Chakudya MCP Server — Cloudflare Workers edition

This is the Cloudflare Workers deployment of the Chakudya MCP server. It exposes the clinical calculators, reference tables, screening workflows and the Chakudya Nutrition Registry (CNR) lookups as tools over MCP Streamable HTTP. It serves the same tool set as the older Render/Express deployment (chakudya-mcp-server repo). The full list is under Tools below.

What's different from the Render version

Only the hosting-layer plumbing — nothing about the tools or the CNR API they call:

The tools in src/tools/* and the WHO growth data match the Render version.

Related MCP server: fhirHydrant

Tools

Tools that need chakudya-api (food lookups, logs, RAG and so on) call it through the CHAKUDYA_API service binding with CHAKUDYA_API_KEY, so that secret must be set. The list below is checked against the code by src/readme.test.ts. After adding or removing a tool, run UPDATE_README=1 npx vitest run src/readme.test.ts to regenerate it.

170 tools: activity_energy_calculate, activity_energy_list, adjusted_body_weight_calculator, adult_integrated_screen, alcohol_kcal_calculator, amputation_weight_adjustment_calculator, analyze_meal, analyze_meal_clinical, aspen_refeeding_initiation_guidance, aspen_refeeding_risk_adult, aspen_refeeding_risk_pediatric, aspen_refeeding_syndrome_severity, atwater_food_energy_calculator, barcode_lookup, bmi_classification, bmi_for_age_classify, burn_energy_requirements, burn_fluid_resuscitation_parkland, burn_glucose_glycemic_targets, burn_hypermetabolism_pharmacologic_dosing, burn_indirect_calorimetry_ree, burn_micronutrient_dosing, burn_nitrogen_balance, burn_pediatric_maintenance_fluid_holliday_segar, burn_pediatric_predictive_equations_comparison, burn_protein_requirements, calculate_nutrients, calculate_recipe_nutrition, carb_containing_foods_reference, carbohydrate_content_calculator, carbohydrate_requirement_reference, cho_fat_ratio_reference, clinical_reference_ranges, compare_foods_nutrients, delete_food_log_entry, diabetes_exchange_lookup, disease_information, dose_adjustment_stepwise_reference, dri_compare_intake, dri_eer_reference_lookup, dri_electrolyte_water_reference, dri_fat_soluble_vitamin_reference, dri_life_stages, dri_lookup, dri_macronutrient_reference, dri_mineral_trace_element_reference, dri_water_soluble_vitamin_reference, electrolyte_replacement_reference, energy_factors_reference, energy_requirements, enteral_formula_lookup, fat_requirement_reference, fenton_preterm_chart, fenton_preterm_classify, fenton_preterm_growth, fenton_preterm_profile, fenton_preterm_references, fenton_preterm_velocity, fever_stress_ree_adjustment, fhir_capability_statement, fhir_ncp_diagnosis_codes, fhir_ncp_operation, fhir_questionnaire, find_food, find_food_substitutes, food_convert, food_energy_calculate, foods_autocomplete, foods_by_category, foods_categories, foods_local_categories, generate_nutrition_label, get_food_details, get_food_log_summary, get_food_serving_sizes, glim_etiology_classification, glim_malnutrition_diagnosis, glim_malnutrition_severity, glycaemic_index_reference, growth_assess, handy_carb_measures_reference, harris_benedict_bee_calculator, ibw_ubw_nutritional_risk_interpreter, icu_fluid_requirement_calculator, indication_based_energy_protein_reference, ingest_document, insulin_types_reference, intergrowth_preterm_postnatal_growth_zscore, iom_dri_eer_calculator, list_drug_nutrient_interactions, list_favorites, list_food_log, list_pending_packaged_foods, list_view_history, log_food_entry, log_view, macronutrient_distribution_check, mealtime_insulin_dose_calculator, measures_catalog, measures_convert, medicine_information, memory_consolidate, memory_recall, memory_write, met_activity_energy_calculator, muac_cutoff_cmam_2017, must_screen, nacs_classify_adult, nacs_classify_children_0_59m, nacs_classify_children_5_17y, nacs_classify_pregnant_postpartum, nitrogen_balance_calculator, npe_nitrogen_ratio_calculator, nutrition_calculator, nutrition_requirement_weight_selection, nutrition_status_classification, oedema_ascites_weight_adjustment_reference, packaged_food_search, parse_ingredients_text, pediatric_activity_stress_factor_reference, pediatric_energy_requirements, pediatric_enteral_feed_advancement, pediatric_enteral_feed_plan, pediatric_fluid_requirements, pediatric_growth_velocity, pediatric_growth_velocity_assessment, pediatric_protein_requirements, percent_ideal_body_weight_calculator, percent_usual_body_weight, percent_weight_change_calculator, pnst_screen, potassium_deficit_estimator, pregnant_postpartum_integrated_screen, preterm_fluid_energy_requirements, propofol_fat_content_calculator, protein_requirement_by_stress_level, pyms_screen, rag_retrieve, remove_favorite, renal_exchange_lookup, respiratory_quotient_interpreter, retrieve_evidence, save_favorite, school_age_integrated_screen, search_baby_foods, search_drug_nutrient_interactions, search_food, search_foods_advanced, search_guidelines, sga_contributing_factor_classification, sga_physical_exam_reference, sga_rating_classification, spinal_cord_injury_ibw_adjustment_reference, stamp_screen, stature_from_demi_span, stature_from_knee_height, stature_from_ulna_length, stress_factor_reference, strongkids_screen, submit_packaged_food, submit_packaged_food_photo, tee_activity_band_estimator, twenty_four_hour_energy_adjustment_reference, under5_anthropometric_assessment, under5_integrated_screen, waist_circumference_interpretation, weight_change_significance_interpreter, weight_estimate_persons_65_and_older, weight_from_knee_height_and_mac, who_growth_zscore

Toolsets

By default a connection gets every tool. To load fewer (smaller context, less confusion for the model), add ?toolset= to the MCP URL with one or more names, comma separated:

https://mcp.chakudya.xyz/mcp?toolset=food,screening

all, or no parameter, means every tool. An unknown name returns a 400 that lists the valid ones. Each tool belongs to exactly one toolset.

Result size cap

Tool results are capped at 30,000 characters of JSON (MCP_MAX_RESULT_CHARS, set under vars in wrangler.jsonc, minimum 2000). When a result is longer, the largest list in it is cut to the number of items that fit and a truncated note reports returned and total, so the model knows to narrow its query. Tools that build their own response instead of calling ok() (for example the Fenton SVG chart) are not capped.

Logging and tracing

Every response has an X-Request-Id header (Cloudflare's cf-ray when present, otherwise a random id). Logs are JSON lines you can follow with npm run tail:

• request: one per request with requestId, method, path, status and ms (/health is not logged).

• tool_call: one per tool call with tool, ms, isError, requestId and sessionId. Tool arguments and results are never logged, because they can hold a user's food log or memory text.

To trace a failed call, take the X-Request-Id from the client and search the logs for it. /health and the MCP handshake report the server version from src/version.ts, which must match package.json (a test checks this).

Lookup cache

Stable lookups are cached in memory for faster repeat calls: the category list (24 hours) and single foods by id, including ?with_servings and labels (MCP_CACHE_TTL_SECONDS, default 3600, set under vars in wrangler.jsonc; 0 turns caching off). Searches, packaged-food lookups, logs, memory, RAG, POSTs and admin calls are never cached, and neither are errors. The cache lives in each Worker isolate, so it is best effort: a cold isolate starts empty, and an update to a food in chakudya-api can take up to the TTL to show.

Setup

npm install

Local dev

cp .dev.vars.example .dev.vars   # fill in a dev MCP_AUTH_TOKEN
npm run dev

Deploy

npx wrangler secret put MCP_AUTH_TOKEN
# required: a chakudya-api consumer key (chakudya-api refuses every data route without a key)
npx wrangler secret put CHAKUDYA_API_KEY
# optional, only if you use admin-gated CNR routes:
npx wrangler secret put CHAKUDYA_ADMIN_API_KEY

npm run deploy

Token in the URL is off by default. The old ?token= fallback is disabled because URLs leak into logs and history. Use Authorization: Bearer <token> or OAuth. If a client can do neither, set MCP_ALLOW_QUERY_TOKEN to "true" under vars in wrangler.jsonc.

Non-secret config (CHAKUDYA_API_BASE_URL, MCP_ALLOWED_ORIGINS, MCP_RATE_LIMIT_PER_MIN) is set in wrangler.jsonc under vars — edit that file directly rather than using wrangler secret put for those.

Malnutrition screening tools (by population)

Each population has one orchestration tool that takes the person's details once, classifies deterministically, and returns a recommended action. The AI layer calling them must treat the output as authoritative.

Supporting tools: must_screen (BAPEN MUST score), nacs_classify_*, who_growth_zscore. For BMI-for-age on its own (5y 1m – 19y 0m) call the Chakudya API's GET /bmi-for-age/classify directly.

BMI-for-age and the Chakudya API. school_age_integrated_screen calls GET /bmi-for-age/classify on the Chakudya API (WHO 2007 table as printed in Malawi MoH Eat Well to Live Well, 2021, Annex 2) through the same CHAKUDYA_API binding / base URL as the other tools. If that call fails, the screening tool falls back to the in-process WHO 2007 LMS calculation and says so in limitations; if the two ever disagree it uses the API result and raises a bmi_for_age_source_disagreement flag.

Height that can't be measured standing. adult_integrated_screen accepts ulna_length_cm (18.5–32.0 cm) or knee_height_cm + race when height_cm is missing, and estimates height with the same functions as stature_from_ulna_length / stature_from_knee_height (ulna is tried first). A measured height always wins. The output labels the height as estimated (measurements.height_source, a bmi_from_estimated_height flag, limitations), shows the BMI range implied by the knee-height equation's published error, and flags bmi_classification_uncertain when that range crosses a NACS cut-off. One ulna-table cell (men 65+, 30.0 cm = 1.71 m) breaks the source table's own sequence, so it is refused rather than used. Weight is never estimated: the published equations' standard errors (about 4–14 kg) are too coarse to base a BMI on.

Weight that can't be measured. With estimate_weight_if_missing: true (an explicit opt-in — never done implicitly) and no weight_kg, adult_integrated_screen estimates weight from muac_mm plus calf_circumference_cm (65+, optional subscapular_skinfold_mm and knee_height_cm) and/or knee_height_cm + race (ages up to 80), using the same functions as weight_estimate_persons_65_and_older and weight_from_knee_height_and_mac; the lowest standard error wins and the others are listed in measurements.weight_estimate.alternatives. The standard errors are large (about 4–5 kg for the 65+ set, 7–14.5 kg for the race-specific set), so the output labels the weight as estimated (weight_source, weight_error_kg, a bmi_from_estimated_weight flag, limitations), gives bmi_range_from_estimate_error (which also folds in a knee-height stature error), and raises bmi_classification_uncertain when that range crosses a NACS BMI cut-off. It affects BMI and MUST only — never MUAC, oedema or weight-loss findings. It is not available in school_age_integrated_screen.

Age boundaries follow NACS: under-5 tool is 0–59 months, school-age is 60 months to under 18 years, adult is 18+. Not assessed for 5–19 years: height-for-age and weight-for-age (no reference loaded here).

Paediatric assessment tools (BND 415)

Beyond the reference look-ups already in pediatricTools.ts (fluid, energy, protein, growth velocity/enteral-feed tables, macronutrient ranges — all sourced from the BND 415 Clinical Nutrition: Paediatric Medicine Resources sheet), pediatricAssessmentTools.ts adds three calculators that turn those tables into numbers for a specific child:

These are pure calculators (no Chakudya API calls) registered alongside the existing pediatric tools in createServer.ts.

INTERGROWTH-21st preterm postnatal growth (Fenton alternative)

intergrowthPretermGrowthTools.ts adds intergrowth_preterm_postnatal_growth_zscore — weight/length/head-circumference z-score and percentile by postmenstrual age (27-64 exact weeks), for tracking a preterm infant's growth after birth.

This covers the same clinical purpose as the Fenton 2013 chart, but unlike Fenton, INTERGROWTH-21st's z-score tables (Villar et al, Lancet Glob Health 2015;3:e681-91) are published openly by the INTERGROWTH-21st Network — no request-to-author needed. The 6 tables in src/data/intergrowth/*.json (weight/length/head-circumference × boys/girls, 38 rows each) are transcribed verbatim from the official PDFs, not estimated from a chart image.

If/when the equivalent Fenton LMS data comes back from Dr. Fenton, add a separate fentonGrowthTools.ts alongside this one rather than merging them — keep the two standards distinguishable in tool output since they're not numerically identical.

Session memory

memory_write, memory_recall and memory_consolidate are registered tools. Memory is stored in chakudya-api (Supabase assistant_memory), keyed by the MCP session id; memory_consolidate needs CHAKUDYA_ADMIN_API_KEY.

Connecting an MCP client

Same as the Render deployment: point the client at https://mcp.chakudya.xyz/mcp with Authorization: Bearer <MCP_AUTH_TOKEN>, or use the OAuth flow (/.well-known/oauth-authorization-server) for clients like Claude.ai's custom connector UI that don't support raw bearer tokens. The Worker's <your-worker-subdomain>.workers.dev/mcp address keeps working too.

Health check

GET /health → {"status":"ok","service":"chakudya-mcp-server-cloudflare",...}