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check_compliance

Read-only

Use only when the user explicitly asks to check an existing report for compliance issues such as missing business purpose or policy violations. Creating a report, excluding Personal expenses, sharing a report, or billing a client is not a compliance request; never call this tool automatically as a preflight or follow-up for those workflows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reportIdYesReport ID to check
userRequestYesThe user's exact request explicitly asking for compliance, policy, audit, substantiation, or business-purpose analysis. Do not paraphrase or invent intent.

Schema Changelog

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

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -{
      -  "additionalProperties": true,
      -  "description": "Standard ExpenseBot tool result envelope. `message` is the human-readable summary the AI cites; `data` is the structured payload (totals, breakdowns, ids, etc.). On failure, `success` is false and `error` carries a code/message/hint triple.",
      -  "properties": {
      -    "data": {
      -      "additionalProperties": true,
      -      "description": "Structured payload. Shape varies per tool — common keys: total, breakdown, comparison, sampleMeta, ids, expenseId, reportId, signupUrl, results.",
      -      "type": "object"
      -    },
      -    "error": {
      -      "additionalProperties": true,
      -      "description": "Present only when success === false.",
      -      "properties": {
      -        "code": {
      -          "type": "string"
      -        },
      -        "hint": {
      -          "type": "string"
      -        },
      -        "message": {
      -          "type": "string"
      -        }
      -      },
      -      "type": "object"
      -    },
      -    "message": {
      -      "description": "Human-readable result text. Always present on success; prefer rendering this verbatim before any further reasoning.",
      -      "type": "string"
      -    },
      -    "sampleMeta": {
      -      "additionalProperties": true,
      -      "description": "Set when the underlying dataset was truncated. isTruncated=true means the agent saw a sample of `sampleCount` of `totalCount` rows; aggregate totals are still accurate.",
      -      "properties": {
      -        "isTruncated": {
      -          "type": "boolean"
      -        },
      -        "sampleCount": {
      -          "type": "integer"
      -        },
      -        "totalCount": {
      -          "type": "integer"
      -        }
      -      },
      -      "type": "object"
      -    },
      -    "success": {
      -      "description": "False on tool errors; check before reading `data`.",
      -      "type": "boolean"
      -    }
      -  },
      -  "type": "object"
      -}New value: +null
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the tool is safe to call. The description adds significant behavioral context by prohibiting automatic invocation and narrowing the operation to explicit user requests, which prevents the agent from using it unnecessarily. No contradictions 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.

Conciseness5/5

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

The description is only two sentences but packs in the exact trigger condition, the resource, the topic, and explicit exclusion scenarios. There is no filler, and the most important usage constraint is front-loaded in the first phrase.

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?

The combination of description and fully documented schema covers everything the agent needs to correctly invoke the tool: exact parameters, exact usage constraints, and safety profile. No output schema exists, but the invocation context is complete and the likely return value is sufficiently evident from the purpose.

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

Parameters3/5

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

Schema coverage is 100%, and both parameters already have clear, detailed descriptions. The tool description reinforces the context behind the userRequest parameter, but it does not add new semantic detail beyond what the schema already provides. Since the schema carries the full burden, a baseline of 3 is appropriate.

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 clearly states a specific verb and object: check an existing report for compliance issues such as missing business purpose or policy violations. It also draws the boundary against non-compliance workflows, so an agent can distinguish it from related tools like fix_compliance and check_tax_deductibility.

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 is explicit about when to use the tool: only when the user explicitly asks for a compliance check of an existing report. It also gives hard exclusions for report creation, personal expense exclusions, sharing, and client billing, and explicitly forbids automatic invocation as a preflight or follow-up. This leaves no ambiguity.

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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TDQS

A3.6/5.0
Disambiguation3/5

Most tools are explicitly scoped, but several analytics/retrieval tools overlap in purpose, such as get_spending_summary vs get_deep_analytics vs get_monthly_books_review, and generic search vs search_expenses vs search_knowledge. The detailed descriptions help, but an agent still has to carefully choose between near-equivalent options like correct_expenses vs update_expense and the three add_income variants.

Naming Consistency5/5

Tool names consistently use lower_snake_case with a recognizable verb prefix: get_*, list_*, add_*, create_*, check_*, scan_*, search_*, and whatif_*. Minor exceptions like fetch and search are still terse retrieval verbs rather than a different naming style, so the overall pattern is predictable.

Tool Count1/5

With 59 tools, this exceeds the 50+ threshold for an extreme tool count and creates a heavy selection surface for an agent. Even though ExpenseBot covers many subdomains, many get_/list_/add_ variants could be consolidated into fewer parameterized tools. The count undermines the otherwise clear naming structure.

Completeness3/5

The surface is strong for creating, reading, and updating expenses, reports, invoices, and Gmail scans, but there are notable lifecycle gaps: no delete/void tools for expenses, income, reports, or invoices, and no update tool for income. Several descriptions explicitly redirect unsupported edits to the web app, confirming that the assistant cannot complete those workflows directly.