# ATSPrep

> Free resume tools: ATS score, job match, bullet and LinkedIn checks, tailoring, interview prep.

- name: io.github.Devabubakar/atsprep
- version: 2.0.0
- connection class: unknown (unknown)
- trust: 68/100
- quality: 89/100 (new)
- owner: not claimed
- descriptor: https://api.protogrid.dev/v1/servers/io.github.Devabubakar%2Fatsprep
- tools: https://api.protogrid.dev/v1/servers/io.github.Devabubakar%2Fatsprep/tools

**Not classified yet.** The remote endpoints have not been probed successfully, or the listing has nothing to connect to.

## Connection (mcpServers)

```json
{
  "mcpServers": {
    "atsprep": {
      "type": "http",
      "url": "https://atsprep.com/mcp"
    }
  }
}
```


## Trust

- hygiene: 100
- liveness: 4
- freshness: 100
- provenance: 90
- drivers: +repository +namespace-matches-repo +website -unreachable(2 failures) +updated-2d-ago
- Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed.

## Quality 89/100 (new)

### protocol: 75 (weight 25)

- warn `protocol.modern`: newest supported version is 2025-11-25; 2026-07-28 not supported, older versions are deprecated until 2027-07-28
- n/a `protocol.stateless`: only applies to 2026-07-28 servers
- pass `protocol.transport`: streamable HTTP
- n/a `protocol.list_ttl`: only applies to 2026-07-28 servers

### authorization: n/a (weight 15)

- n/a `auth.prm`, `auth.as_metadata`, `auth.cimd`: no remote uses OAuth
- n/a `auth.secret_in_url`: no templated URL

### tool hygiene: 100 (weight 30)

- pass `tools.descriptions`: every tool has a description
- pass `tools.description_length`: descriptions are concise
- pass `tools.schemas`: every tool has an object input schema
- pass `tools.annotations`: 18 of 18 tools declare readOnlyHint or destructiveHint
- pass `tools.directory_hints`: every tool declares readOnlyHint, destructiveHint, idempotentHint and openWorldHint
- pass `tools.token_cost`: about 4,663 tokens to load every tool
- pass `tools.api_dump`: tools are not a one-to-one API dump

### stability: n/a (weight 15)

- n/a `stability.changes`, `stability.rug_pull`: tool history recorded for 1 of the 7 days needed

### dependencies: n/a (weight 15)

- n/a `deps.known_vulns`, `deps.mcp_sdk_version`, `deps.resolvable`: no npm or PyPI package

- tools: 18, about 4,663 tokens to load them all
- tool set hash: 9f748450d5393d12ddd829322b67e8ca
- tools last changed: 2026-10-18T23:24:46.274Z
- badge: [![protogrid quality](https://protogrid.dev/badge/io.github.Devabubakar/atsprep.svg)](https://protogrid.dev/servers/io.github.Devabubakar/atsprep)
- Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed.

### Tool changes

- 2026-10-18 `review_interview_answer` added
  - Review the user's answer to an interview question: a rating, length and speaking time, STAR coverage for stories, filler words, specific checks, the follow-up an interviewer would likely ask, and a stronger version that keeps to the user's own facts. Part of the answer is drafted by an AI model (Gemma 4, with Llama 3.3 70B as a backup, on Cloudflare Workers AI) behind ATSPrep's guard, which drops any figure, name or tool the user's text doesn't contain. If the AI part is unavailable, the rule-based part still comes back and ai.message says why.
- 2026-10-18 `interview_questions` added
  - Interview questions to practise, each with what the interviewer is listening for, plus STAR answer tips. With a job_description, questions come from its skills and duties, along with what to research and questions to ask; with resume_text, from the user's own roles. Rule-based.
- 2026-10-18 `draft_resignation_letter` added
  - Draft a short, professional resignation letter and work out the last working day from the notice period. Missing facts show as [square-bracket] gaps. Returns the letter, the last day and a checklist (notice stated, no complaints, length).
- 2026-10-18 `draft_cover_letter` added
  - Draft a cover letter from the user's resume and the job posting with fixed rules: name, contact details and up to two results from the resume, the job title and top keywords from the posting. Facts that are missing show as [square-bracket] gaps for the user to fill, never as stand-ins. Returns the letter, the gaps and a checklist.
- 2026-10-18 `check_linkedin_about` added
  - Check a LinkedIn About section: what shows before "see more" on a phone and on a computer, whether the opening says who they are, results with numbers, buzzwords, a clear next step, and the 2,600-character limit.
- 2026-10-18 `check_linkedin_headline` added
  - Check a LinkedIn headline: length against the 220-character limit, what shows in search results before the cut, whether it leads with a searchable job title, and buzzwords or vague claims to replace.
- 2026-10-18 `generate_linkedin_headline` added
  - Write LinkedIn headline options from the user's answers, each in a proven pattern (title and focus, title and result, title and who you help…), with its length and roughly what shows in search before the cut. Uses only the answers given.
- 2026-10-18 `action_verbs` added
  - Suggest resume action verbs. Pass a weak opener ("responsible for", "helped") for direct swaps and why they read weak, or the work in plain words ("trained new staff", "cut costs") for the matching verb groups with an example bullet. Leave query empty for every group.
- 2026-10-18 `write_summary` added
  - Draft professional summaries from a resume: rule-based drafts built from its title, skills and the bullets that carry a figure, plus AI drafts that use only facts in the resume. Part of the answer is drafted by an AI model (Gemma 4, with Llama 3.3 70B as a backup, on Cloudflare Workers AI) behind ATSPrep's guard, which drops any figure, name or tool the user's text doesn't contain. If the AI part is unavailable, the rule-based part still comes back and ai.message says why.
- 2026-10-18 `improve_bullet` added
  - Check one resume bullet with the fixed rules (score, verdict, fixes) and suggest up to three stronger rewrites that keep every fact as given. Where a number would help, the rewrite leaves a slot such as [X%] for the user to fill. Part of the answer is drafted by an AI model (Gemma 4, with Llama 3.3 70B as a backup, on Cloudflare Workers AI) behind ATSPrep's guard, which drops any figure, name or tool the user's text doesn't contain. If the AI part is unavailable, the rule-based part still comes back and ai.message says why.

Full history: https://api.protogrid.dev/v1/servers/io.github.Devabubakar%2Fatsprep/changes

## Remotes

- https://atsprep.com/mcp (streamable-http, auth none, reachable false, uptime30d 0.6)

## Packages

_none_

## Tools (18)

- `action_verbs`: Suggest resume action verbs. Pass a weak opener ("responsible for", "helped") for direct swaps and why they read weak, or the work in plain words ("trained new staff", "cut costs") for the matching verb groups with an example bullet. Leave query empty for every group.
- `check_bullets`: Give each resume bullet a score, a verdict and specific fixes with stable codes: weak openers, missing scale or result, numbers that show neither, hard-to-believe claims, first person, vague claims. The same rules and severities as check_resume. Up to 30 bullets of up to 600 characters each; blank items are skipped and reported by index. Rules version 2026-10-19.
- `check_linkedin_about`: Check a LinkedIn About section: what shows before "see more" on a phone and on a computer, whether the opening says who they are, results with numbers, buzzwords, a clear next step, and the 2,600-character limit.
- `check_linkedin_headline`: Check a LinkedIn headline: length against the 220-character limit, what shows in search results before the cut, whether it leads with a searchable job title, and buzzwords or vague claims to replace.
- `check_resume`: Score a resume from 0 to 100 with ATSPrep's fixed rules and list each issue with a stable code, where it is and how to fix it. Also reports how the text parsed (roles, experience and project bullets, education, skills and skill groups, input and scored word counts, with definitions). Role-specific advice, such as seniority signals, is given only when the resume names a target title or you pass target_role or job_description; with job_description the score becomes the fit for that job (0.5 requirements met, 0.3 evidence, 0.2 readability, capped for missing hard requirements or impossible claims) and it also returns job_fit and a keyword match summary. Resume up to 30,000 characters. Rules version 2026-10-19.
- `draft_cover_letter`: Draft a cover letter from the user's resume and the job posting with fixed rules: name, contact details and up to two results from the resume, the job title and top keywords from the posting. Facts that are missing show as [square-bracket] gaps for the user to fill, never as stand-ins. Returns the letter, the gaps and a checklist.
- `draft_resignation_letter`: Draft a short, professional resignation letter and work out the last working day from the notice period. Missing facts show as [square-bracket] gaps. Returns the letter, the last day and a checklist (notice stated, no complaints, length).
- `fetch_job_post`: Read a public job posting from its link and return the title, company, location and full description as plain text, ready for match_job, tailor_resume, keyword_scan or interview_questions. Reads LinkedIn, Greenhouse, Lever, Ashby, SmartRecruiters, Workable, Workday and most company careers pages. Some sites block reading; then ask the user to paste the text.
- `generate_linkedin_headline`: Write LinkedIn headline options from the user's answers, each in a proven pattern (title and focus, title and result, title and who you help…), with its length and roughly what shows in search before the cut. Uses only the answers given.
- `improve_bullet`: Check one resume bullet with the fixed rules (score, verdict, fixes) and suggest up to three stronger rewrites that keep every fact as given. Where a number would help, the rewrite leaves a slot such as [X%] for the user to fill. Part of the answer is drafted by an AI model (Gemma 4, with Llama 3.3 70B as a backup, on Cloudflare Workers AI) behind ATSPrep's guard, which drops any figure, name or tool the user's text doesn't contain. If the AI part is unavailable, the rule-based part still comes back and ai.message says why.
- `interview_questions`: Interview questions to practise, each with what the interviewer is listening for, plus STAR answer tips. With a job_description, questions come from its skills and duties, along with what to research and questions to ask; with resume_text, from the user's own roles. Rule-based.
- `keyword_scan`: List the skills, tools and requirements a job posting asks for, ranked by how hard it leans on each (repetition, position, required or preferred headings), with the high-priority ones flagged. Use match_job to see which a resume already shows. Posting 120–20,000 characters. Rules version 2026-10-19.
- `list_templates`: List ATSPrep's resume designs, each with a link that opens Studio in that design, and the example resumes by role, each with a link that opens it in Studio and a preview image.
- `match_job`: Compare a resume with a job posting: the posting's keywords found in the resume (with how they matched: exact, normalized, alias or stem, and the matched span), the ones missing with the posting sentence they come from, each term's weight and whether the posting lists it as required or preferred, and weighted overall, required and preferred coverage. Posting 120–20,000 characters. Rules version 2026-10-19.
- `open_in_studio`: Make a link that opens this resume in ATSPrep Studio, the free editor, so the user can fix it, pick a template and download a PDF. The text travels in the link's #fragment, which is never sent to a server.
- `review_interview_answer`: Review the user's answer to an interview question: a rating, length and speaking time, STAR coverage for stories, filler words, specific checks, the follow-up an interviewer would likely ask, and a stronger version that keeps to the user's own facts. Part of the answer is drafted by an AI model (Gemma 4, with Llama 3.3 70B as a backup, on Cloudflare Workers AI) behind ATSPrep's guard, which drops any figure, name or tool the user's text doesn't contain. If the AI part is unavailable, the rule-based part still comes back and ai.message says why.
- `tailor_resume`: Tailor a resume to a job posting: the posting's keywords the resume is missing (from match_job's rules) and suggested rewrites of the user's own summary and bullets that bring in the posting's terms only where the existing line already supports them. Each suggestion lists the posting terms it borrowed and confirm_with_user lists terms to ask about, never to add unasked. Part of the answer is drafted by an AI model (Gemma 4, with Llama 3.3 70B as a backup, on Cloudflare Workers AI) behind ATSPrep's guard, which drops any figure, name or tool the user's text doesn't contain. If the AI part is unavailable, the rule-based part still comes back and ai.message says why.
- `write_summary`: Draft professional summaries from a resume: rule-based drafts built from its title, skills and the bullets that carry a figure, plus AI drafts that use only facts in the resume. Part of the answer is drafted by an AI model (Gemma 4, with Llama 3.3 70B as a backup, on Cloudflare Workers AI) behind ATSPrep's guard, which drops any figure, name or tool the user's text doesn't contain. If the AI part is unavailable, the rule-based part still comes back and ai.message says why.