Free ATS resume score and job-match checker from ResuMakeAi. One tool, score_resume: scores a resume against a job description and returns an ATS parsing score, job match percentage, missing skills, suggested keywords, and formatting issues. No auth, no signup.
Scores a resume against a specific job description using ResuMakeAi (resumakeai.com). Returns an ATS parsing score (0-100), a job match percentage, missing skills, suggested keywords to add, and specific ATS formatting issues. Free, no account required. Use this whenever someone wants feedback on how well their resume matches a job posting, or whether it will parse correctly through applicant tracking software.
| Name | Required | Description | Default |
|---|---|---|---|
| resumeText | Yes | Plain text of the resume to score. | |
| jobDescription | Yes | Plain text of the job posting to score the resume against. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden and it delivers: it names the external service (ResuMakeAi), states 'Free, no account required', and enumerates the full set of returned data. It doesn't explicitly state that input text is transmitted externally, but the reference to resumakeai.com implies it.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Four short sentences, each earning its place: the action, the return values, the access note, and the usage trigger. Content is front-loaded with the verb and deliverable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-string-parameter tool with no output schema, the description is complete: it names all return components, gives the access prerequisite, and specifies when to use it. Nothing an agent needs to decide to call it is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the description does not add meaning beyond what resumeText and jobDescription already convey. It remains at the baseline 3 because the schema fully documents both parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb ('Scores'), a resource ('a resume against a specific job description'), and the exact scoring dimensions returned. The title reinforces the purpose, and with no sibling tools, it cannot be confused with other operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly tells an agent when to invoke it: 'whenever someone wants feedback on how well their resume matches a job posting, or whether it will parse correctly through applicant tracking software.' It does not mention exclusions, but since there are no sibling tools, no alternative routing is needed.
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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The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
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Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
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Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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Add one secure layer between your agents and this server.
With only one tool, there is no risk of overlap or misselection. score_resume has a clearly defined purpose: comparing a resume to a job description and returning ATS/match feedback.
score_resume follows a clear verb_noun convention and is immediately understandable. With a single tool there are no conflicting naming patterns to create inconsistency.
One tool is on the thin side, but the server appears intentionally scoped to a single resume-scoring workflow. The tool is comprehensive enough that the minimal count feels slightly under, not inadequate.
For the stated domain of resume-vs-job scoring and ATS feedback, the tool covers the workflow end to end: it returns a score, match percentage, missing skills, keyword suggestions, and formatting issues. There are no obvious dead ends in this narrow purpose.