Career Copilot MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_jobsA | Find Data Analyst job postings, highest estimated salary first. Args: skill: Filter to postings whose description mentions this skill. Must be one of: python, sql, excel, tableau, aws, spark. Any other value is rejected. location: US metro in "City, ST" form, e.g. "New York, NY" or "Austin, TX". A partial name like "Austin" is accepted when it is unambiguous. Read market://snapshot for the most common values before guessing. min_salary: Minimum estimated average annual salary in USD, e.g. 90000. limit: How many postings to return, 1-25. Defaults to 5. Ask for more only when the user explicitly wants a long list — each posting costs context. Returns total_matches (how many postings matched overall) alongside the returned slice, so you can tell the user how much you are not showing them. |
| salary_benchmarkA | Get the salary distribution for a slice of the job market. Returns median, 25th and 75th percentile, min and max — plus how many postings the numbers are based on, which matters because narrow slices get thin fast. Args: location: US metro in "City, ST" form. Omit for a nationwide figure. skill: One of python, sql, excel, tableau, aws, spark. Omit to include all postings regardless of skill. Prefer this over calling search_jobs and averaging the results yourself: this uses all matching postings, while search_jobs returns at most 25. |
| skill_demandA | Show how often each tracked skill appears in postings, and its salary effect. For each of python, sql, excel, tableau, aws and spark, returns the number of postings mentioning it, its share of the corpus, and the median salary both with and without that skill — so the pay gap is directly comparable. Takes no arguments; it always covers the whole dataset. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| career_gap_review | Compare a set of skills you already have against what employers are asking for, and get a ranked list of what to learn next. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| Market snapshot | Corpus-level summary of the job postings dataset: total postings, median salary, the most common locations and sectors, and the list of tracked skills. Read this before calling tools — it supplies the valid location names so you don't have to guess. |
| Covered locations | Every US metro in the dataset with a posting count, most common first. Use this to resolve a vague user location like 'the Bay Area' into a value the search_jobs tool will accept. |
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: searching job postings, computing salary benchmarks, and analyzing skill demand. No overlap in functionality; an agent can easily select the right tool based on the user's query.
All tool names follow a consistent lowercase snake_case pattern (search_jobs, salary_benchmark, skill_demand). While the first word is not always a verb, the naming style is uniform and predictable, avoiding any mixing of conventions.
With only 3 tools, the set is tightly scoped to the server's purpose of job market analytics. Each tool covers a distinct high-level capability and none feel redundant, making the count appropriate for the narrow domain.
The tool set covers the core workflows of job searching, salary benchmarking, and skill demand analysis. Minor gaps exist, such as no direct method to fetch a single job posting's full detail beyond search results, but agents can work around this by using the returned data. Overall, the surface is complete for the stated purpose.