LinkedIn MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose targeting different LinkedIn resources: connections, job details, messages, profiles, job search, and connection requests. There is no overlap in functionality, making it easy for an agent to select the right tool.
Naming Consistency5/5
All tools follow a consistent verb_noun naming pattern (e.g., get_connections, search_jobs, send_connection_request). The verbs are appropriate and descriptive, with no deviations in style or convention.
Tool Count5/5
With 6 tools, the server is well-scoped for LinkedIn interactions, covering key areas like profiles, connections, jobs, and messaging. Each tool earns its place without being overwhelming or insufficient for the domain.
Completeness4/5
The tool set covers core LinkedIn functionalities well, including reading (profiles, connections, jobs, messages) and writing (connection requests). Minor gaps exist, such as no tools for updating profiles, managing connections (e.g., removing), or interacting with posts, but agents can still handle common workflows effectively.
Average 3.3/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions filtering and listing, but lacks details on permissions required, rate limits, pagination behavior, or error handling. For a tool that accesses personal data (LinkedIn connections), 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 concise and front-loaded, stating the core purpose in the first sentence. The second sentence adds optional functionality without redundancy. However, it could be slightly more structured by explicitly separating core and optional features, but overall it's efficient with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the return values look like (e.g., format of the list), error conditions, or authentication needs. For a tool that lists personal connections, this leaves significant gaps in understanding how to interpret results or handle failures.
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?
The description adds minimal value beyond the input schema, which has 100% coverage. It mentions 'Optionally filter by name,' aligning with the 'search' parameter, but doesn't provide additional context like syntax examples or edge cases. With high schema coverage, the baseline score of 3 is appropriate, as the schema does most of the work.
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: 'List your LinkedIn connections with their name, headline, and profile URL.' It specifies the verb ('List'), resource ('LinkedIn connections'), and output fields. However, it doesn't explicitly distinguish this tool from its siblings (like 'get_profile' or 'search_jobs'), which would be needed for a score of 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 minimal usage guidance with 'Optionally filter by name,' but it doesn't specify when to use this tool versus alternatives (e.g., 'get_profile' for individual profiles or 'search_jobs' for job-related queries). There's no explicit context, exclusions, or mention of prerequisites, leaving the agent with little direction on tool selection.
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 mentions the tool retrieves 'recent' messages but doesn't define what 'recent' means (e.g., last 30 days, last 100 conversations). It also omits details like pagination, rate limits, authentication requirements, or whether it's read-only (implied but not stated). This leaves significant gaps for an agent to understand operational 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 a single, efficient sentence that front-loads the core purpose. It avoids unnecessary words and directly states what the tool does. However, it could be slightly more structured by explicitly separating scope from returned data, but it's still highly concise and effective.
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 low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and data returned but lacks context on behavior (e.g., 'recent' definition, authentication). Without annotations or output schema, the description should do more to explain operational aspects, but it's not completely inadequate for a simple read tool.
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?
The input schema has 100% description coverage, with the 'limit' parameter fully documented in the schema (type, range, default). The description adds no additional parameter semantics beyond implying it fetches 'recent' conversations, which doesn't directly map to the schema parameters. Baseline 3 is appropriate as the schema does the heavy lifting, but the description doesn't compensate or add value.
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 ('Get') and resource ('recent LinkedIn messages and InMail conversations'), including specific data fields returned (sender, preview, timestamp). It distinguishes this tool from siblings like get_connections or get_profile by focusing on messages rather than connections, jobs, or profiles. However, it doesn't explicitly contrast with potential sibling messaging tools (none listed), so it's not a perfect 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. It doesn't mention prerequisites (e.g., authentication), context for 'recent' (timeframe), or how it differs from other tools that might access messages. With siblings like get_connections and send_connection_request, there's no explicit comparison to help an agent choose appropriately.
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. It mentions the action is to 'send a connection request' but lacks critical behavioral details: it does not specify if this is a one-time action, potential rate limits, success/failure conditions, or what happens if a request already exists. The description is minimal and misses key operational context.
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 is front-loaded with the core action and includes only essential details (optional note with limit). Every part earns its place, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a write operation (sending a connection request) with no annotations and no output schema, the description is incomplete. It lacks information on behavioral traits (e.g., idempotency, error handling), expected outcomes, or integration context. For a mutation tool, this minimal description does not provide sufficient context for reliable agent use.
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 100%, so the schema already documents both parameters (profile_url and note) fully. The description adds marginal value by reiterating the note's optionality and character limit, but does not provide additional semantics beyond what the schema specifies. Baseline score of 3 is appropriate as the schema does the heavy lifting.
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 ('send a LinkedIn connection request') and the resource ('to a user'), distinguishing it from sibling tools like get_connections or get_profile. It explicitly mentions the optional personalized note with character limit, making the purpose unambiguous and distinct.
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 does not mention prerequisites (e.g., authentication, LinkedIn account status), nor does it differentiate from similar actions like messaging or other connection-related tools. Usage context is implied but not explicitly stated.
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. It describes what information is retrieved but does not disclose behavioral traits such as authentication needs, rate limits, error handling, or whether the data is cached. This is a significant gap for a tool with no annotation coverage.
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 that front-loads the purpose and lists key details without waste. Every part earns its place by specifying the resource and content retrieved.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It does not explain return values, error conditions, or behavioral constraints, which are crucial for a tool that interacts with external data. This leaves gaps in understanding how to use the tool 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 100%, so the schema already documents the 'job_url' parameter. The description adds minimal value beyond the schema by implying the URL is for a LinkedIn job posting, but does not provide additional syntax or format details. Baseline 3 is appropriate when schema does the heavy lifting.
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 verb 'Get' and the resource 'full details of a specific LinkedIn job posting', listing specific content like description, requirements, salary range, and apply link. It distinguishes from siblings like 'search_jobs' (which likely returns multiple results) and 'get_profile' (which targets user profiles).
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 for retrieving details of a specific job posting, but does not explicitly state when to use this tool versus alternatives like 'search_jobs' (for finding jobs) or 'get_profile' (for user data). It provides some context but lacks explicit exclusions or comparisons.
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 mentions the return format ('list of matching jobs with title, company, location, and URL'), which adds some context. However, it lacks details on permissions, rate limits, pagination, or error handling, which are important for a search tool with no annotation coverage.
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 two sentences, front-loaded with the core purpose and followed by return details. Every sentence earns its place by adding value without redundancy, making it efficient and well-structured.
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 no annotations and no output schema, the description provides basic purpose and return format, which is adequate for a simple search tool. However, it lacks details on behavioral aspects like rate limits or error handling, leaving gaps in completeness for a tool with 4 parameters and no structured safety hints.
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 100%, meaning the input schema already documents all parameters thoroughly. The description adds marginal value by listing the searchable fields ('title, location, and/or company') and mentioning the return structure, but it does not provide additional syntax or format details beyond what the 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 specific action ('Search LinkedIn job listings'), resources involved ('job listings'), and scope ('by title, location, and/or company'). It distinguishes from siblings like 'get_job_details' (which presumably retrieves details for a specific job) and 'get_connections' (which deals with connections rather than job listings).
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 through the phrase 'by title, location, and/or company,' suggesting when to use it (for searching jobs with these criteria). However, it does not explicitly state when to use this tool versus alternatives like 'get_job_details' or provide exclusions (e.g., when not to use it for non-job-related searches).
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 mentions the default behavior for the logged-in user, which is useful context, but does not cover other traits like authentication needs, rate limits, error handling, or data freshness. This leaves gaps in understanding the tool's operational behavior.
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 and includes only essential details (content enumeration and default behavior) in two efficient sentences. There is no redundant or verbose language, making it highly concise and well-structured.
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 (fetching profile data with one optional parameter), no annotations, and no output schema, the description is incomplete. It lacks details on return format (e.g., structure of experience/education data), error scenarios, or prerequisites like login status, which are necessary for full contextual understanding.
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?
The input schema has 100% description coverage, so the schema already documents the single parameter 'profile_url' with its purpose and optional nature. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or edge cases, resulting in the baseline score of 3.
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 ('LinkedIn user profile summary') with detailed content enumeration ('name, headline, location, about section, experience, and education'). It distinguishes from siblings like 'get_connections' or 'get_job_details' by focusing on comprehensive profile data rather than connections, job details, or other LinkedIn entities.
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 provides clear context for when to use this tool: to fetch profile summaries, with a default behavior for the logged-in user. However, it does not explicitly state when not to use it or name alternatives (e.g., using 'get_connections' for network data instead of profile details), which prevents a perfect score.
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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