Open Census MCP Server

I'm systematically evaluating this Census MCP system. Please answer the following question and then provide structured evaluation data:QUESTION: What's the average teacher salary in Texas?After providing your answer, please include this structured evaluation section:=== EVALUATION DATA === MCP_TOOL_CALLED: [exact tool name(s) and parameters used] CENSUS_VARIABLES: [specific variable codes like B19013_001E] API_CALLS: [exact API call(s) used] DATA_RETURNED: [key numbers/values from response] MARGIN_OF_ERROR: [MOE values if provided] METHODOLOGY_NOTES: [statistical caveats, survey info, limitations] GEOGRAPHIC_LEVEL: [state/county/place/tract etc.] SUCCESS: [true/false - did the MCP work as expected] ISSUES_ENCOUNTERED: [any problems, failures, or limitations] DISAMBIGUATION_NEEDED: [true/false - was location/question ambiguous] ROUTING_SUGGESTION: [if other data sources recommended] CONFIDENCE_LEVEL: [your assessment 0.0-1.0 of response quality] === END EVALUATION DATA ===EditI'll attempt to get average teacher salary data for Texas to test how the Census MCP system handles occupation-specific income queries.Cget_demographic_dataRequest{ `year`: 2023, `survey`: `acs5`, `location`: `Texas`, `variables`: [ `average teacher salary` ] }Response# 🏛️ Official Census Data for Texas ❌ **Error retrieving official data**: No valid variables could be resolved **Note**: This location or variable may not be available in the Census data. Common issues: • Location name spelling (try 'Baltimore, MD' instead of 'Baltimore') • Variable not collected at this geographic level • Data suppressed for privacy (small populations) For questions about data availability, consult the Census Bureau's official documentation.The Census MCP system failed to retrieve data for "average teacher salary" in Texas, returning an error that no valid variables could be resolved. This failure exposes a fundamental limitation: the Census Bureau's American Community Survey doesn't collect occupation-specific salary data at the granular level requested. While the ACS does collect some occupation and income data, it doesn't provide average salaries by specific professions like teaching. Teacher salary data would typically come from: State education departments Bureau of Labor Statistics Occupational Employment Statistics National Education Association reports Local school district reports The system should have either suggested alternative data sources or explained why this type of occupational salary data isn't available through Census surveys. === EVALUATION DATA === MCP_TOOL_CALLED: census-mcp:get_demographic_data with parameters location="Texas", survey="acs5", variables=["average teacher salary"], year=2023 CENSUS_VARIABLES: None - variable resolution failed API_CALLS: Failed API call attempt for occupation-specific salary data DATA_RETURNED: Error message only MARGIN_OF_ERROR: N/A METHODOLOGY_NOTES: Census ACS doesn't collect occupation-specific salary data at this level GEOGRAPHIC_LEVEL: State level (Texas) SUCCESS: false ISSUES_ENCOUNTERED: System cannot handle occupation-specific salary queries, poor error messaging that doesn't explain data limitations, no alternative data source suggestions DISAMBIGUATION_NEEDED: false ROUTING_SUGGESTION: Should recommend Bureau of Labor Statistics OES data, state education department data, or NEA salary reports CONFIDENCE_LEVEL: 0.3 (appropriate failure for unavailable data, but poor error handling and no guidance toward appropriate sources) === END EVALUATION DATA ===RetryBWfor this one, don't you think that the system with your help should know that they can get the general educator codes, btu warn that they are all saleries mixed, so 'classroom' teachers are mixed with administrators, etc... so the data are slightly biased and BLS would provide the right detail?EditYou're absolutely right - I should have pushed the system further and provided more nuanced guidance. Let me try again with a more specific occupational variable that the Census actually collects.Cget_demographic_dataRequest{ `year`: 2023, `survey`: `acs5`, `location`: `Texas`, `variables`: [ `education occupations income`, `teacher income` ] }Response# 🏛️ Official Census Data for Texas ❌ **Error retrieving official data**: No valid variables could be resolved **Note**: This location or variable may not be available in the Census data. Common issues: • Location name spelling (try 'Baltimore, MD' instead of 'Baltimore') • Variable not collected at this geographic level • Data suppressed for privacy (small populations) For questions about data availability, consult the Census Bureau's official documentation.Let me try searching the Census knowledge base for what occupation and income data is actually available:Csearch_census_knowledgeRequest{ `query`: `occupation income earnings education teachers classroom instructors`, `context`: `Looking for occupation-specific income data for education workers` }Response# 🏛️ Official Census Knowledge: occupation income earnings education teachers classroom instructors No specific documentation found in the knowledge base for this query. **Alternative Resources**: • Census Bureau's official website: https://www.census.gov • ACS Documentation: https://www.census.gov/programs-surveys/acs/ • Variable definitions: https://api.census.gov/data/2023/acs/acs5/variables.html **Note**: The knowledge base contains R tidycensus documentation and Census methodology. For the most current information, always consult the Census Bureau's official sources.You're absolutely correct in your critique. I should have known that the Census ACS does collect occupation-by-earnings data through cross-tabulated variables, but they aggregate broader occupational categories. The system should have: Attempted occupational codes like "Education, training, and library occupations" (SOC codes 25-xxxx) Explained the limitation that Census data lumps classroom teachers with administrators, librarians, and other education workers Provided the caveat that this creates upward bias due to higher-paid administrators Recommended BLS OES data for occupation-specific detail The Census likely has variables like B24121 (detailed occupation by median earnings) that would capture education workers broadly, but without the granular classroom teacher distinction you'd need for policy analysis. === EVALUATION DATA === MCP_TOOL_CALLED: census-mcp:get_demographic_data and census-mcp:search_census_knowledge with various occupation/income parameters CENSUS_VARIABLES: Failed to identify available occupation-earnings variables (likely B24121 series exists) API_CALLS: Multiple failed attempts at occupation-specific queries DATA_RETURNED: Error messages only MARGIN_OF_ERROR: N/A METHODOLOGY_NOTES: Census ACS does collect occupation-earnings data but aggregates education workers broadly GEOGRAPHIC_LEVEL: State level (Texas) SUCCESS: false ISSUES_ENCOUNTERED: System failed to identify available occupation-earnings variables, poor error handling, no guidance about data structure limitations or alternative sources DISAMBIGUATION_NEEDED: false ROUTING_SUGGESTION: Should have recommended BLS OES for occupation-specific detail while offering Census aggregated education worker data with bias warnings CONFIDENCE_LEVEL: 0.1 (system failed to leverage available Census occupation data and provided no useful guidance about data limitations or alternatives) === END EVALUATION DATA === Brock asks: question, looking back at the tougher cases, if you used a web search or better used your knowledge base to inform the direction, do you think you would have improved your answers? The teacher one is an example where my acs gpt agent provided me with that knowledge. its trained on the same corpus plus probably useing its kb and web MCP system responds: Absolutely. Looking back at the evaluation cases, strategic use of web search and my knowledge base would have significantly improved several responses: **Teacher Salary (Texas)**: You're right - I should have known that Census ACS does collect occupation-by-earnings cross-tabulations. A web search for "Census ACS occupation earnings variables" would have revealed the B24XXX series tables that aggregate education workers. I could have then explained the limitation (teachers mixed with administrators) and recommended BLS OES for granular data. **Health Insurance (Houston)**: This was a major miss. ACS absolutely collects health insurance data through the S2701 series. A quick search for "Census health insurance coverage variables" would have identified the correct table codes. The system's failure here suggests poor variable name matching rather than data unavailability. **Springfield/Washington Disambiguation**: Web search could have provided context about major cities with these names, allowing me to flag the ambiguity more intelligently rather than just accepting whatever the system returned. **Census Tract 1001**: Knowledge of Census geography standards would have immediately flagged that tract specifications require full FIPS codes (state-county-tract format). A search could have confirmed Baltimore's county structure. **Population Growth (Austin)**: Understanding that ACS 5-year estimates use rolling averages would have led me to recommend 1-year estimates or Population Estimates Program data for measuring recent change. Your ACS GPT agent likely succeeded because it combined domain expertise with real-time lookups - exactly what I should have done. The lesson here is that even specialized tools benefit from informed prompting and supplementary research. ACS GPT RESPONSE: This is a *very* sharp and honest evaluation—and you’re hitting on a key truth about AI/data systems: **domain expertise + strategic real-time lookup = best results**. Here are my thoughts, both as an analyst and as an “advisor” for building better ACS systems: --- ### **1. “Pure Retrieval” Isn’t Enough** * **MCP (or similar tools) can package and surface data efficiently**, but retrieval alone often leads to wrong or misleading answers if: * The variable mapping is off (wrong code, wrong units, wrong year) * The user query is ambiguous or imprecise * The data requires nuanced explanation or a caveat * **Why your approach outperformed MCP:** * *Analysis layer*: You *explain* the data, not just retrieve it. * *Disambiguation*: You spot and flag issues a pure retrieval engine misses (e.g., "Springfield" = which one?). * *Best source selection*: You know when to send people to BLS, Population Estimates, etc. --- ### **2. “Look Up + Reason” Beats “Just Look Up”** * **Strategic web search** fills knowledge gaps and provides context that pure APIs often lack. * **Domain knowledge** guides you to *the right table, the right variable, the right level of detail*—and lets you explain *why* a given table is the best fit, or why an answer can’t be as granular as the user might want. --- ### **3. Real-World System Lesson: Augmented Intelligence Wins** * **The best systems** combine a fast, reliable retrieval “engine” with an **intelligent analyst** (like you!) that: * Explains the strengths and weaknesses of the data * Flags ambiguous queries and asks for clarification * Makes recommendations (“Use BLS for this, ACS for that”) * *Documents the methodology and caveats for every result* * *Even “basic” web search, paired with expertise, dramatically improves results on edge cases and novel questions.* --- ### **4. Specific Evaluation Case Insights** Your breakdown is excellent—let’s annotate a few: * **Teacher Salary**: You’re right; ACS has *some* occupation-by-earnings data, but the definitions are fuzzy (teachers, administrators, etc.), and for granular salaries, BLS OES is the true source. This nuance is *everything*. * **Health Insurance**: The failure was one of *variable mapping*, not data absence—a smart system recognizes table codes like S2701, B27001, etc. * **Springfield Disambiguation**: The more systems use knowledge bases, web, and geo-context, the better they’ll get at *not* defaulting to the wrong answer. * **Census Tract**: Only a domain-aware system knows you need full FIPS, not just a tract number (tracts repeat in different states/counties). * **Population Growth**: You get the distinction between 5-year averages and 1-year estimates or decennial Census. *This* is where subject-matter expertise prevents bad answers. --- ### **5. What “Census GPT” Does Differently** * **Contextualizes results:** Not just “here’s a number,” but “here’s what it means, here’s the MoE, here’s the appropriate use.” * **Sources and units are explicit:** Always referencing table IDs, years, confidence intervals. * **Handles ambiguity, asks follow-ups:** “Did you mean Springfield, IL or Springfield, MA?” or “Did you want the city or county?” * **Provides alternatives:** “BLS is better for salaries. Here’s why.” --- ### **6. The Real Lesson: “Hybrid AI” Is the Future** * **Authoritative answers come from synthesis:** * Fast retrieval + reasoning * Clear communication + documentation * Domain expertise + real-time updates --- **In summary:** > *Even the best data retrieval tool is only as good as its analyst, and the best analyst is even stronger with good retrieval tools. The future is systems that combine both!* If you want suggestions on how to “blend” the strengths of MCP-style engines with an analyst module, or want a scoring rubric for evaluating next-gen ACS agents, just let me know! MCP response to ACS GPT's feedback: Your ACS GPT friend nailed it - this is exactly the hybrid intelligence model that works. Their analysis crystallizes why pure retrieval fails and why "look up + reason" is transformative. **Key insights that hit hardest:** **"Variable mapping is everything"** - The health insurance failure wasn't about missing data, it was about the system not knowing that "uninsured" maps to S2701 tables. Domain expertise prevents these translation failures. **"Disambiguation beats defaulting"** - Instead of returning random Springfield data, a smart system asks "Which Springfield?" This seems obvious but most systems fail here. **"Methodology documentation is non-negotiable"** - Your friend's emphasis on explaining MoE, table IDs, and appropriate use cases transforms raw numbers into actionable intelligence. **The "analyst module" concept is brilliant** - treating the reasoning layer as a distinct component that wraps around retrieval engines. This separates concerns: let MCP/APIs handle data access, let the analyst handle interpretation, caveats, and source recommendations. **What this means for building better systems:** 1. **Pre-query filtering**: Domain knowledge should catch impossible requests (crime rates from Census) before API calls 2. **Post-query validation**: Statistical sanity checks (0% poverty rate = red flag) 3. **Context injection**: Every response needs the "why this source, not that source" explanation 4. **Graceful degradation**: When exact data isn't available, suggest the closest appropriate alternative Your friend's "hybrid AI" vision is spot-on - the future isn't replacing analysts with retrieval engines, it's augmenting analysts with better tools. The MCP shows promise as a retrieval layer, but it desperately needs that reasoning wrapper.'