Clado MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation3/5
The tools have some overlap in purpose, particularly between 'enrich_linkedin' and 'scrape_linkedin', which both retrieve LinkedIn profile data, though 'scrape_linkedin' includes posts and comments. 'retrieve_contacts' focuses on contact information, while 'linkedin_post_reactions' and 'search_for_users' are more distinct. Descriptions help differentiate them, but confusion could arise between the profile-focused tools.
Naming Consistency4/5
Most tools follow a consistent verb_noun pattern (e.g., 'enrich_linkedin', 'scrape_linkedin', 'search_for_users'), which is clear and predictable. However, 'retrieve_contacts' uses 'retrieve' instead of a more aligned verb like 'get' or 'enrich', causing a minor deviation. Overall, the naming is mostly consistent and readable.
Tool Count5/5
With 5 tools, the count is well-scoped for a LinkedIn-focused server, covering key operations like profile enrichment, contact retrieval, post analysis, and user search. Each tool appears to serve a distinct purpose, and the number is neither too sparse nor overwhelming for the domain.
Completeness4/5
The tool set covers core LinkedIn data retrieval functions, including profile enrichment, contact information, post engagement, and user search. Minor gaps exist, such as the lack of tools for updating or managing data (e.g., no 'create_contact' or 'delete_profile'), but agents can likely work around these for typical lookup and analysis tasks.
Average 3.5/5 across 5 of 5 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
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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 the tool 'retrieves' data, implying a read-only operation, but doesn't disclose any behavioral traits such as rate limits, authentication requirements, data freshness, or what specific engagement metrics are included. This leaves significant gaps for a tool that likely interacts with external APIs.
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, well-structured sentence that efficiently conveys the core purpose without any redundant information. It is appropriately sized and front-loaded, 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of retrieving engagement data from LinkedIn (likely involving API calls), the lack of annotations, and no output schema, the description is insufficient. It doesn't explain what data is returned (e.g., types of reactions, metrics), potential limitations, or error handling, leaving the agent with incomplete context for effective tool 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?
The input schema has 100% description coverage, with the single parameter 'post_url' clearly documented as 'The LinkedIn post URL to analyze for reactions.' The description adds no additional semantic meaning beyond this, so it meets the baseline score 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 'retrieves' and the resource 'reactions and engagement data for a specific LinkedIn post URL', making the purpose unambiguous. However, it doesn't explicitly differentiate this tool from sibling tools like 'scrape_linkedin' or 'enrich_linkedin', which might also involve LinkedIn data retrieval, so it doesn't reach the highest 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 any prerequisites, exclusions, or compare it to sibling tools like 'scrape_linkedin' or 'enrich_linkedin', leaving the agent to infer usage context independently.
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 full burden but offers minimal behavioral insight. It mentions 'filters like query, school, and match threshold' but doesn't disclose critical traits: whether this is a read-only operation, rate limits, authentication needs, result format, or what 'match threshold' entails. For a search tool with 7 parameters, this is inadequate.
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 with the core purpose and includes specific examples of filters. Every word earns its place, making it easy to parse quickly.
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 tool's complexity (7 parameters, no annotations, no output schema), the description is insufficient. It lacks details on behavioral traits, result format, pagination behavior (implied by 'search_id' and 'offset' but not explained), and differentiation from siblings. For a search tool with multiple filters, more context is needed to guide effective 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 fully documents all 7 parameters. The description adds marginal value by highlighting 'query, school, and match threshold' as key filters, but 'match threshold' isn't in the schema, creating confusion. Baseline 3 is appropriate as the schema does heavy lifting, though the description's mention of an undefined parameter slightly detracts.
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 Clado'), with specific filters mentioned. It distinguishes from siblings by focusing on user search rather than enrichment, posting, or scraping. However, it doesn't explicitly differentiate from 'retrieve_contacts' which might overlap in retrieving user information.
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?
No guidance is provided on when to use this tool versus alternatives like 'enrich_linkedin' or 'retrieve_contacts'. The description mentions filters but doesn't specify use cases, prerequisites, or exclusions. This leaves the agent without context for tool selection among siblings.
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 valuable context beyond what the input schema provides: it discloses credit costs (4 credits for email enrichment if found, 10 credits for phone enrichment if found), which is crucial for understanding resource usage and potential expenses. This information isn't captured in the schema, making the description transparent about 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, consisting of two concise sentences that efficiently convey the tool's purpose, capabilities, and cost implications. Every sentence earns its place by providing essential information without redundancy or unnecessary details, making it easy for an AI agent to parse and understand quickly.
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 of a tool with 5 parameters, no annotations, and no output schema, the description does a good job of providing context. It explains the tool's purpose, reverse lookup support, and credit costs, which are critical for usage. However, it doesn't describe the return format or what happens when no data is found, leaving some gaps in completeness for a tool without 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 schema description coverage is 100%, meaning all parameters are well-documented in the schema itself. The description adds some semantic context by mentioning credit costs associated with 'email_enrichment' and 'phone_enrichment', but it doesn't provide additional meaning beyond what the schema already covers for parameters like 'linkedin_url', 'email', or 'phone'. Given the high schema coverage, a baseline 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 tool's purpose: retrieving email addresses and phone numbers for LinkedIn profiles, with support for reverse lookup via email or phone. It specifies the resource (LinkedIn profiles) and the action (retrieving contact information). However, it doesn't explicitly differentiate this tool from sibling tools like 'enrich_linkedin' or 'search_for_users', which might have overlapping functionality.
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 mentioning reverse lookup capabilities and credit costs, but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'enrich_linkedin' or 'search_for_users'. It mentions cost implications, which helps inform usage decisions, but lacks direct comparisons or exclusions for sibling 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 adds valuable behavioral context: it discloses the cost per request ('2 credits'), which is a rate-limiting consideration not evident from the schema. However, it does not mention authentication requirements, rate limits beyond cost, or error handling.
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 core purpose and scope, and the second adds critical cost information. Both sentences earn their place with no wasted words or redundancy.
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 (data scraping with cost implications), no annotations, and no output schema, the description is minimally adequate. It covers purpose and cost but lacks details on output structure, error cases, or prerequisites like API keys, leaving gaps for the agent to handle.
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 'linkedin_url' parameter fully. The description does not add any parameter-specific details beyond what the schema provides, such as URL format examples or validation rules, meeting the baseline for high schema 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 data and posts with comments') and resource ('from a LinkedIn profile URL'), distinguishing it from siblings like 'enrich_linkedin' or 'linkedin_post_reactions' by specifying the comprehensive data retrieval scope.
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 the data source ('LinkedIn profile URL') and API provider ('RapidAPI'), but does not explicitly state when to use this tool versus alternatives like 'enrich_linkedin' or 'search_for_users', leaving the agent to infer based on the data types mentioned.
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 beyond the input schema by stating 'Each successful lookup costs 1 credit,' which is crucial context about cost implications. However, it doesn't cover other behavioral traits like rate limits, error handling, or response format, leaving some gaps 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with two sentences: the first states the purpose, and the second adds critical behavioral context (cost). It's front-loaded with the core functionality and avoids unnecessary details, making every sentence earn its place without waste.
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 complexity (a single-parameter lookup with cost implications) and no annotations or output schema, the description is fairly complete. It covers the purpose and cost, but lacks details on output format or error cases. For a tool with no structured output documentation, it could be more comprehensive, but it provides enough context for basic usage.
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 parameter 'linkedin_url' clearly documented. The description doesn't add any additional meaning beyond what the schema provides (e.g., it doesn't specify URL format or validation rules). According to the rules, with high schema coverage, the baseline is 3, and the description doesn't compensate further, so this score is appropriate.
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 resource ('for a specific LinkedIn URL'), distinguishing it from sibling tools like 'linkedin_post_reactions' (which deals with reactions) and 'scrape_linkedin' (which might be more general). It precisely communicates 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 mentioning 'for a specific LinkedIn URL,' suggesting it's for individual profile lookups, but it doesn't explicitly state when to use this tool versus alternatives like 'search_for_users' (which might search by name) or 'retrieve_contacts' (which might handle bulk data). There's no guidance on exclusions or prerequisites, leaving usage context somewhat inferred.
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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