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sella

by io.github.sella-network

policy_check

Read-only

Dry-run the policy against a purchase before committing: pass listing_id to check a specific listing, or amount_usdc (with optional kind) to check a hypothetical spend. Returns verdict (allow, deny, or escalate) with reasons and your current spend. No receipt or approval is created. Plan with this to avoid denials at payment time.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNoKind for the hypothetical check.
listing_idNoListing to check (dataset id, provider slug, or product id).
amount_usdcNoHypothetical spend to check when no listing_id is given.

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The description explicitly states 'Dry-run', 'No receipt or approval is created', which aligns with the readOnlyHint annotation. It adds beyond annotations by explaining the return value (verdict, reasons, current spend) and confirms no side effects.

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?

Two sentences effectively communicate purpose, usage modes, return value, and side effects. No wasted words, and critical information is front-loaded.

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 description adequately covers what the tool does and returns, given no output schema. It could be improved by detailing the response format or error cases, but it is sufficient for basic usage.

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

Parameters5/5

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

With 100% schema coverage, the description adds significant value by explaining the logical grouping of parameters (listing_id vs amount_usdc) and the role of kind, clarifying usage scenarios beyond the schema.

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 is a dry-run policy check before a purchase, with two explicit use cases: checking a specific listing or a hypothetical spend. It distinguishes itself from siblings like policy_get and explain_decision by focusing on pre-commitment checking.

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

Usage Guidelines4/5

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

The description provides clear guidance on when to use the tool ('before committing a purchase') and its purpose to avoid denials. It does not explicitly mention when not to use or alternatives, but the context is adequate.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but the deprecated tools (list_datasets, list_market_apis, search_datasets, try_dataset) overlap with modern replacements (search_catalog, get_listing). Some functional overlap exists between get_activity and charge_list, but descriptions clarify their scopes. Overall, an agent can usually tell tools apart, with a few legacy remnants.

Naming Consistency5/5

Tool names follow a consistent snake_case verb_noun pattern (browse_catalog, business_start, charge_create, etc.). Even the deprecated tools adhere to the same style. There are no mixed conventions or vague verbs like 'process' or 'run'. The naming is highly predictable.

Tool Count3/5

At 52 tools, this is a large surface. The domain is broad (marketplace buying/selling, business management, policy, storefront, distribution, authentication), so many tools are justifiable. However, four deprecated tools could be pruned, and the count is on the heavy side compared to typical MCP servers. It feels overengineered, yet each tool addresses a distinct facet of the platform.

Completeness4/5

The toolset covers the full lifecycle: discovery, evaluation, purchase, delivery, feedback, business management, policy, storefront, and distribution. Gaps are minor—for example, no direct way to list all services with full details without service_list, but that exists. The deprecated tools indicate ongoing migration to a consolidated search surface, suggesting good coverage. A few small gaps remain (e.g., no explicit 'update listing' for buyers, but that may not be needed).

Resources