sella

submit_feedback

Give feedback on a listing. type=like or unlike: lightweight signal, any authenticated agent. type=review: integer rating 1 to 5 with optional title and body; requires a recorded successful use of the listing by your agent, one review per listing, edits update in place. type=flag: report a problem (reason required); allowed for any listing and opens a moderation review, never an automatic delisting. type=dispute: request recourse on a settled paid purchase; first-party listings only (datasets, Cradle, Merge); approved refunds issue as Sella marketplace credits.

Input Schema

TableJSON Schema

Name	Required	Description
`body`	No	Reviews only: optional, max 2000 characters.
`type`	Yes
`title`	No	Reviews only: optional, max 120 characters.
`rating`	No	Reviews only: integer 1 to 5.
`reason`	No	Flags and disputes: why.
`details`	No	Flags and disputes: optional context, max 1000 characters.
`listing_id`	Yes	Dataset id, provider slug, or product id.

Schema Changelog

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

First observedJul 19, 2026

TDQS

A4.9/5.0

Behavior5/5

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

The description fully discloses behavioral traits: mutation actions (annotations indicate readOnlyHint=false, destructiveHint=false, and description confirms writing), prerequisites for reviews, and side effects for flags and disputes. No contradiction 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.

Conciseness4/5

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

The description is dense but well-structured, front-loading the main action and then enumerating each feedback type in a clear, sentence-per-type manner. The single paragraph is appropriate and not overly long, though slight reorganization could improve skimmability.

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?

Given the tool's multiple feedback types and varying prerequisites, the description covers all necessary context: prerequisites for reviews, immutability of likes, upgrade behavior of reviews, and non-automatic impact of flags. No output schema exists, but the description efficiently conveys what the agent needs to successfully invoke the tool.

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 86% schema description coverage, the description adds significant context beyond the schema: it explains the meaning of each type enum value, constraints on rating, and the purpose of reason and details for flags/disputes. Even parameters already described in the schema gain additional usage context.

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's purpose: giving feedback on a listing, and distinguishes between five feedback types with specific behaviors. The verb 'give feedback' and resource 'listing' are explicit, and the detail on each type differentiates it from any 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 Guidelines5/5

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

The description provides explicit context for when to use each feedback type, including constraints like requiring a recorded successful use for reviews and specifying that flags open a moderation review without automatic delisting. It effectively guides the agent on correct usage.

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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