A
licenseA
qualityA
maintenanceTailor your CV to any job posting with ATS keyword scoring and clean PDF/DOCX export.
Last updated
6
1
MIT
maxcv — CV tailoring
by ai.maxcv
Server Details
Score and tailor your CV/resume against a job posting — for AI agents and humans, no-login trial.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
MCP client
Glama
MCP server
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
100% free. Your data is private.
Tool Definition Quality
A4.4/5.0
Tool DescriptionsA
Average 4.4/5 across 2 of 2 tools scored.
Server CoherenceA
Disambiguation5/5
Each tool has a distinct, non-overlapping purpose: score_cv for assessment and gap analysis, tailor_cv for actual rewriting. No ambiguity.
Naming Consistency5/5
Both tools follow a consistent verb_noun pattern with underscores: score_cv and tailor_cv, making them predictable.
Tool Count4/5
With only 2 tools, the server is minimal but still covers the core workflow of scoring and tailoring. Could potentially include a tool for resetting or managing CVs, but the count is appropriate for the narrow domain.
Completeness4/5
The tools cover the essential steps: assess the gap and then tailor. While there are no additional tools for viewing history or managing multiple CVs, the surface is sufficient for the stated purpose.
Available Tools
2 toolsscore_cvAInspect
Score how well a CV/resume matches a specific job posting. Fast and cheap — returns the original match score and the score after tailoring, plus requirement counts. Use this first to show the user the gap before a full tailor.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| cvText | Yes | The full CV/resume as plain text. | |
| jobDescription | Yes | The job posting text. |
Tool Definition Quality
A4.4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses performance (fast and cheap), return content (original and tailored scores, requirement counts), and implied non-destructive nature. With no annotations, description carries burden and does so adequately.
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, front-loaded with main action, highly efficient with no wasted words.
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?
Explains return values in absence of output schema, clarifies usage context. Could add note on expected input format (plain text) but sufficient overall.
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% with clear descriptions. The tool description adds no param-specific detail beyond stating it takes full CV and job posting as plain text, which is already in schema.
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 it scores a CV against a job posting, specifies returned values, and distinguishes from sibling tool tailor_cv by noting usage order.
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 advises using this tool first to identify gaps before tailoring, referencing the sibling tool tailor_cv for the full tailor.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
tailor_cvAInspect
Tailor a CV/resume to a specific job posting: rewrites the CV with the posting's ATS keywords (never fabricating skills not already present), and returns the tailored CV, a match score, role-fit notes and interview prep. Trial is rate-limited per IP; for unlimited use the user should sign up at maxcv.ai.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| cvText | Yes | The full CV/resume as plain text. | |
| jobDescription | Yes | The job posting text. |
Tool Definition Quality
A4.4/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully carries the burden of behavioral disclosure. It explicitly states that skills are never fabricated, returns detailed outputs (tailored CV, match score, role-fit notes, interview prep), and discloses rate-limiting and sign-up requirement.
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 concise—two sentences that efficiently cover purpose, deliverables, constraints, and call-to-action. Every sentence adds value without repetition or fluff.
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?
The description explains what outputs are returned (tailored CV, match score, notes, prep) and the rule about not fabricating skills. Without an output schema, this provides sufficient context, though it could specify output format or score range.
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%, so the schema already describes both parameters adequately. The description adds some context (e.g., 'rewrites the CV with ATS keywords') but does not significantly enhance the meaning beyond what the schema provides, so 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 the specific verb 'Tailor' and identifies the resource as 'CV/resume'. It clearly states the action (rewriting with ATS keywords) and distinguishes from sibling tool 'score_cv' by mentioning the deliverables (tailored CV, match score, notes, prep).
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 explains when to use the tool (tailoring a CV to a job posting) and notes rate-limiting for trial use. However, it does not explicitly contrast with the sibling tool 'score_cv' or state when not to use it, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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
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
Centralized credential management – store and rotate API keys and OAuth tokens in one place
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.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Servers
- AlicenseAqualityCmaintenanceRewrites resumes to beat ATS screening (Workday, Greenhouse, iCIMS, Taleo) against a specific job description, with strict truthfulness guardrails — never invents dates, metrics, titles, or seniority. Pay-what-you-want access codes ($0 works).Last updated2MIT
- Alicense-qualityBmaintenanceAnalyzes resume text against job descriptions to provide ATS fit scores, missing keywords, and suggestions.Last updatedMIT
- Flicense-qualityDmaintenanceAnalyzes resumes against job descriptions to identify missing skills, keywords, and improvement opportunities using AI. Provides structured feedback including gap analysis, ATS optimization suggestions, and actionable recommendations to improve job application success.Last updated