get_linkedin_profiles
List LinkedIn profiles this project tracks. Does not spend credits. A scheduled profile scan spends 30 credit(s). A lead email lookup spends 5 credit(s).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
List LinkedIn profiles this project tracks. Does not spend credits. A scheduled profile scan spends 30 credit(s). A lead email lookup spends 5 credit(s).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely new behavioral context no annotation provides: this call is free, while a scheduled scan costs 30 credits and an email lookup costs 5, which shapes how an agent budgets calls.
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?
Three short sentences, with the purpose front-loaded and the zero-cost property immediately after. The two credit-comparison sentences are useful but slightly tangential and could have been collapsed into one line without loss.
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 parameterless list tool with no output schema, the description covers purpose and cost but says nothing about what a returned profile contains, whether results are paginated or scoped to the whole project, or how large the list may be. An agent knows whether to call it, but not what to expect back.
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 tool takes zero parameters, so there is nothing for the description to clarify and the baseline applies. The extra sentences about credit costs are not parameter guidance but do not detract from an otherwise complete parameter story.
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 gives a specific verb and resource ('List LinkedIn profiles this project tracks') and implicitly separates itself from list_linkedin_leads by naming profiles rather than leads. It does not explicitly call out the sibling it differs from, so it stops short of full differentiation.
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 only usage signal is cost-based ('Does not spend credits'), which hints that this is the free way to inspect tracked profiles versus paying for scans or lookups. There is no explicit when-to-use or when-not-to-use statement, and no pointer to list_linkedin_leads or set_linkedin_profile as alternatives.
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.