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Glama

finance_anomaly_investigation

Destructive

Investigate finance anomalies by describing the issue and optional inputs to uncover root causes, assess risk, and guide corrective actions.

Instructions

Run the finance domain agent action finance_anomaly_investigation.

Routes through the platform's domain-agent dispatcher under your JWT, tenant, and company scope.

Args: message: Free-text objective for the action. inputs: Optional JSON string of structured inputs for the action.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputsNo{}
messageNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.3/5.0
Behavior3/5

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

The annotations already flag destructiveHint=true, readOnlyHint=false, and idempotentHint=false, so the safety profile is covered. The description adds some useful context by stating it runs under the JWT, tenant, and company scope. However, it discloses nothing about what side effects or changes the underlying action may have beyond the annotations.

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

Conciseness3/5

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

The description is short and uses a clean Args structure, but the first sentence is a redundant restatement of the tool name. The more informative routing and parameter notes are buried after that, making the structure slightly less effective than it could be.

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

Completeness2/5

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

With many sibling tools in the finance anomaly space and a generic dispatcher pattern, the description lacks the critical details an agent needs: what the action actually does, when to select it over finance_fraud_investigation or finance_anomaly_fraud_case_file, and what structured inputs the action consumes. The existence of an output schema helps, but the selection and invocation context remains incomplete.

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

Parameters2/5

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

Schema description coverage is 0%, so the description carries the burden of explaining parameters. It only says message is a 'free-text objective' and inputs is an 'optional JSON string of structured inputs,' without describing what the finance anomaly investigation action expects inside that JSON or what kinds of objectives are valid. This is too shallow for an agent to construct meaningful inputs.

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

Purpose2/5

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

The description opens with 'Run the finance domain agent action finance_anomaly_investigation,' which is a tautology—it restates the tool name without saying what the action actually accomplishes. The rest describes routing through a dispatcher, not the domain behavior. An agent cannot tell this apart from similar siblings like finance_fraud_investigation or finance_anomaly_fraud_case_file.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus alternative finance anomaly/fraud tools, or when free-text message vs. structured inputs should be preferred. The description gives no exclusions, prerequisites, or selection conditions.

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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