Skip to main content
Glama
SARAMALI15792

LinkedIn Custom MCP Server

Search Companies

linkedin_search_companies

Find companies on LinkedIn using keywords to identify business opportunities, research competitors, or discover potential partners.

Instructions

Search for companies on LinkedIn by keywords.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Annotations only provide a title, so the description carries the burden of behavioral disclosure. It states the search functionality but doesn't add context about rate limits, authentication needs, result format, or pagination. This is minimal but not contradictory, scoring a baseline 3 for adding some value beyond the sparse annotations.

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

Conciseness5/5

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

The description is a single, efficient sentence with zero waste. It's front-loaded and appropriately sized for the tool's complexity, earning a top score for conciseness.

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?

Given the tool has an output schema (which handles return values) and low complexity, the description is somewhat complete but lacks usage context and behavioral details. It's adequate as a basic search tool description but misses opportunities to guide the agent effectively.

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

Parameters3/5

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. It mentions 'by keywords,' which aligns with the single parameter 'keywords,' adding semantic meaning. However, it doesn't detail syntax, format, or examples, leaving gaps. With one parameter and partial compensation, a score of 3 is appropriate.

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 action ('Search for companies') and the resource ('on LinkedIn'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'linkedin_search_people' or 'linkedin_search_jobs', which would require a 5.

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

Usage Guidelines2/5

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 'linkedin_get_company_profile' or other search tools. It lacks context about use cases, prerequisites, or exclusions, offering only a basic functional statement.

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/SARAMALI15792/Linkedin_mcp_custom_server'

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