Scrape LinkedIn URLs
scrape_linkedin_urlsFull records for LinkedIn profile, company, job or post URLs you already have.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Profile | |
| urls | Yes | ||
| find_emails | No |
scrape_linkedin_urlsFull records for LinkedIn profile, company, job or post URLs you already have.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Profile | |
| urls | Yes | ||
| find_emails | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden and largely fails it. "Full records" hints at completeness of return data, but there is no mention of cost, rate limits, the 1000-URL batch ceiling, authentication, or that find_emails performs an additional (potentially sensitive) enrichment pass.
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?
A single front-loaded sentence with no filler. It is efficient, though its brevity is partly the cause of the missing guidance rather than a sign of disciplined editing.
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 3-parameter batch tool with no annotations, no output schema, and 0% schema coverage, the description is too thin. An agent cannot tell what a "full record" contains, whether the call is billable or slow, or how find_emails changes the operation.
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 0%, so the description must compensate for all three parameters. It only implicitly covers `type` by listing the four entity kinds (already captured by the enum), and says nothing about `urls` (the required array, max 1000) or the `find_emails` boolean, which has real behavioral consequences.
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 states a clear outcome ("Full records") and names the entity types covered (profile, company, job, post), and the phrase "URLs you already have" distinguishes it from the discovery-oriented siblings like find_linkedin_leads and find_creators. It stops short of an explicit verb and does not name a sibling, but an agent can identify what the tool returns and for which inputs.
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?
"URLs you already have" gives clear selection context: use this when URLs are known, rather than for discovery. It does not explicitly name the alternative find_* tools or state when not to use it, but the condition that selects this tool is legible.
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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