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DingDawg Agent Spend Policy MCP

Official
by dingdawg

Evaluate agent spend-policy eligibility

evaluate_spend_policy
Read-onlyIdempotent

Evaluate a proposed agent spend against a policy, returning ELIGIBLE, DENY, or STEP_UP with stable reason codes. Serves as local policy evidence, not payment authorization.

Instructions

Deterministically evaluates a proposed action against caller-supplied policy. This is local policy evidence only: it never authorizes, signs, sends, settles, custodies, or records a payment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYes
policyYes
evaluationTimeYes
alreadySpentMicrosYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
outcomeYes
reasonCodeYes
Behavior5/5

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

The description goes well beyond annotations by stating 'deterministically' and enumerating all the things it never does: 'never authorizes, signs, sends, settles, custodies, or records a payment.' This is critical behavioral context that annotations (readOnly, idempotent, non-destructive) do not fully convey, especially the deterministic nature and the explicit non-authorization boundary.

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 two sentences long, with the primary action in the first sentence and critical boundary statements in the second. It is front-loaded with the main purpose and wastes no words. Every clause adds value.

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

Completeness4/5

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

The tool has complex nested parameters and no parameter descriptions, but the output schema exists (not shown) and annotations provide safety hints. The description clearly conveys the tool's role as a local, deterministic policy check and its non-payment-execution boundary. It could add more detail about the evaluation result (e.g., what the response contains), but the output schema likely covers that, so the description is reasonably complete.

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?

The input schema has 0% description coverage for its 4 parameters, so the description must compensate. It only loosely references 'proposed action' and 'caller-supplied policy' (mapping to 'action' and 'policy'), but it does not explain 'evaluationTime' or 'alreadySpentMicros'. The description adds minimal parameter-level meaning, leaving users to infer the rest from schema structure alone.

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 the tool 'deterministically evaluates a proposed action against caller-supplied policy,' specifying the exact verb and resource. It distinguishes the tool's purpose from typical payment execution by explicitly listing what it never does (authorize, sign, send, settle, custody, record). This makes it distinct even without sibling tools.

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

Usage Guidelines3/5

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

The description implies usage for policy evaluation ('Deterministically evaluates a proposed action against caller-supplied policy') and adds the context 'local policy evidence only.' However, it does not explicitly state when to use this tool versus other potential approaches, nor does it provide exclusions or alternatives (though no siblings exist). The guidance is present but mostly implicit.

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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