Linkd MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but 'enrich_linkedin' and 'scrape_linkedin' could be confused as both retrieve detailed profile information. The descriptions clarify that 'scrape_linkedin' includes posts and comments, while 'enrich_linkedin' is more basic, but the overlap in core functionality might cause misselection.
Naming Consistency4/5
Tool names follow a consistent snake_case pattern with clear verb_noun structures, such as 'check_deep_research_status' and 'search_for_users'. Minor deviations exist, like 'enrich_linkedin' using a verb without an explicit object, but overall the naming is predictable and readable.
Tool Count5/5
With 7 tools, the count is well-scoped for a LinkedIn-focused server, covering key operations like profile retrieval, research, and searches. Each tool appears to earn its place without being excessive or insufficient for the domain.
Completeness4/5
The toolset provides good coverage for LinkedIn operations, including profile enrichment, contact retrieval, and searches for users and companies. Minor gaps exist, such as no explicit update or delete tools, but these are less critical in a data retrieval context, and agents can likely work around them.
Average 3.4/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under ISC License.
This repository includes a README.md file.
Tools from this server were used 8 times in the last 30 days.
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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 states this checks status but doesn't explain what statuses exist (e.g., pending, running, completed), whether it's idempotent, if there are rate limits, or what the response format looks like. This leaves significant gaps for a tool that likely returns progress or result information.
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 core purpose without any wasted words. It's appropriately sized for a simple status-checking tool and earns its place by clearly stating the action and target.
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 checking job status (which often involves progress indicators, error states, or result readiness) and the lack of both annotations and an output schema, the description is incomplete. It doesn't explain what information is returned, leaving the agent uncertain about how to interpret the status or what to do next after checking.
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 schema description coverage is 100%, with the single parameter 'job_id' clearly documented in the schema. The description doesn't add any meaning beyond this (e.g., where to get the job_id from or format examples), so it meets the baseline of 3 where the schema does the heavy lifting.
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 ('Check') and resource ('status of an ongoing deep research job'), making the purpose immediately understandable. However, it doesn't differentiate this tool from potential siblings like 'initiate_deep_research' or 'retrieve_contacts' beyond the 'check status' action, 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 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., that a job must be initiated first), exclusions, or relationships with sibling tools like 'initiate_deep_research' for starting jobs or 'retrieve_contacts' for getting results.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'filters like query and match threshold' which hints at search functionality, but doesn't describe what the search returns, pagination behavior, rate limits, authentication requirements, or error conditions. For a search tool with zero annotation coverage, this leaves significant behavioral gaps.
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 states the core purpose upfront. Every word serves a purpose - 'Search for companies on Linkd' establishes the action and target, while 'using filters like query and match threshold' adds useful operational context without redundancy.
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?
For a search tool with 3 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what kind of results are returned (company profiles, basic info, etc.), how results are ordered, whether there's pagination, or what happens when no matches are found. The agent would need to guess about the tool's behavior and outputs.
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 mentions 'filters like query and match threshold' which maps to two of the three parameters (query and acceptance_threshold). However, with 100% schema description coverage, the schema already fully documents all parameters including 'limit'. The description adds minimal value beyond what's in the schema, meeting the baseline for high coverage.
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 'Search' and resource 'companies on Linkd', making the purpose immediately understandable. It also mentions 'filters like query and match threshold' which provides additional context about how the search works. However, it doesn't explicitly differentiate this from sibling tools like 'search_for_users' or 'scrape_linkedin', 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 'search_for_users' or 'scrape_linkedin'. It mentions filters but doesn't specify use cases, prerequisites, or exclusions. Without any contextual direction, the agent must infer usage from the tool name alone.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'Search for users' but doesn't clarify if this is a read-only operation, requires authentication, has rate limits, or describes the return format. The lack of such details is a significant gap for a search tool.
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 directly states the tool's purpose and key filters. It's front-loaded with essential information and contains no unnecessary words, 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 no annotations and no output schema, the description is incomplete. It doesn't explain behavioral traits, return values, or usage context, which are crucial for a search tool with multiple parameters. The high schema coverage doesn't compensate for these missing elements.
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 fully documents all parameters. The description adds minimal value by listing filters like query, school, and match threshold, but doesn't provide additional syntax or usage context beyond the schema. This meets the baseline for high schema coverage.
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 users') and resource ('on Linkd'), specifying the tool's purpose. It mentions filters like query, school, and match threshold, which adds detail. However, it doesn't explicitly differentiate from sibling tools like 'search_for_companies' or 'retrieve_contacts', keeping it from 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 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 for usage, prerequisites, or exclusions, nor does it reference sibling tools. This leaves the agent without context for tool selection.
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 adds some context: it mentions that 'Each result costs 1 credit' (cost implication) and that it initiates a 'job' (asynchronous operation). However, it doesn't describe what happens after initiation (e.g., how to check status, expected response format, or error handling), which is critical for a tool named 'initiate' with siblings like 'check_deep_research_status'.
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 extremely concise and front-loaded: two sentences that directly state the tool's function and cost implication. Every word earns its place with zero waste or redundancy, making it easy for an agent to parse quickly.
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 complexity (initiating a research job with cost implications), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the asynchronous nature (despite a sibling tool for checking status), what 'deep research' entails operationally, or what the agent should expect after invocation. The cost warning is helpful but insufficient 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?
Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain how 'query' relates to 'search variations' or what 'enrich_emails' entails). Baseline 3 is appropriate when the schema does the heavy lifting.
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: 'Initiate a deep research job that combines multiple search variations with optional email enrichment.' It specifies the verb ('initiate'), resource ('deep research job'), and key features (search variations, email enrichment). However, it doesn't explicitly differentiate from sibling tools like 'search_for_companies' or 'search_for_users' beyond mentioning 'deep research'.
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 mentions 'deep research' but doesn't explain how this differs from simpler search tools in the sibling list. There's no mention of prerequisites, use cases, or exclusions, leaving the agent to guess when this tool is appropriate.
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 the full burden of behavioral disclosure. It effectively adds value by specifying the data source (LinkedIn via RapidAPI) and cost implications ('Each request costs 2 credits'), which are critical behavioral traits not covered by the input schema. It doesn't describe output format or error handling, but for a tool with no annotations, this is a strong effort.
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 highly concise and well-structured in a single sentence that front-loads the core functionality ('Retrieves detailed profile data and posts with comments') and efficiently adds operational context ('using RapidAPI') and cost ('Each request costs 2 credits'). Every part earns its place without redundancy or 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 complexity (scraping with external API), no annotations, and no output schema, the description is moderately complete. It covers purpose, source, and cost, but lacks details on output format, error cases, rate limits, or authentication needs. For a tool with these gaps, it's adequate but has clear room for improvement.
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 single parameter 'linkedin_url' fully documented in the schema. The description doesn't add any parameter-specific details beyond what the schema provides, such as URL format examples or validation rules. According to the rules, with high schema coverage, the baseline is 3, which is appropriate here.
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: 'Retrieves detailed profile data and posts with comments from a LinkedIn profile URL.' It specifies the verb (retrieves), resource (profile data and posts with comments), and source (LinkedIn profile URL). However, it doesn't explicitly differentiate from sibling tools like 'enrich_linkedin' or 'search_for_users,' 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 context by mentioning 'using RapidAPI' and cost ('Each request costs 2 credits'), which suggests when to consider this tool for scraping. However, it lacks explicit guidance on when to use this versus alternatives like 'enrich_linkedin' or 'search_for_users,' and doesn't state prerequisites or exclusions, leaving usage somewhat ambiguous.
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 the full burden. It discloses key behavioral traits: the operation is a retrieval (not creation or update), it involves a cost ('1 credit per successful lookup'), and it requires a specific URL. However, it lacks details on error handling, rate limits, or authentication needs.
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 and adds critical cost information in the second, with no wasted words. Every sentence earns its place by providing essential information efficiently.
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, no output schema, and a simple input schema, the description is adequate but has gaps. It covers purpose and cost but lacks details on return values, error cases, or performance characteristics, which would help an agent use it more 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 'url' parameter fully. The description adds no additional meaning or context beyond what the schema provides, such as URL format examples or validation rules, meeting the baseline for high coverage.
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 ('Retrieves detailed profile information') and target resource ('for a specific LinkedIn URL'), distinguishing it from sibling tools like 'scrape_linkedin' or 'search_for_users' which imply different operations or scopes.
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 when detailed LinkedIn profile data is needed from a specific URL, but it does not explicitly state when to use this tool versus alternatives like 'scrape_linkedin' or 'search_for_users', nor does it mention prerequisites or exclusions.
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 the full burden of behavioral disclosure. It effectively communicates key operational constraints: the tool performs data retrieval (not creation/modification), has a cost implication (1 credit per lookup), and targets LinkedIn profiles. However, it doesn't mention rate limits, authentication requirements, data freshness, or what happens with invalid URLs.
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 perfectly concise with two sentences that each earn their place: the first states the core functionality, the second adds crucial cost information. It's front-loaded with the primary purpose and wastes no words on redundant 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?
For a single-parameter retrieval tool with no annotations and no output schema, the description provides good coverage of what the tool does and its cost implications. However, it doesn't describe the return format (structure of email/phone data) or potential error conditions, which would be helpful given the absence of an output schema.
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 single parameter 'linkedin_url' clearly documented. The description doesn't add any parameter-specific information beyond what the schema already provides (it doesn't elaborate on URL format requirements or validation rules). The baseline score of 3 is appropriate when 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 ('retrieves') and resources ('email addresses and phone numbers') with the target ('LinkedIn profile'). It distinguishes itself from sibling tools like 'enrich_linkedin' or 'scrape_linkedin' by focusing specifically on contact information retrieval rather than broader enrichment or scraping operations.
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 about when to use this tool ('for a LinkedIn profile') and mentions the cost implication ('Each lookup costs 1 credit'), which helps guide usage decisions. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools for different scenarios.
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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