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mcp-server

by com.koongo

get_feed_output

Read-only

Read the CURRENT output of a feed's already-generated file WITHOUT regenerating it — so it works on a read-only connection (unlike export_feed, which regenerates the file and is a write action). Use this to see what a feed currently contains: its live download URL, product count and a sample of the actual rows, instead of downloading the file and counting it yourself. Inputs: feed_id (the feed's id from list_feeds / create_feed), project_id (OPTIONAL — inferred for a single-project customer; project_id_required if they have several, then call list_projects), sample_size (default 20, max 20), offset (default 0 — skip this many rows to read a deeper page; page by increasing offset in steps of sample_size). Returns {feedId, projectId, feedCode, fileFormat, feedUrl, fileExists, fileSize, productsCount, lastGeneratedAt, sampleRowCount, sample:[{field: value}, ...]}. feedUrl is the public download URL of the current file; productsCount is exactly the number of rows in that file; lastGeneratedAt is when it was last generated. fileExists:false means the feed has not been generated yet — call export_feed, then poll get_feed_status until it is generated. The sample rows are real feed data returned only after you set acknowledge_sensitive:true. The first call (flag absent/false) returns {sensitiveGate:{confirmationRequired:true, itemCount, fields, kind}} with sample EMPTY (all the metadata is still returned) — present that gate to the USER, get approval, then re-call with acknowledge_sensitive:true to receive the rows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
offsetNoSkip this many rows to read a deeper page of the feed; page by increasing offset in steps of sample_size (default 0).
feed_idYes
project_idNo
sample_sizeNoHow many feed rows to return in the sample (default 20, max 20).
acknowledge_sensitiveNoSet true only AFTER the user approves seeing real data. When absent/false the tool returns a sensitive gate with an empty sample (metadata still included); with true it returns the actual rows.

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description reinforces this by stating it works on a read-only connection and does not regenerate. It additionally discloses the sensitive gate behavior, fileExists:false handling, and the distinction between metadata-only and row-returning responses, which goes well beyond the 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 long but every sentence earns its place by covering usage, parameters, return fields, and edge cases. It front-loads the core purpose and differentiation before diving into details, though it is slightly dense and could be broken into clearer sections. Still, for a tool this behavior-rich, the density is justified.

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?

With no output schema, the description carries the full burden of explaining return values, and it does so thoroughly: every returned field is listed and key fields like feedUrl, productsCount, lastGeneratedAt, and sensitiveGate are semantically defined. The complete workflow from first call to gated sample to pagination is described, making the tool fully understandable without external context.

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?

Schema coverage is only 60%, but the description compensates fully: it explains feed_id provenance, project_id inference rules, sample_size default/max, offset paging semantics, and acknowledge_sensitive gating. This adds meaning beyond the raw schema, especially the non-obvious behavior of acknowledge_sensitive and the single-project inference for project_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 states a specific verb ('Read') and resource ('CURRENT output of a feed's already-generated file') and immediately distinguishes itself from export_feed by noting it does NOT regenerate the file. An agent can clearly differentiate this tool from its closest sibling without opening the schema.

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?

It explicitly tells when to use the tool ('see what a feed currently contains'), names the alternative export_feed and why it differs, and gives conditions for optional project_id. It also explains the sensitive-data workflow and pagination approach, leaving no ambiguity about how to proceed.

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

A4.2/5.0
Disambiguation4/5

Tools are organized around distinct resources (ads, marketplaces, feeds, orders, rules, sources) with clear action verbs, and descriptions explicitly disambiguate near-pairs like get_feed_status vs ad_status or set_feed_filter vs set_feed_attribute_filter. A few similarly named status/action pairs (e.g. ad_status vs get_ad, run_ad_item_action vs run_ad_operation) require careful reading, but overall the purposes are separable.

Naming Consistency4/5

The overwhelming majority follow a consistent verb_noun snake_case pattern (list_*, get_*, create_*, set_*, run_*, test_*). Minor deviations like ad_status and marketplace_status (noun-based status tools) and koongo_knowledge break the pattern slightly, but the convention is clearly recognizable and predictable.

Tool Count1/5

At 105 tools, the surface is extreme and far beyond the 50+ threshold, even for a complex e-commerce integration domain. Much of the bloat comes from systematic triplication across ads, marketplaces, and feeds (e.g. three nearly identical map_*_attribute tools, three list_*_items, three get_*_report) that a generic resource parameter could have consolidated.

Completeness4/5

The toolset covers the full lifecycle of feeds, ads, marketplaces, order connections, rules, and imports, including create/read/update/delete, status monitoring, item-level actions, validation, repair, and restore. Minor gaps exist, such as no delete for standalone order connections and limited update capabilities for some entities, but these are workable and do not create dead ends for the core workflows.

Resources