facebook_marketplace_item_getGet details for a Facebook Marketplace listing by item URL.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | Public Facebook Marketplace item URL. | |
| itemId | No | Facebook Marketplace item identifier. Provide this instead of url when you already know the item id. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the read-only, external-fetch nature is covered. The description adds nothing beyond 'Get details', which is consistent but does not provide extra context such as error behavior or rate limits. It does not contradict annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loaded with the action and resource. It is minimal and to the point, with no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple get operation with no output schema, the description is adequate but has gaps: it does not clarify that at least one of url or itemId must be provided (both are optional in the schema), nor does it describe what 'details' includes. Given the tool's complexity, a bit more context would be helpful, but it is still usable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so both parameters (url and itemId) are documented with clear meaning. The tool description adds no new semantic insight beyond the schema, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get details') and the resource ('Facebook Marketplace listing'), and it distinguishes from search/list siblings by targeting a single item. However, it only mentions URL as the locator while the schema also supports itemId, so it's slightly incomplete.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like facebook_marketplace_search_list or facebook_marketplace_locations_search. The parameter description for itemId hints at when to use it, but the tool description itself is silent on tool selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool is clearly scoped to a specific platform and action (e.g., facebook_post_get vs instagram_post_get). Descriptions explicitly differentiate similar tools across platforms, and within-a-platform tools like tiktok_search_videos_list vs tiktok_search_hashtag_list have clear disambiguation notes.
All 167 tools follow a strict `platform_resource_action` pattern (e.g., youtube_video_comments_list). No mixing of styles—snake_case throughout, with consistent verb ordering (get, list, search, etc.).
The server has 167 tools, which is far beyond the typical well-scoped range of 3-15. While the broad multi-platform scope justifies many tools, this extreme number makes the tool surface overwhelming and difficult for an agent to navigate efficiently.
The tool set covers a wide range of platforms and operations including profile retrieval, post/video fetching, comments, search, transcripts, and ad library access. Minor gaps exist (e.g., no Facebook events or LinkedIn messaging), but the surface is comprehensive for a read-only data aggregation use case.