LinkedIn MCP Server

by maykonlong

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation4/5
Most tools are clearly distinct, targeting specific profile sections or actions. However, add_experience and update_current_position could be confused, as updating the current position is a form of experience management. analyze_profile_seo and get_profile both access profile data but serve different purposes.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with the linkedin_ prefix. Verbs (update, get, add, publish, analyze, search) and nouns are clear, and snake_case is used throughout, making the API predictable.
Tool Count5/5
With 10 tools, the server is well-scoped for its purpose. It covers profile management, posting, job search, and SEO analysis without unnecessary redundancy, fitting comfortably within the ideal 3-15 range.
Completeness3/5
The tool surface covers core profile reads and updates, additions, posting, analysis, and job search. However, there are notable gaps: no update or delete operations for experiences, education, or skills, and no way to view or delete posts, limiting lifecycle management.
Average 3.4/5 across 10 of 10 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 36 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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, the description carries the full burden of behavioral disclosure. It states the mutation ('Atualiza') but does not disclose whether this overwrites an existing current position, requires any permissions, or returns anything. The behavior is ambiguous, especially regarding its relationship to linkedin_add_experience.
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 verb and resource. There is zero wasted text, and it conveys the essential purpose and parameters in a compact form.
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?
The tool is simple with only 3 string parameters, but the lack of annotations and output schema means the description must clarify whether it replaces or creates a current position. It fails to do so, leaving critical ambiguity that could lead to misuse. The description is minimal but not sufficient for safe operation.
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%, with each parameter already documented in the schema (title, company, description). The description repeats these fields but adds no new meaning beyond what the schema provides. Baseline 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 function: 'Atualiza o cargo atual' (updates the current position) and lists the fields (title, company, description). This distinguishes it from siblings like linkedin_update_about and linkedin_update_headline, but it does not explicitly contrast with linkedin_add_experience, which could also involve positions.
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. It does not mention that this updates/replaces an existing current position, nor does it suggest using linkedin_add_experience for adding a new position. The user 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 full responsibility for behavioral disclosure. It only states that a new experience is added, without mentioning permissions, side effects, reversibility, or what happens upon success/failure. This is inadequate for a mutation 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, concise sentence that effectively communicates the tool's purpose without unnecessary detail or repetition.
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 mutation tool with no annotations and no output schema, the description is too sparse. It lacks operational context such as expected outcomes, constraints, or integration details. The schema covers parameters but not behavioral context.
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 covers 100% of parameters with descriptions, so the baseline is 3. The tool description adds no additional parameter meaning beyond what the schema already 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 function: adding a new professional experience to the profile. It uses a specific verb ('Adiciona') and resource ('experiência profissional'), which distinguishes it from sibling tools like linkedin_add_education and linkedin_add_skill.
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 such as linkedin_update_current_position. It does not mention prerequisites, exclusions, or scenarios where this tool should be preferred over others.
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 full responsibility for behavioral disclosure. It only says 'adds a new skill' but does not explain side effects (e.g., whether it appends to existing skills, requires authentication, or is irreversible).
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, clear sentence with no wasted words. It is front-loaded and easy to parse, though it could be expanded with additional context without becoming wordy.
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?
For a simple one-parameter tool, the description is minimally sufficient but lacks details about the profile context, like whether this replaces or appends to the existing skill list. No output schema or annotations exist to fill gaps, but the tool's simplicity keeps this at an adequate level.
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 already covers the single parameter 'skill' with a clear description and example (100% coverage). The tool description does not add extra meaning beyond the schema, so the baseline of 3 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 action (add) and the resource (a new skill to the profile), with a specific verb and object. It distinguishes itself from sibling tools by focusing on skills specifically, not experience or education.
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. Sibling tools like linkedin_add_experience and linkedin_add_education exist, but the description does not mention them or clarify that this is the sole tool for adding skills.
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 must fully disclose behavioral traits. It only states that it adds a new education entry, without mentioning side effects, authentication requirements, overwriting behavior, or response format. This is insufficient for a mutation 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 concise sentence that clearly and directly states the action, with no redundant or filler content. It is appropriately brief.
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?
Despite having 5 parameters and being a write operation with no annotations, the description only gives a one-line summary. It lacks details on expected behavior (e.g., whether it appends or replaces education), prerequisites, return values, or edge cases, making it incomplete for the tool's complexity.
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 provides descriptions for all 5 parameters (degree, school, endYear, startYear, fieldOfStudy), so the description adds no additional parameter semantics. The baseline score of 3 applies due to 100% 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 uses the verb 'Adiciona' (adds) with a specific resource 'nova formação acadêmica' (new academic education) to the profile, which clearly states the function and distinguishes it from sibling tools like add_experience and add_skill.
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 adding education but provides no explicit guidance on when to use this tool versus alternatives like linkedin_add_experience or linkedin_update_about. No exclusions or alternative references are mentioned.
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, and the description does not disclose any behavioral traits beyond the basic mutate action. It fails to mention potential side effects, authentication requirements, rate limits, or irreversibility, leaving a significant gap for a mutation 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, compact sentence that directly states the purpose. It is front-loaded and contains no filler or 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?
Given the tool's simplicity (one parameter, no output schema), the description is largely sufficient for an agent to understand the action. However, the lack of usage guidance and behavioral transparency slightly reduces completeness, which could have been a 5 if more context were provided.
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 coverage is 100%, so the schema fully documents the 'content' parameter. The description adds no extra meaning beyond the schema, so the baseline of 3 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 uses a specific verb 'Publica' (publishes) and clearly states the resource 'post no feed do LinkedIn' (post in LinkedIn feed), distinguishing it from sibling tools that update profile sections or search jobs.
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?
There is no guidance on when to use this tool versus alternatives. The description simply states what it does without mentioning any exclusions, prerequisites, or comparisons to sibling tools.
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 must disclose behavioral traits but does not. It only states that the headline is updated, without mentioning potential side effects (e.g., overwriting current headline), authentication requirements, or reversibility. This is insufficient for a mutation tool with no annotation support.
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, concise sentence that immediately conveys the tool's purpose without extraneous information. It is appropriately sized for a simple, single-parameter tool.
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 simplicity (one parameter, no output schema, no annotations), the description is largely complete for understanding the basic operation. However, it lacks any behavioral context, such as confirmation or error handling, which would be expected for a profile-modifying action. Still sufficient for a straightforward update.
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 has 100% coverage with a parameter description 'Novo título/headline do perfil', which mirrors the tool description. The description does not add additional meaning beyond what the schema already provides, but the schema fully explains the single parameter. Baseline 3 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 uses a specific verb 'Atualiza' (updates) with a clear resource 'título/headline do perfil do LinkedIn', clearly distinguishing it from sibling tools like linkedin_update_about or linkedin_update_current_position. It precisely identifies the target field being modified.
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, such as linkedin_update_about or linkedin_update_current_position. There are no mention of prerequisites, exclusions, or scenarios where another tool would be more appropriate. The usage is only implied by the tool's name and description.
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 the output (report with score and recommendations) but does not disclose potential side effects, non-destructive behavior, authentication needs, or any other behavioral aspects. The verb 'analisa' implies a read-only operation, but this is not explicit.
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, front-loaded sentence that immediately states the action and output. It is concise and free of unnecessary details.
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?
The description covers the basic purpose and output, but lacks context about which profile is analyzed (e.g., the authenticated user's profile), any preconditions, or the format of the report. Given there is no output schema, these details would enhance 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 tool has zero parameters, so schema coverage is trivially complete. According to the baseline, 0 parameters merit a 4. The description adds no parameter details because none are needed.
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 analyzes a LinkedIn profile and generates a report with an attractiveness score (0-100) and recommendations. This is distinct from sibling tools like linkedin_update_about or linkedin_search_jobs, which focus on updates or searches.
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 explicit guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or contrast with sibling tools, leaving the user to infer usage from the purpose 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?
Sem anotações, a descrição apenas afirma que a ferramenta atualiza a seção 'Sobre', não divulgando comportamento de substituição total, permissões ou efeitos colaterais. Isso é uma lacuna para uma operação de escrita.
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?
Frase única e direta, sem repetições ou excessos. A informação essencial está na primeira linha e é perfeitamente legível.
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?
Para uma ferramenta simples com um parâmetro e sem output schema, a descrição é suficiente para a operação básica, mas não informa se o texto é substituído integralmente, se há limites de tamanho ou se há retorno esperado.
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?
O schema já cobre o único parâmetro 'about' com descrição ('Novo texto da seção Sobre'), e a descrição da ferramenta não adiciona semântica extra além disso. Com cobertura de 100%, o baseline 3 é adequado.
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?
A descrição usa o verbo 'Atualiza' com o recurso específico 'seção Sobre do perfil do LinkedIn', distinguindo claramente de ferramentas irmãs como update_headline. É direta e específica.
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?
Não há orientação explícita sobre quando usar ou alternativas; o uso é implícito pela finalidade, mas sem menção a exclusões ou contextos adicionais.
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 must carry the behavioral transparency burden. It only restates the search purpose and does not disclose the return format, pagination, authentication needs, or any side effects. The read-only nature is implied by 'pesquisa' but not explicitly confirmed.
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, concise sentence that front-loads the action and resource. No wasted words, and it is perfectly sized for the tool's simplicity.
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 simple tool (2 parameters, no output schema), the description is largely complete. It states the purpose and maps to the parameters. It could mention the return type, but that is not required for a straightforward search operation.
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 provides full descriptions for both parameters (keywords and location with default 'Brasil'). The description mentions keywords and location, but adds no new semantics beyond what the schema already documents, so a baseline 3 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 'Pesquisa vagas de emprego no LinkedIn' (searches LinkedIn job postings), with a specific verb (search), resource (jobs), and parameters (keywords and location). It distinguishes from sibling tools, which are all about profile updates and content publishing.
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?
Usage is clearly implied: this is the only search tool among siblings, so a user would know to use it for finding jobs. However, there is no explicit 'use when' statement or mention of alternatives, which would make it fully explicit.
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 must disclose behavior. The verb 'Obtém' indicates a read operation, and the listed fields suggest a non-destructive retrieval. However, it does not mention authentication requirements, rate limits, or whether the data belongs to the authenticated user or someone else.
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?
A single sentence efficiently states the tool's purpose and lists the data categories, with no filler or 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?
For a simple read tool with no parameters and no output schema, the description adequately explains what data is returned through the field list. It lacks an explicit statement that this is the authenticated user's profile, but that is a minor ambiguity given the absence of parameters.
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 tool has zero parameters and the schema has no properties, so schema coverage is trivially complete. The description correctly focuses on the output data rather than parameters, meeting the baseline for a no-parameter tool.
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 uses a specific verb ('Obtém') and resource ('perfil do LinkedIn'), and enumerates key data sections (headline, sobre, localização, experiências, formação, habilidades). This clearly distinguishes it from sibling tools that perform updates, adds, or searches.
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?
No explicit when-to-use or alternative guidance is provided. The context is implied by comparison with siblings (which are all mutation or search tools), but the description does not state 'use this for reading profile data' or mention exclusions.
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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