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Fusingchart

college-aid-mcp

by Fusingchart
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

college-aid-mcp

An MCP server that combines the US College Scorecard (Dept of Education) and CareerOneStop Scholarship Finder (Dept of Labor) so you can ask Claude to research colleges and matching scholarships in one conversation.

What it can do

search_colleges — search colleges by name, state, degree type, or max tuition. Returns for each school:

  • In-state / out-of-state tuition

  • Median debt at graduation

  • Median earnings 10 years after entry

  • 4-year graduation rate

  • Admission rate

search_scholarships — search 9,500+ scholarships and grants. Filter by keyword, state, study level. Returns award amount, deadline, provider, and direct link.

find_similar_colleges — given a reference school, generate a reach/target/safety list of alternatives with similar Carnegie classification. Filters by admission rate windows (reach = more selective, target = ±7%, safety = less selective), then sorts each bucket by earnings, tuition, or closest match. Falls back to adjacent Carnegie tiers if a bucket returns too few results.

compare_colleges — side-by-side comparison table for 2–5 schools. Runs all lookups in parallel and returns a single markdown table across 18 metrics: location, type, acceptance rate, SAT/ACT ranges, in-state/out-of-state tuition, net price at 3 income brackets, median debt, Pell rate, 4-yr grad rate, retention, 10-yr earnings, and first-gen student share.

search_careers — search careers by job title or keyword using CareerOneStop/O*NET data. Returns for each occupation:

  • Annual salary percentiles (P25 / median / P75 / P90), nationally and by state

  • Bright job outlook flag (above-average growth or openings)

  • Typical education required

  • Key job tasks

estimate_loan_repayment — pure computation, no API key needed. Given a loan balance and expected salary, calculates:

  • Standard 10-year plan: monthly payment, total interest

  • Extended 25-year plan: lower monthly cost, higher total interest

  • Income-driven repayment (IDR): payment as % of discretionary income, forgiveness after 20 years if balance remains

  • Debt-to-income health check and a recommendation on which plan makes sense

get_college_details — deep dive on a single school. Returns:

  • Net price broken down by family income bracket ($0–30k, $30–48k, $48–75k, $75–110k, $110k+)

  • SAT/ACT score ranges (25th–75th percentile)

  • Acceptance rate, enrollment, 1st-year retention, 4-year graduation rate

  • First-generation student share, Pell grant rate

  • Median debt at graduation and 10-year earnings

  • Institution type, Carnegie classification, locale

search_by_major — compare the same major across schools using program-specific data, not school-wide averages. Supports 60+ major aliases (e.g. "cs", "nursing", "mechanical engineering") or raw 4-digit CIP codes. Returns for each school:

  • Program-specific 4yr median earnings (vs. national benchmark for that program)

  • Program-specific median student debt

  • In-state tuition

  • Sortable by earnings, debt, or tuition

Example prompts

  • "Find CS-focused universities in Washington state with tuition under $15k and show me STEM scholarships I could apply to as a high schooler."

  • "Compare median debt vs earnings for the top 10 engineering schools."

  • "Find first-generation college student scholarships in California."

  • "Rank nursing bachelor programs in Texas by earnings — which schools beat the national median?"

  • "Show me mechanical engineering programs sorted by lowest debt in the midwest."

  • "Get full details for Georgia Tech — what would I actually pay if my family earns $60k/year?"

  • "Compare Georgia Tech and Carnegie Mellon on net price, SAT ranges, and 10-year earnings."

  • "I'll graduate with $28k in debt and expect to earn $55k — show me my repayment options."

  • "Find CS programs in California sorted by lowest debt, then estimate repayment for the top school."

  • "I'm targeting University of Washington — give me reach, target, and safety alternatives sorted by lowest cost."

  • "Compare MIT, Stanford, Georgia Tech, and Carnegie Mellon side by side on cost, debt, and earnings."

  • "What do software engineers actually earn in Washington state, and which CS programs give the best shot at hitting P75 salary?"

  • "Compare nursing salaries in Texas vs nationally, then find nursing programs with the lowest debt."

Related MCP server: lenderwiki

Setup

1. Get API keys (both free)

College Scorecard API key

  1. Go to api.data.gov/signup

  2. Register — you'll receive a key by email immediately

  3. Set as env var: COLLEGE_SCORECARD_API_KEY=your_key

CareerOneStop API credentials

  1. Go to api.careeronestop.org/api-explorer

  2. Register for a free account

  3. You'll receive a User ID and Token

  4. Set as env vars: CAREERONESTOP_USER_ID=your_user_id and CAREERONESTOP_TOKEN=your_token

2. Install

git clone https://github.com/Fusingchart/college-aid-mcp.git
cd college-aid-mcp
npm install
npm run build

3. Configure Claude Desktop

Add to your claude_desktop_config.json (usually at ~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "college-aid": {
      "command": "node",
      "args": ["/absolute/path/to/college-aid-mcp/build/index.js"],
      "env": {
        "COLLEGE_SCORECARD_API_KEY": "your_scorecard_key",
        "CAREERONESTOP_USER_ID": "your_user_id",
        "CAREERONESTOP_TOKEN": "your_token"
      }
    }
  }
}

4. Configure Claude Code

claude mcp add college-aid \
  -e COLLEGE_SCORECARD_API_KEY=your_key \
  -e CAREERONESTOP_USER_ID=your_user_id \
  -e CAREERONESTOP_TOKEN=your_token \
  -- node /absolute/path/to/college-aid-mcp/build/index.js

Data sources

Source

Provider

Cost

College Scorecard API

US Dept of Education

Free

CareerOneStop Scholarship Finder

US Dept of Labor

Free

Available Tools

4 tools
get_college_detailsA

Deep dive on a single school: net price broken down by family income bracket, SAT/ACT score ranges, retention rate, first-generation student share, financial aid stats, and 10-year earnings. Much more detail than search_colleges.

ParametersJSON Schema
NameRequiredDescriptionDefault
nameYesSchool name to look up, e.g. 'MIT', 'University of Washington', 'Georgia Tech'

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It lists the data returned (net price, SAT/ACT, etc.) but does not disclose behavior for missing schools, error handling, or authorization needs. It implies read-only but lacks full transparency.

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 a single sentence plus a comparative statement, highly concise and front-loaded with the core purpose. Every part adds value without redundancy.

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?

Given the tool's simplicity (one parameter, no output schema), the description covers the main purpose and output content well. It could mention the output format, but overall it is sufficiently complete for an agent to understand what the tool returns.

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 description adds no new meaning for the 'name' parameter beyond what the schema already states. The list of returned details implicitly guides usage but does not enhance parameter semantics.

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 a specific verb 'deep dive' and resource 'single school', and clearly distinguishes itself from the sibling tool 'search_colleges' by noting it provides much more detail.

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 states this tool is for when you need more detail than search_colleges, giving clear context. However, it does not explicitly mention when not to use it or alternative tools for other cases.

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

search_by_majorA

Compare the same major across different colleges using College Scorecard field-of-study data. Returns program-specific median earnings, median debt, and in-state tuition for each school — far more accurate than school-wide averages.

ParametersJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of results
majorYesMajor or field of study, e.g. 'computer science', 'nursing', 'mechanical engineering'. Also accepts 4-digit CIP codes directly.
stateNoTwo-letter state code to filter schools, e.g. 'WA'
sort_byNoSort results by highest earnings, lowest debt, or lowest tuitionearnings
credential_levelNoDegree level to comparebachelor

TDQS

A4/5.0
Behavior3/5

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

No annotations provided, so description carries full burden. It accurately describes the data returned and emphasizes accuracy over averages, but lacks broader behavioral context such as authentication requirements, rate limits, or side effects. Adequate but not rich.

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 purpose, each sentence adds value. No fluff or repetition.

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?

Given 5 parameters and no output schema, the description explains what data is returned (earnings, debt, tuition) and why it's useful. Sufficient for an agent to understand the tool's output without additional documentation.

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%, so baseline is 3. Description does not add meaning beyond schema for parameters like 'major' accepting CIP codes (already in schema description). No additional semantic value or constraints beyond what schema provides.

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?

Description clearly states 'Compare the same major across different colleges' and lists specific metrics (median earnings, median debt, in-state tuition). It distinguishes from sibling tools like get_college_details, search_colleges, and search_scholarships by focusing on cross-college major comparisons using field-of-study data.

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?

Explicitly states when to use (comparing majors across colleges) and contrasts with school-wide averages, implying alternative. Does not explicitly mention when not to use or list sibling tools by name, but the context is clear.

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

search_collegesA

Search colleges by name, state, or degree type. Returns tuition, median debt, median earnings, graduation rate, and admission rate for each match.

ParametersJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of results
queryNoSchool name or keyword (optional)
stateNoTwo-letter state code, e.g. 'WA'
degree_typeNoDegree type to filter by
max_tuitionNoMaximum in-state tuition per year (USD)

TDQS

A3.6/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden. It lists returned fields but omits important behavioral traits such as pagination, default ordering, behavior with no query, and rate limits.

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 with two sentences: one for purpose and one for return fields. 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?

Given the tool has 5 parameters and no output schema, the description covers the basic purpose and output fields. However, it lacks details on behavior like result ordering or default query handling, which would improve completeness.

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 parameters are well-documented in schema. The description adds no additional parameter meaning beyond the schema, hence a baseline of 3.

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 clearly states the action ('Search colleges') and specifies filtering criteria (name, state, degree type), as well as return fields. It distinguishes from siblings like 'get_college_details' by indicating a search over multiple colleges.

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

Usage Guidelines3/5

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

No explicit guidance on when to use this tool versus alternatives, but the description implies it is for searching colleges with filters, and sibling names provide some context (e.g., 'get_college_details' for details on a single college).

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

search_scholarshipsA

Search 9,500+ scholarships and grants from the US Dept of Labor CareerOneStop database. Filter by keyword, state, study level, or major.

ParametersJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of results
stateNoTwo-letter state code to find scholarships restricted to that state, e.g. 'WA'
keywordNoKeyword to search scholarships by (e.g. 'engineering', 'STEM', 'first generation')
study_levelNoEducation level the scholarship is for

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, and the description only mentions the dataset and filters. It fails to disclose return format, pagination, order, or any limitations, leaving behavioral traits unclear.

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?

Single sentence that is front-loaded with the key action and relevant details, no unnecessary words.

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

Completeness3/5

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

Given no output schema and moderate complexity, the description provides basic context but omits expected output format and result ordering, which would aid an AI agent.

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 description adds no new meaning to parameters. It merely restates the filter options already defined in the 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?

The description clearly states the tool searches scholarships and grants from a specific database (US Dept of Labor CareerOneStop) and lists filtering options, distinguishing it from sibling tools focused on colleges and majors.

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

Usage Guidelines3/5

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

The description implies usage for scholarship searches but does not explicitly state when to use this tool over siblings or what to do if results are insufficient.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 4 tool updatesv0.1.0
    • First observedget_college_details
    • First observedsearch_by_major
    • First observedsearch_colleges
    • First observedsearch_scholarships
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TDQS

A4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: general school search, deep college details, major-specific comparisons, and scholarship search. No overlap or ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., search_colleges, get_college_details), making the set predictable and easy to navigate.

Tool Count5/5

4 tools is well-scoped for the college aid domain, covering school search, detailed info, major analysis, and scholarships without being excessive or insufficient.

Completeness4/5

Covers core college search and scholarship lookup. Minor gaps like side-by-side comparison or financial aid calculators exist, but agents can work around them.

Maintenance

ActivityStale
ResponsivenessNo issues

Resources

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