linkedin_people_search_listSearch LinkedIn people by first and/or last name. Returns a list (use cursor when paginated).
| Name | Required | Description | Default |
|---|---|---|---|
| lastName | No | Last name filter. At least one of `firstName` or `lastName` is required. | |
| firstName | No | First name filter. At least one of `firstName` or `lastName` is required. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already mark this as a read-only operation, so the description doesn't need to restate that. It adds useful behavioral context beyond the annotations by mentioning that results are returned as a list and that a cursor should be used for pagination. This helps the agent handle large result sets appropriately.
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 extremely concise: two sentences that state the purpose and the key behavioral note about pagination. It is front-loaded with the primary action and adds the return type and pagination detail, with no wasted 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 two-parameter search tool with no output schema, the description is adequately complete. It covers the main use case, the return type (list), and pagination handling. It doesn't describe result fields, but that's not necessary here given the tool's simplicity and the existing annotations.
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?
The schema description coverage is 100%, with both `firstName` and `lastName` already documented as filters and the requirement that at least one is present. The description adds no new parameter semantics beyond what the schema already states, so the baseline score of 3 is appropriate.
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 tool searches LinkedIn people by first and/or last name. It specifies the resource (people) and the filtering criteria, and the tool name aligns perfectly. It distinguishes itself from other LinkedIn search tools (jobs, posts, etc.) by explicitly focusing on people.
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 clearly implies when to use it: when you need to find LinkedIn people by name. It provides a clear context for use but does not mention alternatives or explicitly state when not to use it. Since there are many related LinkedIn search tools, some exclusion guidance would be helpful, but the context is clear enough for an agent to infer typical usage.
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.