing a ready-made `text`. It enumerates the exact behavior patterns covered (weekday-by-category, post-income spending increase, weekend category rises) and explicitly distinguishes the tool from insights_highlight and specific anomaly tools, making the resource and scope unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear when-not guidance by naming alternatives: insights_highlight for a single budget/delay-focused highlight, and bill_anomalies, transport_routine, bill_concentration, best_card_day, subscriptions_overview for isolated anomaly signals. This lets an agent route to the correct tool instead of behavior_insights.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All CDF Finance toolsInspect
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TDQS

A3.7/5.0
Disambiguation3/5

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.

Naming Consistency3/5

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.

Tool Count1/5

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.

Completeness4/5

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.

Resources