LinkedIn Profile Optimizer
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| prompts | {
"listChanged": true
} |
| resources | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_guidelinesA | Return the section-by-section LinkedIn profile guidelines (recruiter-search based). Omit 'section' for the full guide. |
| lint_fieldA | Check one LinkedIn field against its character limit and for AI-sounding text (em dashes, stock phrases, 'it's not X, it's Y'), list markers and emoji. Shows what is visible before truncation. |
| review_headlineB | Score a headline: role in the first 60 characters, length used, vague labels, segments, 'open to' line, and target keywords. |
| review_experienceB | Check one Experience description: length, opening line, bullet count, numbers in bullets, action verbs, duty phrasing, keywords and AI-sounding text. |
| keyword_coverageB | Find terms that recur across the pasted job descriptions and report which ones the profile text contains. Only add a missing term if it truly describes the person's work. |
| review_profileA | Run every check on a pasted profile (for example the text of LinkedIn's 'Save to PDF'). Returns per-section results and a checklist. Never logs in to or scrapes LinkedIn. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| optimize_section | Rewrite a LinkedIn section for a target role using the guidelines, then verify it with the lint tools. |
| full_profile_review | Review a pasted profile like a technical recruiter: what I see first, the one fix that matters most, and why, then section by section. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| guide | |
| limits |
TDQS
Scored across 6 tools
Each tool targets a recognizable scope: guidelines retrieval, single-field linting, headline review, experience review, keyword extraction, and full-profile review. The main overlap is that review_profile aggregates the other review tools and lint_field duplicates some character-limit/AI-tone checks already inside review_headline and review_experience, but the descriptions make the aggregate-vs-component relationship clear enough to select correctly.
Five of six names follow a verb_noun pattern (get_guidelines, lint_field, review_headline, review_experience, review_profile), and the shared 'review_' prefix groups the scoring tools predictably. keyword_coverage breaks the pattern as a noun phrase, a minor deviation that is still self-explanatory.
Six tools is a tight, well-scoped set for a profile-review assistant: one reference tool, three field-level checks, one keyword analysis, and one orchestrator. Every tool has a distinct job and none feels like filler.
The surface covers guidelines, headline, experience, keywords, and a full-profile pass, but the guidelines are section-by-section while only the Experience section gets a dedicated reviewer — About/Summary, Skills, and Education have no targeted check. lint_field partially compensates for character limits, but agents reviewing a full profile will hit a dead end on the About section, which is one of the most important LinkedIn fields.