Skip to main content
Glama
egig

linkedin-mcp

by egig
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Local

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one handles authentication, the other handles posting. There is no overlap or ambiguity between them.

    Naming Consistency3/5

    Naming conventions are inconsistent: 'create_image_post' follows a verb_noun pattern while 'linkedin_login' follows a noun_noun pattern. However, both names are clear and understandable.

    Tool Count3/5

    With only 2 tools, the server feels thin for a social media platform, but it may be intentionally scoped to image posting only. The count is borderline acceptable.

    Completeness3/5

    The server covers the basic workflow of authentication and posting an image, but lacks operations like updating, deleting, or retrieving posts. Notable gaps exist for a complete CRUD surface.

  • Average 4.6/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 1 commit 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

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    The description discloses key behaviors: it opens a browser for OAuth, and tokens persist across sessions. It also explains the effect of the `force` parameter. However, it does not mention potential errors (e.g., user cancellation) or that the token is likely stored for future use, and there is no output schema. Despite no annotations, the description carries the burden well.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Two sentences: first states the core function, second gives key usage guidance. No filler, every word adds value. Appropriate length for a simple authentication 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 optional param, no output schema), the description covers the purpose, usage context, and parameter behavior adequately. It lacks details on failure modes or token scope, but is sufficient for an agent to select and invoke the tool correctly.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema already describes the `force` parameter fully (boolean, description). The description adds context by tying `force` to re-authentication when token expires, reinforcing the parameter's purpose. With 100% schema coverage, the description adds meaningful extra context.

    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 'Authenticate' and resource 'LinkedIn via OAuth', and mentions opening a browser for user login and permission grant. It is distinct from the only sibling 'create_image_post', which is a content creation tool.

    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 on when to use: for authentication, emphasizing that it's only needed once due to token persistence. It explains when to use the `force` parameter. However, it does not explicitly state when not to use or mention alternatives, though the sibling tool is unrelated.

    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?

    Discloses that it posts to personal feed and requires prior login. Could be improved by mentioning posting side effects (e.g., immediate publication) or failure scenarios, but adequate given no annotations.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Two sentences, no redundancy. Front-loaded with action and target. Every word is purposeful.

    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?

    Covers prerequisites and parameter constraints, but does not describe return values (e.g., post ID, success indicator). Given low complexity and no output schema, this is a minor gap.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Adds value beyond schema by specifying mutual exclusivity of 'imagePath' and 'imageUrl', which schema does not enforce. Schema coverage is 100%, so description augments rather than repeats.

    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?

    Clearly states verb ('publish'), resource ('image with text'), and target ('logged-in user's personal LinkedIn feed'). Distinguishes from sibling 'linkedin_login' by making it a prerequisite.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Explicitly states prerequisite ('Requires linkedin_login to have been run first') and parameter constraint ('Provide exactly one of imagePath or imageUrl'). Provides clear guidance for correct invocation.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

linkedin-mcp MCP server

Copy to your README.md:

Score Badge

linkedin-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/egig/linkedin-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server