sella

get_listing

Read-only

Full pre-buy detail for one listing by id: the common evidence card, kind-specific detail (datasets: trial preview, structure, judge review, field coverage; APIs: endpoints with input schemas, reliability, compliance, links; products: usage), and a pay quote (APIs include platform fee and payable chains). Free. Dataset ids are 24-character hex; API providers use their slug; products are sella-cradle and sella-merge.

Input Schema

TableJSON Schema

Name	Required	Description
`kind`	No	Optional check: error if the resolved listing is a different kind.
`listing_id`	Yes	Dataset id, provider slug, or product id from search_catalog results.
`required_fields`	No	Datasets only: schema fields to verify before purchase (returns field coverage).

Schema Changelog

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

Changed1 schema field changedAug 4, 2026

changedInput schema / properties / kind / enum

Previous value: -[
-  "dataset",
-  "api",
-  "workflow",
-  "native"
-]New value: +[
+  "dataset",
+  "api",
+  "workflow",
+  "native",
+  "service"
+]

First observedJul 19, 2026

TDQS

A4.3/5.0

Behavior4/5

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

The annotations already declare readOnlyHint=true, so the description needed only to add extra context. It adds 'Free' and clarifies expected id formats for each kind. However, it lists kind-specific details only for datasets, APIs, and products, leaving workflow/native/service undefined, which is a minor transparency gap.

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

Conciseness4/5

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

The description is dense but not overly verbose. It front-loads the main purpose and packs useful details in a compact form. The parenthetical breakdown of kind-specific content is efficient, though the sentence structure is a bit run-on.

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?

In the absence of an output schema, the description thoroughly explains the return payload: common evidence card, kind-specific details, and pay quote including platform fee. However, it only elaborates on datasets, APIs, and products, omitting workflow, native, and service kinds, leaving a small completeness gap.

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 schema already describes all three parameters with 100% coverage. The description adds value by specifying id formats for listing_id ('24-character hex', 'slug', 'sella-cradle and sella-merge'), which is not present in the schema. This helps the agent correctly resolve the listing id.

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 opens with 'Full pre-buy detail for one listing by id', which specifies a clear verb (get), resource (listing), and scope (one by id). It enumerates the return contents (evidence card, kind-specific detail, pay quote), making it distinct from catalog search or browse tools.

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 clearly indicates when to use the tool: when a pre-buy detail for a specific listing is needed. It does not explicitly name alternatives or state when not to use it, but the pre-buy context and 'by id' scope are sufficient guidance.

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

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