Skip to main content
Glama

list_generated_resumes

View and filter AI-generated resumes tailored for specific job applications. Manage automatically created application documents by job ID or trigger type.

Instructions

List AI-generated custom resumes. These are resumes that were automatically tailored for specific job applications.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobApplicationIdNoFilter by job application ID
manualTriggerNoFilter by whether resume was manually triggered

Implementation Reference

  • The tool 'list_generated_resumes' is defined and implemented here using the `server.tool` MCP method. It maps the input arguments to a client call and formats the response.
    server.tool(
      'list_generated_resumes',
      'List AI-generated custom resumes. These are resumes that were automatically tailored for specific job applications.',
      {
        jobApplicationId: z.string().optional().describe('Filter by job application ID'),
        manualTrigger: z.boolean().optional().describe('Filter by whether resume was manually triggered'),
      },
      async (args) => {
        const result = await client.listGeneratedResumes({
          jobApplicationId: args.jobApplicationId,
          manualTrigger: args.manualTrigger,
        });
        const response = {
          count: result.resumes.length,
          generatedResumes: result.resumes.map(r => ({
            id: r._id,
            jobApplicationId: r.jobApplicationId,
            jobTitle: r.meta?.title,
            company: r.meta?.companyName,
            filename: r.fileName,
            status: r.status,
            aiRelevancyScore: r.aiRelevancyScore ? `${Math.round(r.aiRelevancyScore * 100)}%` : null,
            createdAt: r.dateCreated,
          })),
        };
        return { content: [{ type: 'text' as const, text: JSON.stringify(response, null, 2) }] };
      }
    );
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool lists resumes but doesn't describe output format, pagination, sorting, permissions required, rate limits, or whether it's a read-only operation. The phrase 'automatically tailored' hints at generation context but lacks operational details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences with zero waste, front-loading the core action ('List AI-generated custom resumes'). It could be slightly improved by integrating filtering hints, but it's efficiently structured and appropriately sized for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description is incomplete. It doesn't explain what the output contains (e.g., resume metadata, content, links), how results are organized, or error conditions. For a list operation with filtering parameters, more context on behavior and results is needed to be fully helpful.

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 fully documents both parameters (jobApplicationId, manualTrigger). The description adds no parameter-specific information beyond what's in the schema, such as example values or combined filtering logic. Baseline 3 is appropriate when the schema handles parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'List AI-generated custom resumes' with the specific resource (resumes) and qualification (AI-generated, tailored for job applications). It distinguishes from siblings like 'list_resumes' by specifying the AI-generated subset, though it doesn't explicitly contrast with 'get_generated_resume' (singular) or 'list_applications'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'list_resumes' (which might include all resumes) or 'get_generated_resume' (which retrieves a single resume), nor does it specify prerequisites or contextual triggers for listing generated resumes.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/6figr-com/job-gpt-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server