Skip to main content
Glama
BACH-AI-Tools

LinkedIn Data API MCP Server

get_received_recommendations

Fetch received recommendations for a LinkedIn username. Supports pagination: increment start by 100 per request until the total count is reached.

Instructions

To scrape all recommendations from a profile, increase the start value to +100 for each request until you reach the total recommendations count. You can find the total recommendations count in the response

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
usernameYesExample value: ryanroslansky
startNoExample value: 0
Behavior2/5

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

No annotations exist, so description carries full burden. It reveals pagination behavior and mentions 'total recommendations count' in response, but omits details like authorization needs, rate limits, or statefulness of repeated calls. Lacks full transparency for a scraping tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One sentence covers the core instruction but is somewhat run-on. Information is front-loaded with purpose, but could be split for clarity. Acceptable but not highly concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Adequately explains pagination to retrieve all recommendations, given no output schema. However, lacks description of response structure for individual recommendations, which is needed for complete understanding. Sufficient for basic use with trial-and-error.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% coverage with examples. Description adds meaning by explaining 'start' is for pagination and increments by 100, which is not evident from schema alone. The total count reference also adds context for the response.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool scrapes 'all recommendations from a profile', indicating it retrieves received recommendations. It distinguishes implicitly from sibling 'get_given_recommendations' by not mentioning 'given', but does not explicitly state 'received'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit pagination instructions (increase start by 100 until total count), but no direct comparison with alternatives like 'get_given_recommendations' or 'profile_data_&_recommendations'. The context is clear for pagination usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/BACH-AI-Tools/bachai-linkedin-data-api'

If you have feedback or need assistance with the MCP directory API, please join our Discord server