LinkedIn MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose targeting specific LinkedIn resources: company profiles/posts, person profiles, job details/searches, and browser management. There is no overlap in functionality, with clear boundaries between company, person, and job operations.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with snake_case (e.g., get_company_profile, search_jobs, close_browser). The naming is predictable and readable throughout the set, using appropriate verbs like 'get', 'search', and 'close'.
Tool Count5/5
With 7 tools, this server is well-scoped for LinkedIn data access, covering key entities (companies, people, jobs) and essential operations (profiles, posts, searches, details). Each tool earns its place without being overwhelming or insufficient.
Completeness4/5
The toolset provides strong coverage for reading LinkedIn data, including profiles, posts, and jobs with search capabilities. Minor gaps exist, such as no tools for creating or interacting with content (e.g., posting updates or sending messages), but core data retrieval workflows are well-supported.
Average 3.8/5 across 7 of 7 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No high-severity vulnerability alerts
- No code scanning findings
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but lacks details on behavioral traits such as rate limits, authentication requirements, pagination, data freshness, or error handling. For a tool that likely accesses external APIs (LinkedIn), this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the core purpose stated concisely in the first sentence. The 'Args' section adds necessary parameter details without redundancy. However, the structure could be slightly improved by integrating the parameter explanation more seamlessly, but overall it's efficient with minimal waste.
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's moderate complexity (accessing LinkedIn data), no annotations, and an output schema present (which handles return values), the description is minimally adequate. It covers the purpose and parameter semantics but lacks usage guidelines and behavioral transparency, leaving gaps in context that could hinder effective tool selection and invocation by an AI agent.
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?
The description adds meaningful context for the single parameter 'company_name' by specifying it as a 'LinkedIn company name' and providing examples ('google', 'stripe', 'openai'). Since schema description coverage is 0% and there's only one parameter, this effectively compensates for the schema's lack of details, making the parameter semantics clear and actionable.
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 verb 'Get' and resource 'recent posts from a company's LinkedIn feed', making the purpose specific and understandable. However, it doesn't explicitly distinguish this tool from sibling tools like 'get_company_profile' or 'search_people', which might also retrieve LinkedIn data but for different resources.
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. It doesn't mention scenarios where this tool is preferred over siblings like 'get_company_profile' (which might get company info instead of posts) or 'search_people' (which might find individuals). There's no context on prerequisites, limitations, or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool searches LinkedIn but doesn't mention limitations (e.g., rate limits, authentication needs, result format, pagination, or whether it's read-only). For a search tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose followed by parameter details. The 'Args' section is structured clearly, though it could be more integrated into the flow. There's minimal waste, with every sentence adding value.
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's moderate complexity (2 parameters, no annotations, but an output schema exists), the description is partially complete. It covers the purpose and parameters adequately, but lacks behavioral context (e.g., search scope, limitations). The presence of an output schema reduces the need to explain return values, but more operational details would improve completeness.
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?
The description adds meaningful semantics beyond the schema, which has 0% coverage. It explains that 'keywords' are search terms with examples like 'product manager', and 'location' is an optional filter with examples like 'London'. This compensates well for the schema's lack of descriptions, though it doesn't detail constraints like character limits.
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's purpose as 'Search for people on LinkedIn' with a specific verb ('Search') and resource ('people on LinkedIn'). It distinguishes from siblings like 'search_jobs' by specifying the resource type, though it doesn't explicitly contrast with 'get_person_profile' which retrieves individual profiles rather than searching.
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 'get_person_profile' (for retrieving a specific person's details) or 'search_jobs' (for job searches). It lacks context about use cases, prerequisites, or exclusions, offering only basic functional information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves details but doesn't mention aspects like rate limits, authentication needs, data freshness, or error handling. For a read operation with no annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond its basic function.
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 front-loaded with the core purpose in the first sentence, followed by a concise 'Args' section that adds necessary parameter details without redundancy. Every sentence earns its place, and there's no wasted text, making it highly efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter), no annotations, but an output schema exists, the description is reasonably complete. It explains what the tool does and the parameter meaning, and the output schema will handle return values. However, it could improve by addressing behavioral aspects like permissions or limitations, which are missing.
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?
The schema description coverage is 0%, so the description must compensate. It adds meaningful context for the single parameter 'job_id' by explaining it's a 'LinkedIn job ID' and providing examples (e.g., '3912045678'), which clarifies the format beyond the schema's basic string type. This effectively covers the parameter semantics, though it doesn't detail constraints like length or validation.
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's purpose: 'Get job details for a specific job posting on LinkedIn.' It specifies the verb ('Get') and resource ('job details'), and distinguishes it from sibling tools like 'search_jobs' by focusing on a specific job rather than searching. However, it doesn't explicitly contrast with 'get_company_profile' or 'get_person_profile' in terms of data scope, which prevents a perfect score.
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?
The description implies usage by specifying 'for a specific job posting,' suggesting it should be used when a job ID is known, unlike 'search_jobs' for broader queries. However, it lacks explicit guidance on when not to use it (e.g., vs. 'get_company_posts' for company-related content) or clear alternatives, making it only moderately helpful.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool scrapes data (implied read-only behavior) and includes default behavior (about page always included), but lacks details on rate limits, authentication needs, or error handling. It adds some context but not comprehensive behavioral traits.
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 appropriately sized and front-loaded: the first sentence states the purpose, followed by a structured 'Args:' section with clear bullet points. Every sentence adds value without redundancy, making it efficient and well-organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, no annotations, and an output schema (which handles return values), the description is mostly complete. It covers purpose, parameters, and basic behavior, but could improve by addressing sibling tool differentiation or more behavioral context like scraping limitations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the input schema, which has 0% coverage. It explains 'company_name' as 'LinkedIn company name' with examples, and details 'sections' with available options, default behavior, and formatting instructions. This fully compensates for the schema's lack of descriptions.
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 verb 'Get' and resource 'company's LinkedIn profile', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_company_posts' or 'get_person_profile', 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying what data is included (about page always, optional sections), but doesn't provide explicit guidance on when to use this tool versus alternatives like 'get_company_posts' or 'search_jobs'. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It mentions scraping behavior and that the main profile is always included, which adds useful context. However, it doesn't disclose rate limits, authentication needs, or potential side effects like data freshness or scraping constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear purpose statement followed by detailed parameter explanations. It's appropriately sized, though the sections explanation could be slightly more concise. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 2 parameters with 0% schema coverage and no annotations, the description does an excellent job explaining inputs. With an output schema present, it doesn't need to detail return values. However, for a scraping tool, it could better address behavioral aspects like rate limits or data reliability.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description fully compensates by explaining both parameters in detail. It defines linkedin_username with examples and sections with available options, default behavior, and format, adding significant meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get') and resource ('specific person's LinkedIn profile'), distinguishing it from sibling tools like get_company_profile or search_people. It precisely defines what the tool does without being vague or tautological.
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?
The description implies usage by specifying it's for getting a 'specific person's' profile, but doesn't explicitly state when to use this versus alternatives like search_people. No guidance on prerequisites, exclusions, or comparative contexts with siblings is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return format (job_ids) and mentions pagination behavior ('Maximum number of result pages to load'), which is valuable context. However, it doesn't disclose important behavioral aspects like rate limits, authentication requirements, or whether this is a read-only operation, leaving significant gaps for a tool with 9 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear purpose statement, usage guidance, and organized parameter documentation. While appropriately sized for a tool with 9 parameters, the parameter explanations could be slightly more concise, but overall the structure is logical and front-loaded with the most important information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (9 parameters, no annotations, but with output schema), the description is mostly complete. It explains the purpose, usage workflow, and all parameters thoroughly. The existence of an output schema means the description doesn't need to explain return values, but it could better address behavioral aspects like authentication or rate limits for a LinkedIn search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by providing comprehensive parameter documentation. Each of the 9 parameters is clearly explained with examples and valid values, including defaults for max_pages and easy_apply, and format guidance for comma-separated filters. This adds substantial meaning beyond what the bare schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verb ('Search for jobs') and resource ('on LinkedIn'), and distinguishes it from sibling tools by mentioning that it returns job_ids for use with get_job_details. This explicitly differentiates it from search_people and other sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool versus alternatives by stating 'Returns job_ids that can be passed to get_job_details for full info,' which clearly indicates the relationship between search_jobs and get_job_details. This gives the agent clear direction on the workflow between these tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it destroys the browser instance, releases resources, and preserves credentials. It doesn't mention error conditions or side effects, but covers the essential mutation behavior adequately.
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?
Two concise sentences with zero waste. The first sentence states the core action and consequence, the second adds crucial credential preservation detail. Every word earns its place in this well-structured description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simple nature (no parameters, has output schema), the description provides complete context for understanding what the tool does and its behavioral implications. It covers the mutation aspect, resource management, and credential handling without needing to explain return values.
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?
With 0 parameters and 100% schema description coverage, the baseline is 4. The description appropriately doesn't discuss parameters since none exist, focusing instead on the tool's behavioral impact.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Close the browser instance') and the resource affected ('browser instance'), distinguishing it from all sibling tools which are data retrieval operations. It adds meaningful context about resource release, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning resource release and credential preservation, suggesting this should be used when cleaning up browser sessions. However, it doesn't explicitly state when to use this tool versus alternatives or provide exclusion criteria, leaving some guidance gaps.
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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