MaxCV.ai
Server Details
Tool that tailors your existing CV/resume to a specific job posting. Beat AI screening with AI tailoring.
- 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.
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.
Tool Definition Quality
Average 4.4/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: 'score_cv' evaluates match and shows gap, while 'tailor_cv' rewrites the CV. There is no overlap in functionality.
Both tools follow a consistent verb_noun pattern: 'score_cv' and 'tailor_cv'. The naming is predictable and clear.
With only 2 tools, the server is minimal but still appropriate for its focused domain of CV scoring and tailoring. It covers the essential workflow without unnecessary extras.
The tools cover the core operations: scoring and tailoring. A minor gap is the lack of a standalone keyword extraction tool, but the score_cv tool provides requirement counts, mitigating this.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| cvText | Yes | The full CV/resume as plain text. | |
| jobDescription | Yes | The job posting text. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses outputs (original score, tailored score, requirement counts) and performance characteristics ('fast and cheap'). Adequate for a simple tool.
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?
Two sentences with no wasted words. First sentence packs purpose and outputs, second sentence adds usage context. Ideal length.
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?
Complete for a 2-param, no-output-schema tool. Covers purpose, outputs, and usage order. Could elaborate on score range, but not essential.
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 coverage is 100%, so baseline is 3. Description adds minimal extra meaning ('full CV/resume' vs 'CV'), but the schema already describes both parameters sufficiently.
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?
Clearly states the verb 'Score' with specific resource 'CV/resume matching job posting'. Distinguishes from sibling 'tailor_cv' by positioning as a first-step gap analysis.
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 says 'Use this first to show the user the gap before a full tailor', providing clear workflow context. Could be improved with explicit when-not-to-use cases.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| cvText | Yes | The full CV/resume as plain text. | |
| jobDescription | Yes | The job posting text. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It reveals the tool rewrites the CV without fabricating skills, returns four specific items, and is rate-limited per IP. This is comprehensive, though it could mention if results are cached or if there are authentication requirements.
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?
The description is extremely concise: two sentences. The first sentence introduces the core function and key constraint. The second sentence addresses usage limits and upsell. No unnecessary words, and the most important information is front-loaded.
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?
Given the tool has only two simple parameters (100% schema coverage) and no output schema, the description adequately covers what the agent needs: it lists the return items (tailored CV, match score, role-fit notes, interview prep). No additional details are necessary for correct invocation.
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?
The schema descriptions for cvText and jobDescription are basic but clear. The description adds value by stating the constraint about not fabricating skills, which provides behavioral context for the cvText parameter. This goes beyond mere schema documentation.
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?
The description clearly states the tool's purpose: tailoring a CV to a job posting by rewriting with ATS keywords, returning the tailored CV, match score, role-fit notes, and interview prep. It distinguishes from sibling 'score_cv' by implying this tool also returns the tailored output, not just a score.
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?
The description provides clear context on when to use the tool (to tailor a CV to a job posting) and includes a critical constraint (never fabricating skills). It mentions rate-limiting and an alternative for unlimited use, but does not explicitly contrast with the sibling 'score_cv' tool for when to choose one over the other.
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!