Which integrations are available for this server?

Allows using Amazon Bedrock as the LLM backend for generating CanvasXpress JSON configs from natural language descriptions. Allows using locally hosted Ollama models as the LLM backend for generating CanvasXpress JSON configs from natural language descriptions. Allows using OpenAI-compatible APIs (including corporate gateways) as the LLM backend for generating CanvasXpress JSON configs from natural language descriptions.

How do I use CanvasXpress MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@CanvasXpress MCP Server Create a violin plot of gene expression by cell type using Tableau colors" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

CanvasXpress MCP Server

by neuhausi

Overview Schema Related Servers Score Discussions

Python

Hybrid

CanvasXpress MCP Server

Natural language → CanvasXpress JSON configs, served over HTTP on port 8100.

Describe a chart in plain English. Get back a ready-to-use CanvasXpress JSON config object ready to pass directly to new CanvasXpress(). No CanvasXpress expertise required.

"Clustered heatmap with RdBu colors and dendrograms on both axes"
"Volcano plot with log2 fold change on x-axis and -log10 p-value on y-axis"
"Violin plot of gene expression by cell type, Tableau colors"
"Survival curve for two treatment groups"
"PCA scatter plot colored by Treatment with regression ellipses"

Supports four LLM backends: Anthropic API, Amazon Bedrock, Ollama (local), and OpenAI-compatible APIs including corporate gateways.

How it works

Your description is matched against few-shot examples using semantic vector search (sqlite-vec)
The top 6 most relevant examples are included as context (RAG)
A tiered system prompt is assembled from the canvasxpress-LLM knowledge base — only the content relevant to your request is included
The configured LLM generates a validated CanvasXpress JSON config
Hallucinated parameter names are stripped against the known schema
If headers/data are provided, all column references are validated against them
The config is returned ready to pass to new CanvasXpress()

Project structure

canvasxpress-mcp/
│
├── src/
│   ├── server.py           — FastMCP HTTP server (main entry point)
│   ├── llm_providers.py    — Unified LLM backend (Anthropic, Bedrock, Ollama, OpenAI)
│   ├── cx_knowledge.py     — Parameter knowledge skill (fetch, parse, validate, inject)
│   ├── cx_survival.py      — Kaplan-Meier skill (generate, detect columns, validate, annotate)
│   └── cx_selector.py      — Chart type selection skill (deterministic, no LLM)
│
├── data/
│   ├── few_shot_examples.json  — RAG examples (add more to improve accuracy)
│   └── embeddings.db           — sqlite-vec vector index (built by build_index.py)
│
├── build_index.py          — builds the vector index from few_shot_examples.json
│
├── test_client.py          — Python test client
├── test_client.pl          — Perl test client
├── test_client.mjs         — Node.js test client (Node 18+)
│
├── USAGE.md                — usage guide (production, SSH tunnel, local)
├── requirements.txt
└── README.md

Setup

1. Python environment

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

2. Build the vector index (one-time)

python build_index.py

Re-run whenever you add or change data/few_shot_examples.json. If you skip this step the server still works — it falls back to text-similarity matching and logs a warning.

3. Configure your LLM provider

# Quickstart — Anthropic (default)
export ANTHROPIC_API_KEY="sk-ant-..."

See the LLM providers section for all four backends.

4. Start the server

python src/server.py

Server starts at http://localhost:8100. The MCP protocol endpoint is at /mcp. REST endpoints are at /generate, /modify, /km, etc.

Debug mode — full reasoning trace per request:

CX_DEBUG=1 python src/server.py

Run in background (production):

nohup python src/server.py > /tmp/cx-server.log 2>&1 &

REST endpoints

All endpoints accept GET (query parameters) or POST (JSON body). Endpoints that interact with the LLM also support JSONP via a callback= parameter for direct integration with CanvasXpress's askLLM() function.

Endpoint	Tool	Required params
`GET /generate`	Generate a new config	`description`
`GET /modify`	Modify an existing config	`config`, `instruction`
`GET /km`	Kaplan-Meier config	at least one of: `description`, `headers`, `data`, `config`
`GET /params`	Query parameter schema	none (optional: `graph_type`, `param_name`, `refresh`)
`GET /axes`	Axis assignment rules	`graph_type`
`GET /select`	Recommend a chart type	`intent`, `column_types`
`GET /explain`	Explain a config property	`property`
`GET /explain-r`	CanvasXpress in R	none (optional: `topic`)
`GET /explain-ggplot`	CanvasXpress ggplot2 bridge	none (optional: `topic`)
`GET /minimal-params`	Minimal required parameters	`graph_type`
`GET /map`	Map visualization config	`map_id`
`GET /map`	Map visualization config	`map_id`
`POST /feedback`	Rate a tool call thumbs up/down	`request_id`, `rating`
`GET /feedback/export`	Export call log	— (optional: `tool`, `rated_only`, `limit`)
`POST /feedback/purge`	Delete call log rows (admin key required)	— (optional: `tool`, `rated_only`)
`GET /ui`	Browser form UI	—

Common parameters (all LLM endpoints)

Parameter	Description
`description`	Plain English chart description. Alias: `prompt`, `q`
`headers`	Comma-separated column names: `Gene, Expression, Treatment`
`column_types`	Column types: `Gene=string, Expression=numeric, Treatment=factor`
`data`	JSON array of arrays (first row = headers): `[["Gene","Expr"],["BRCA1",1.2]]`
`temperature`	LLM creativity 0.0–1.0 (default 0.0 = deterministic)
`callback`	JSONP callback name — set automatically by CanvasXpress
`target`	CanvasXpress chart target ID — passed through to JSONP response
`client_id`	CanvasXpress client ID — passed through to JSONP response

Examples

# Generate a config
curl -s "http://localhost:8100/generate?description=Violin+plot+of+expression+by+treatment\
&headers=Expression,Treatment&column_types=Expression=numeric,Treatment=factor"

# Modify an existing config
curl -s "http://localhost:8100/modify?\
config=%7B%22graphType%22%3A%22Heatmap%22%7D\
&instruction=change+colorScheme+to+Spectral+and+add+a+title"

# Kaplan-Meier config from headers
curl -s "http://localhost:8100/km?\
description=OS+curve+by+treatment+arm\
&headers=PatientID,OS_Time,OS_Status,Treatment"

# Query all parameters for a graph type
curl -s "http://localhost:8100/params?graph_type=Heatmap"

# Look up a single parameter
curl -s "http://localhost:8100/params?param_name=colorScheme"

# Axis assignment rules for a chart type
curl -s "http://localhost:8100/axes?graph_type=Scatter2D"

# Recommend a chart type
curl -s "http://localhost:8100/select?\
intent=show+expression+distribution+by+cell+type\
&column_types=Expression=numeric,CellType=factor"

# Explain a config property
curl -s "http://localhost:8100/explain?property=groupingFactors"

# Minimal required parameters

# Map config — world choropleth
curl -s "http://localhost:8100/map?map_id=World&color_scheme=Blues&title=World+GDP"

# Map config — US states pie chart
curl -s "http://localhost:8100/map?map_id=USAStates\
&color_by=Winner&size_by=Total\
&topo_json=https://www.canvasxpress.org/data/json/usa-albers-states.json"
curl -s "http://localhost:8100/minimal-params?graph_type=KaplanMeier"

# Map config — world choropleth
curl -s "http://localhost:8100/map?map_id=World&color_scheme=Blues&title=World+GDP"

# Map config — US states pie chart
curl -s "http://localhost:8100/map?map_id=USAStates\
&color_by=Winner&size_by=Total\
&topo_json=https://www.canvasxpress.org/data/json/usa-albers-states.json"

CanvasXpress integration (JSONP)

Set llmServiceURL in your CanvasXpress config. CanvasXpress will append generate and add all required JSONP parameters automatically:

Production (canvasxpress.org):

cx.llmServiceURL = "https://www.canvasxpress.org/";

Local development via SSH tunnel:

# Run this once in a terminal and leave it open
ssh -L 8100:127.0.0.1:8100 canvasxpress@canvasxpress.org -N

cx.llmServiceURL = "http://localhost:8100/";

Local server:

cx.llmServiceURL = "http://localhost:8100/";

Apache proxy configuration

To expose the server through Apache on a production host, add this to your VirtualHost include directory. The ProxyPass / ! line must be last.

# /etc/apache2/conf.d/userdata/ssl/2_4/canvasxpress/canvasxpress.org/mcp-proxy.conf
<Location /mcp>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/mcp
    ProxyPassReverse http://127.0.0.1:8100/mcp
</Location>
<Location /generate>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/generate
    ProxyPassReverse http://127.0.0.1:8100/generate
</Location>
<Location /modify>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/modify
    ProxyPassReverse http://127.0.0.1:8100/modify
</Location>
<Location /km>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/km
    ProxyPassReverse http://127.0.0.1:8100/km
</Location>
<Location /params>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/params
    ProxyPassReverse http://127.0.0.1:8100/params
</Location>
<Location /axes>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/axes
    ProxyPassReverse http://127.0.0.1:8100/axes
</Location>
<Location /select>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/select
    ProxyPassReverse http://127.0.0.1:8100/select
</Location>
<Location /explain-r>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/explain-r
    ProxyPassReverse http://127.0.0.1:8100/explain-r
</Location>
<Location /explain-ggplot>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/explain-ggplot
    ProxyPassReverse http://127.0.0.1:8100/explain-ggplot
</Location>
<Location /explain>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/explain
    ProxyPassReverse http://127.0.0.1:8100/explain
</Location>
<Location /minimal-params>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/minimal-params
    ProxyPassReverse http://127.0.0.1:8100/minimal-params
</Location>
<Location /map>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/map
    ProxyPassReverse http://127.0.0.1:8100/map
</Location>
<Location /feedback>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/feedback
    ProxyPassReverse http://127.0.0.1:8100/feedback
</Location>
<Location /ui>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/ui
    ProxyPassReverse http://127.0.0.1:8100/ui
</Location>
<Location /favicon.ico>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/favicon.ico
    ProxyPassReverse http://127.0.0.1:8100/favicon.ico
</Location>

Note: explain-r and explain-ggplot must appear before explain — Apache matches first-wins, so more specific paths go first. PassengerEnabled Off and ProxyPass must be inside the same <Location> block; top-level ProxyPass directives are intercepted by Passenger before they can fire.

To write the file in one shot on the production server (run as root):

cat > /etc/apache2/conf.d/userdata/ssl/2_4/canvasxpress/canvasxpress.org/mcp-proxy.conf << 'EOF'
<Location /mcp>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/mcp
    ProxyPassReverse http://127.0.0.1:8100/mcp
</Location>
<Location /generate>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/generate
    ProxyPassReverse http://127.0.0.1:8100/generate
</Location>
<Location /modify>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/modify
    ProxyPassReverse http://127.0.0.1:8100/modify
</Location>
<Location /km>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/km
    ProxyPassReverse http://127.0.0.1:8100/km
</Location>
<Location /params>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/params
    ProxyPassReverse http://127.0.0.1:8100/params
</Location>
<Location /axes>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/axes
    ProxyPassReverse http://127.0.0.1:8100/axes
</Location>
<Location /select>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/select
    ProxyPassReverse http://127.0.0.1:8100/select
</Location>
<Location /explain-r>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/explain-r
    ProxyPassReverse http://127.0.0.1:8100/explain-r
</Location>
<Location /explain-ggplot>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/explain-ggplot
    ProxyPassReverse http://127.0.0.1:8100/explain-ggplot
</Location>
<Location /explain>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/explain
    ProxyPassReverse http://127.0.0.1:8100/explain
</Location>
<Location /minimal-params>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/minimal-params
    ProxyPassReverse http://127.0.0.1:8100/minimal-params
</Location>
<Location /map>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/map
    ProxyPassReverse http://127.0.0.1:8100/map
</Location>
<Location /feedback>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/feedback
    ProxyPassReverse http://127.0.0.1:8100/feedback
</Location>
<Location /ui>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/ui
    ProxyPassReverse http://127.0.0.1:8100/ui
</Location>
<Location /favicon.ico>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/favicon.ico
    ProxyPassReverse http://127.0.0.1:8100/favicon.ico
</Location>
EOF

apachectl configtest && service httpd restart

MCP tools

The server exposes the following tools over the MCP protocol (used by AI assistants such as Claude Desktop) and as REST endpoints (used by web pages and scripts directly).

`generate_canvasxpress_config`

Generate a new CanvasXpress config from a plain English description.

Argument	Type	Required	Description
`description`	string	✅	Plain English chart description
`headers`	string[]	❌	Column names from your dataset
`data`	array[][]	❌	Full data array — first row = headers. Overrides `headers`
`column_types`	object	❌	Map of column → type (`string`/`numeric`/`factor`/`date`)
`temperature`	float	❌	LLM creativity 0–1 (default 0.0)

Response:

{
  "config":         { "graphType": "Violin", "xAxis": ["Expression"], "groupingFactors": ["Treatment"] },
  "valid":          true,
  "warnings":       [],
  "invalid_refs":   {},
  "headers_used":   ["Expression", "Treatment"],
  "types_used":     { "Expression": "numeric", "Treatment": "factor" },
  "removed_params": [],
  "success":        true,
  "datetime":       "Fri, 10 Apr 2026 19:00:00 GMT",
  "request_id":     "3fa85f64-5717-4562-b3fc-2c963f66afa6"
}

Field	Description
`config`	The CanvasXpress JSON config — pass to `new CanvasXpress()`
`valid`	`true` if all column references exist in the provided headers
`warnings`	Column reference or parameter value warnings
`removed_params`	Parameter names the LLM invented that were stripped
`success`	Same as `valid` — included for CanvasXpress `callbackLLM` compatibility
`request_id`	UUID for this call — use with `POST /feedback` to submit a rating

`modify_canvasxpress_config`

Modify an existing config using a plain English instruction.

Argument	Type	Required	Description
`config`	object	✅	The existing CanvasXpress JSON config to modify
`instruction`	string	✅	Plain English description of the change to apply
`headers`	string[]	❌	Column names for validating new column references
`data`	array[][]	❌	Full data array. Overrides `headers`
`column_types`	object	❌	Map of column → type
`temperature`	float	❌	LLM creativity 0–1 (default 0.0)

Response:

{
  "config":         { "graphType": "Heatmap", "colorScheme": "Spectral", "title": "My Heatmap", "xAxis": ["Gene"] },
  "prompt":         "change colorScheme to Spectral and add a title",
  "valid":          true,
  "warnings":       [],
  "invalid_refs":   {},
  "headers_used":   ["Gene"],
  "types_used":     { "Gene": "string" },
  "removed_params": [],
  "changes":        { "added": ["title"], "removed": [], "changed": ["colorScheme"] },
  "success":        true,
  "datetime":       "Fri, 10 Apr 2026 19:00:00 GMT",
  "tool":           "modify_canvasxpress_config",
  "request_id":     "7c9e6679-7425-40c3-bba5-0ee65d4ef6a4"
}

Field	Description
`config`	The updated CanvasXpress JSON config
`prompt`	The instruction echoed back
`changes`	Keys added, removed, and changed relative to the input config
`request_id`	UUID for this call — use with `POST /feedback` to submit a rating

Example instructions:

"add a title My Heatmap"
"change the color scheme to Tableau"
"remove the legend"
"switch to dark theme"
"add groupingFactors for the Treatment column"
"set y-axis min to 0 and max to 100"
"add a horizontal reference line at y = 1.5"

`generate_km_config`

Generate, validate, and detect columns for Kaplan-Meier survival plots. Accepts any combination of description, headers, data, and existing config.

Argument	Type	Required	Description
`description`	string	❌	Plain English KM description
`headers`	string[]	❌	Column names from your dataset
`data`	array[][]	❌	Full data array — enables column detection
`config`	object	❌	Existing KM config to validate and fix
`temperature`	float	❌	LLM creativity 0–1 (default 0.0)

At least one argument must be provided.

Response:

{
  "config": {
    "graphType": "KaplanMeier",
    "xAxis": ["OS_Time"],
    "yAxis": ["OS_Status"],
    "groupingFactors": ["Treatment"],
    "xAxisTitle": "Time (months)",
    "yAxisTitle": "Survival Probability",
    "colorScheme": "Tableau",
    "showLegend": true
  },
  "valid": true,
  "errors": [],
  "warnings": [],
  "suggestions": [],
  "column_detection": {
    "time_col":   "OS_Time",
    "event_col":  "OS_Status",
    "group_cols": ["Treatment"],
    "confidence": "high",
    "notes": []
  },
  "valid":      true,
  "success":    true,
  "datetime":   "Fri, 10 Apr 2026 19:00:00 GMT",
  "tool":       "generate_km_config",
  "request_id": "2b9e6679-9c0b-4ef8-bb6b-6bb9bd380a55"
}

`query_canvasxpress_params`

Query the CanvasXpress parameter knowledge base — fetched live from the canvasxpress-LLM GitHub repo with automatic local cache fallback.

Argument	Type	Required	Description
`graph_type`	string	❌	Chart type — returns all parameters for this type
`param_name`	string	❌	Parameter name — returns full definition and valid values
`refresh`	boolean	❌	Force re-fetch from GitHub (default `false`)

Pass either, both, or neither (returns full schema summary).

Env var	Default	Description
`CX_SCHEMA_TTL`	`3600`	Schema cache TTL in seconds
`CX_SKIP_FETCH`	`0`	Set to `1` to always use bundled schema, no GitHub fetch

Response — single parameter (param_name=colorScheme):

{
  "found":        true,
  "param":        "colorScheme",
  "description":  "Color palette applied to the chart.",
  "type":         "string",
  "valid_values": ["Blues", "Reds", "RdBu", "Tableau", "CanvasXpress", "..."],
  "graph_types":  ["Bar", "Heatmap", "Scatter2D", "Violin", "..."],
  "schema_source": "github",
  "tool":         "get_chart_parameters",
  "valid":        true,
  "datetime":     "Fri, 10 Apr 2026 19:00:00 GMT",
  "request_id":   "a0eebc99-9c0b-4ef8-bb6b-6bb9bd380a11"
}

Response — graph type (graph_type=Heatmap):

{
  "graph_type":   "Heatmap",
  "param_count":  42,
  "params": {
    "colorScheme":          { "description": "Color palette.", "type": "string", "valid_values": ["RdBu", "Blues", "..."] },
    "samplesClustered":     { "description": "Cluster columns with dendrogram.", "type": "boolean", "valid_values": [] },
    "variablesClustered":   { "description": "Cluster rows with dendrogram.", "type": "boolean", "valid_values": [] }
  },
  "schema_source": "github",
  "tool":         "get_chart_parameters",
  "valid":        true,
  "datetime":     "Fri, 10 Apr 2026 19:00:00 GMT",
  "request_id":   "b1ddbc00-1d1c-5fg9-cc7c-7cc0ce491b22"
}

`get_axes_info`

Return axis assignment rules for a given graph type: which axes are valid, which are forbidden, and which axis title parameter to use.

Argument	Type	Required	Description
`graph_type`	string	✅	CanvasXpress chart type e.g. `Bar`, `Scatter2D`, `BarLine`

Response:

{
  "graph_type":       "Scatter2D",
  "category":         "multi_dim",
  "valid_axes":       ["xAxis", "yAxis"],
  "invalid_axes":     [],
  "axis_title_param": "xAxisTitle / yAxisTitle",
  "notes":            "Scatter2D requires both xAxis (numeric) and yAxis (numeric). Use xAxisTitle and yAxisTitle for axis labels. Never use smpTitle.",
  "schema_snippet":   "xAxis — columns for x-axis; yAxis — columns for y-axis ...",
  "tool":             "suggest_axes",
  "valid":            true,
  "datetime":         "Fri, 10 Apr 2026 19:00:00 GMT",
  "request_id":       "c2eecd11-2e2d-6gh0-dd8d-8dd1df502c33"
}

Field	Description
`category`	`single_dim`, `multi_dim`, or `combined`
`valid_axes`	Axis keys that apply to this chart type
`invalid_axes`	Axis keys that must NOT be used
`axis_title_param`	Correct axis title parameter (`smpTitle` vs `xAxisTitle/yAxisTitle`)

`select_canvasxpress_chart`

Recommend the most appropriate chart type given column metadata and a plain English intent. Deterministic — no LLM call. Returns a ranked list of candidates with rationale and a ready-made description hint to pass to generate_canvasxpress_config.

Argument	Type	Required	Description
`intent`	string	✅	Plain English description of what you want to show
`column_types`	object	✅	Map of column name → type (`string`/`numeric`/`factor`/`date`)
`n_samples`	integer	❌	Optional number of rows — used to refine recommendations

Response:

{
  "intent": "show expression distribution by cell type",
  "column_summary": { "n_factor": 1, "n_numeric": 1, "n_time": 0, "n_bool": 0, "n_text": 0 },
  "top_recommendation": {
    "graphType":       "Violin",
    "score":           0.9,
    "description":     "Kernel density distribution of a numeric variable split by a categorical grouping factor.",
    "clinical_use":    "Gene expression by cell type, biomarker distribution by cohort.",
    "next_step":       "generate_canvasxpress_config with description='Violin chart of Expression grouped by CellType'",
    "scoring_factors": ["1 numeric + 1 factor — ideal for distribution by group"],
    "minimal_config":  { "graphType": "Violin", "xAxis": ["Expression"], "groupingFactors": ["CellType"] }
  },
  "alternatives": [
    {
      "graphType":      "Boxplot",
      "score":          0.78,
      "description":    "Box-and-whisker summary statistics by group.",
      "clinical_use":   "Quick distribution summary when n is sufficient.",
      "scoring_factors": ["1 numeric + 1 factor — suitable for grouped distribution"],
      "minimal_config": { "graphType": "Boxplot", "xAxis": ["Expression"], "groupingFactors": ["CellType"] }
    }
  ],
  "generate_hint": "Violin chart of Expression grouped by CellType — columns: Expression, CellType",
  "valid":          true,
  "warnings":       [],
  "type_source":    "explicit",
  "tool":           "select_canvasxpress_chart",
  "datetime":       "Fri, 10 Apr 2026 19:00:00 GMT",
  "request_id":     "d3ffde22-3f3e-7hi1-ee9e-9ee2eg613d44"
}

Field	Description
`top_recommendation`	Best chart type with score, rationale, and `minimal_config`
`alternatives`	Up to 4 other ranked candidates, each also with `minimal_config`
`generate_hint`	Ready-made description to pass to `generate_canvasxpress_config`
`minimal_config`	Minimal axis config ready to use — attach to the `generate` call
`type_source`	How column types were resolved: `explicit`, `inferred`, or `merged`

`explain_config_property`

Return a plain English explanation of any CanvasXpress configuration property.

Argument	Type	Required	Description
`property`	string	✅	Config property name e.g. `colorScheme`, `groupingFactors`, `decorations`

Response:

{
  "property":    "groupingFactors",
  "explanation": "**`groupingFactors`** — Array of column names used to group/color data. e.g. ['Treatment', 'CellType']",
  "tool":        "explain_canvasxpress_property",
  "valid":       true,
  "datetime":    "Fri, 10 Apr 2026 19:00:00 GMT",
  "request_id":  "e4ggef33-4g4f-8ij2-ff0f-0ff3fh724e55"
}

`explain_canvasxpress_r`

Usage guide for CanvasXpress in R — installation, basic usage, data formats, Shiny integration, R Markdown, and the ggplot2 bridge.

Argument	Type	Required	Description
`topic`	string	❌	Filter by topic: `installation`, `basic`, `shiny`, `rmarkdown`, `data`, `config`

Response (no topic — full guide):

{
  "overview":          "The canvasXpress R package wraps the CanvasXpress JavaScript library as an htmlwidget ...",
  "sections": {
    "installation": { "title": "Installation", "content": "Install from CRAN: install.packages('canvasXpress') ..." },
    "basic":        { "title": "Basic Usage",   "content": "The main function is canvasXpress() ..." },
    "data":         { "title": "Data Format",   "content": "CanvasXpress in R expects data in one of two orientations ..." },
    "config":       { "title": "Configuration Parameters", "content": "All CanvasXpress JSON config parameters map directly ..." },
    "shiny":        { "title": "Using CanvasXpress in Shiny", "content": "CanvasXpress integrates with Shiny via canvasXpressOutput() ..." },
    "rmarkdown":    { "title": "Using CanvasXpress in R Markdown / Quarto", "content": "..." }
  },
  "available_topics": ["installation", "basic", "data", "config", "shiny", "rmarkdown"],
  "tool":             "explain_canvasxpress_r",
  "valid":            true,
  "datetime":         "Fri, 10 Apr 2026 19:00:00 GMT",
  "request_id":       "f5hhfg44-5h5g-9jk3-gg1g-1gg4gi835f66"
}

Response (with topic=shiny):

{
  "topic":   "shiny",
  "section": { "title": "Using CanvasXpress in Shiny", "content": "CanvasXpress integrates with Shiny via canvasXpressOutput() ..." },
  "available_topics": ["installation", "basic", "data", "config", "shiny", "rmarkdown"],
  "tool":             "explain_canvasxpress_r",
  "valid":            true,
  "datetime":         "Fri, 10 Apr 2026 19:00:00 GMT",
  "request_id":       "g6iigh55-6i6h-0kl4-hh2h-2hh5hj946g77"
}

`explain_canvasxpress_ggplot`

Usage guide for the CanvasXpress ggplot2 bridge — convert any ggplot2 object to an interactive CanvasXpress widget with a single function call.

Argument	Type	Required	Description
`topic`	string	❌	Filter by topic: `installation`, `geoms`, `example`

Response (no topic — full guide):

{
  "sections": {
    "overview":      { "title": "Overview",      "content": "canvasXpress() accepts a ggplot2 object directly ..." },
    "installation":  { "title": "Installation",  "content": "install.packages('canvasXpress') ..." },
    "geoms":         { "title": "Supported Geoms", "content": "geom_point → Scatter2D, geom_bar → Bar ..." },
    "example":       { "title": "Full Example",   "content": "library(ggplot2); library(canvasXpress) ..." }
  },
  "available_topics": ["overview", "installation", "geoms", "example"],
  "tool":             "explain_ggplot_to_canvasxpress",
  "valid":            true,
  "datetime":         "Fri, 10 Apr 2026 19:00:00 GMT",
  "request_id":       "h7jjih66-7j7i-1lm5-ii3i-3ii6ik057h88"
}

`get_minimal_parameters`

Return the minimal set of required parameters for a specific chart type.

Argument	Type	Required	Description
`graph_type`	string	✅	CanvasXpress chart type e.g. `Scatter2D`, `Heatmap`

Response:

{
  "graphType":           "KaplanMeier",
  "required_parameters": ["graphType", "xAxis", "yAxis"],
  "tool":                "get_minimal_params",
  "valid":               true,
  "datetime":            "Fri, 10 Apr 2026 19:00:00 GMT",
  "request_id":          "i8kkji77-8k8j-2mn6-jj4j-4jj7jl168i99"
}

Field	Description
`graphType`	The requested chart type
`required_parameters`	Minimum set of parameters that must be populated for a valid config

`create_map_config`

Generate a CanvasXpress map (choropleth, pie, or marker) config without an LLM call. Supports world maps, continent maps, country maps, US state/county maps, and custom topoJSON maps. Optionally overlays pie charts per region, proportional sizing, and geocoded marker pins.

Argument	Type	Required	Description
`map_id`	string	✅	Map identifier — see table below
`data`	array[][]	❌	CSV-style data; first row = headers, first column = geographic IDs
`title`	string	❌	Chart title
`color_scheme`	string	❌	Color palette e.g. `Blues`, `RdBu`, `YlOrRd`
`color_by`	string	❌	Column name to color regions/symbols by
`size_by`	string	❌	Column name whose values scale symbol/pie size per region
`decorations`	object	❌	Decoration overlays — currently supports `pie` (see below)
`topo_json`	string	❌	URL to a custom topoJSON file
`legend_order`	object	❌	Map of column → ordered list of values for legend display
`markers`	array	❌	List of marker pin dicts (see below)

map_id values:

`map_id`	Coverage	First-column ID format
`World`	All countries	ISO 3-letter codes (`USA`, `FRA`, `CHN`, …)
`WorldContinents`	6 continents	Continent names (`Africa`, `Asia`, `Europe`, …)
`Africa`, `Asia`, `Europe`, `NorthAmerica`, `SouthAmerica`, `Oceania`	One continent	ISO 3-letter codes
`USAStates`	50 US states + DC	2-letter codes (`CA`, `TX`, `NY`, …)
`USACounties`	US counties	5-digit FIPS codes (`06037`, `48113`, …)
`albersStatesPie`	US states, Albers projection	2-letter codes — use when Albers projection is explicitly requested
ISO 3-letter code (`CAN`, `GBR`, `AUS`, …)	Country sub-regions	Feature property values — set `mapPropertyId` in config
2-letter US state code (`CA`, `TX`, `NY`, …)	State counties	County names or FIPS codes

Pie overlay (decorations.pie):

"decorations": {
  "pie": {
    "smps":   ["Democrat", "Republican", "Libertarian", "Other"],
    "colors": ["blue", "red", "yellow", "green"],
    "size":   2.5
  }
}

decorations.pie.size is a float multiplier that controls how large each pie is drawn. size_by (top-level) is a column name that scales each pie proportionally to its data value. These two work independently — both can be used together.

Marker pins (markers list):

Each marker dict supports three ways to specify location:

Key	Description
`lat` + `lng`	Explicit decimal-degree coordinates
`zip`	US ZIP code — resolved via free zippopotam.us API
`location`	Any city, address, or landmark — geocoded via Nominatim (OpenStreetMap)

Optional fields: label (string), color (default "red"), shape (teardrop|circle|star|square, default teardrop), size (int 1–10, default 4).

Response:

{
  "config": {
    "graphType":    "Map",
    "mapId":        "USAStates",
    "colorBy":      "Winner",
    "sizeBy":       "Total",
    "decorations":  { "pie": [{"smps": ["Democrat","Republican","Libertarian","Other"], "colors": ["blue","red","yellow","green"], "size": 2.5}] },
    "legendOrder":  { "Winner": ["Republican","Democrat"] },
    "title":        "2000 Presidential Elections"
  },
  "valid":        true,
  "warnings":     [],
  "map_id":       "USAStates",
  "headers_used": ["Id","Total","Democrat","Republican","Libertarian","Other","State","Winner"],
  "tool":         "create_map_config",
  "datetime":     "Fri, 10 Apr 2026 19:00:00 GMT",
  "request_id":   "j9lllk88-9l9k-3no7-kk5k-5kk8km279j00"
}

Example prompts for generate_canvasxpress_config:

"Show GDP by country on a world map using Blues color scheme"
"US states map colored by unemployment rate"
"Create a map of France showing population by region"
"Pie map of the 2000 US Presidential Election — Democrat, Republican, Libertarian, Other slices per state"
"World map with markers at Paris, Tokyo, New York, and Sydney"
"USA states map with markers at ZIP codes 10001, 90210, and 60601"

Call logging & feedback

Every tool call is automatically logged to data/call_log.db (a separate SQLite database from the vector index). Each response includes a request_id UUID that can be used to submit thumbs-up/down feedback.

Submit feedback

curl -X POST http://localhost:8100/feedback \
  -H "Content-Type: application/json" \
  -d '{"request_id": "<uuid from response>", "rating": 1, "comment": "Perfect"}'

Field	Type	Required	Description
`request_id`	string	✅	UUID from the tool response
`rating`	integer	✅	`1` = thumbs up, `-1` = thumbs down
`comment`	string	❌	Optional free-text note

Export call log

# All calls (latest 500)
curl "http://localhost:8100/feedback/export"

# Only rated calls for one tool
curl "http://localhost:8100/feedback/export?tool=select_canvasxpress_chart&rated_only=true"

Parameter	Description
`tool`	Filter by tool name (optional)
`rated_only`	`true` — return only rows that have a rating
`limit`	Max rows to return (default `500`)

Purge call log (admin only)

Requires the X-Admin-Key header. See ADMIN_KEY below.

# Delete all rows
curl -X POST http://localhost:8100/feedback/purge \
  -H "X-Admin-Key: your-secret-key"

# Delete only rated rows for one tool
curl -X POST http://localhost:8100/feedback/purge \
  -H "X-Admin-Key: your-secret-key" \
  -H "Content-Type: application/json" \
  -d '{"tool": "generate_canvasxpress_config", "rated_only": true}'

Body field	Description
`tool`	Delete only rows for this tool name (optional)
`rated_only`	`true` — delete only rows that have a rating (optional)

Omitting both deletes all rows. Returns {"success": true, "deleted": N}.

Security — without a correct X-Admin-Key header the endpoint returns 403 Forbidden. The comparison uses hmac.compare_digest to prevent timing-based attacks. Set ADMIN_KEY in .env for a persistent key; if not set, a random UUID is generated per restart and printed to the server log.

Manage the call log locally (CLI)

manage_call_log.py is a standalone CLI script for managing data/call_log.db directly on the server — no HTTP key required.

Note — data/call_log.db is in .gitignore and is never overwritten by git pull. The server uses CREATE TABLE IF NOT EXISTS, so the file is only ever opened and appended to, never recreated on restart.

# Summary: row counts by tool, rated/unrated, 👍/👎
python manage_call_log.py stats

# Export everything to a timestamped JSON file
python manage_call_log.py export --out backup_$(date +%Y%m%d).json

# Export only rated calls as CSV
python manage_call_log.py export --rated-only --format csv --out rated.csv

# Export only calls for one tool
python manage_call_log.py export --tool generate_canvasxpress_config

# Purge only rated calls (keeps unrated history)
python manage_call_log.py purge --rated-only

# Purge everything without a prompt (e.g. in a cron job after export)
python manage_call_log.py purge --yes

# Use a different database path
python manage_call_log.py --db /path/to/other/call_log.db stats

stats — prints a per-tool breakdown table with total, rated, 👍, 👎 counts and first-call timestamp.

export options:

Option	Description
`--tool TOOL`	Filter by tool name (partial match)
`--rated-only`	Only rows that have a rating
`--limit N`	Max rows to export (default: all)
`--format json\|csv`	Output format (default: `json`)
`--out FILE`	Write to file instead of stdout

purge options:

Option	Description
`--tool TOOL`	Delete only rows for this tool (partial match)
`--rated-only`	Delete only rows that have a rating
`--yes`	Skip the confirmation prompt

LLM providers

Anthropic (default)

export LLM_PROVIDER=anthropic          # optional — this is the default
export ANTHROPIC_API_KEY="sk-ant-..."
export LLM_MODEL=claude-sonnet-4-20250514  # optional
python src/server.py

Amazon Bedrock

pip install boto3
export LLM_PROVIDER=bedrock
export AWS_REGION=us-east-1
# Uses your existing AWS credentials (IAM role, SSO profile, or explicit keys)
python src/server.py

Ollama (local, no API key)

ollama serve
ollama pull llama3.2
export LLM_PROVIDER=ollama
export LLM_MODEL=llama3.2
python src/server.py

OpenAI / corporate gateway

pip install openai
export LLM_PROVIDER=openai
export OPENAI_API_KEY="your-key"
export OPENAI_BASE_URL="https://api.your-company.com/openai/v1"
export LLM_MODEL=gpt-4o
python src/server.py

Environment variables

Variable	Default	Description
`LLM_PROVIDER`	`anthropic`	LLM backend: `anthropic`, `bedrock`, `ollama`, `openai`
`LLM_MODEL`	provider default	Model name / ID
`ANTHROPIC_API_KEY`	—	Anthropic API key
`AWS_REGION`	`us-east-1`	AWS region for Bedrock
`OLLAMA_BASE_URL`	`http://localhost:11434`	Ollama server URL
`OPENAI_API_KEY`	—	OpenAI / gateway API key
`OPENAI_BASE_URL`	`https://api.openai.com/v1`	OpenAI-compatible endpoint
`MCP_HOST`	`0.0.0.0`	Server bind host
`MCP_PORT`	`8100`	Server port
`CORS_ORIGINS`	`*`	Comma-separated allowed origins
`ADMIN_KEY`	auto-generated	Secret key required for `POST /feedback/purge`. Auto-generated and logged if not set
`CX_DEBUG`	`0`	Set to `1` for full debug trace
`CX_SCHEMA_TTL`	`3600`	Schema cache TTL in seconds
`CX_SKIP_FETCH`	`0`	Set to `1` to skip GitHub schema fetch
`EMBEDDING_MODEL`	`all-MiniLM-L6-v2`	Sentence transformer model for vector search

All variables can also be set in a .env file in the project root.

Troubleshooting

Updating the production server from the repo

Run these steps every time you pull a new version:

cd canvasxpress-mcp/
./server.sh stop
git fetch origin
git reset --hard origin/main
git apply --whitespace=nowarn canvasxpress-ctypes-fix.patch
source .venv/bin/activate
pip install -r requirements.txt
./server.sh start

git reset --hard discards any local changes and makes the working tree match the remote exactly. If you have local config files (.env, data/call_log.db) they are untracked and will not be touched.
If src/server.py is missing after the patch step (patch did not restore it), recover it with:
git restore src/server.py

No module named 'dotenv'

pip install python-dotenv

No module named 'starlette'

pip install starlette fastmcp

Port 8100 already in use

lsof -ti :8100 | xargs kill -9   # macOS/Linux

Homepage shows "It works! Python 3.12" Remove the Passenger configuration from /home/canvasxpress/public_html/.htaccess. Delete the lines between CLOUDLINUX PASSENGER CONFIGURATION BEGIN and CLOUDLINUX PASSENGER CONFIGURATION END.

404 from the browser but curl works Check that llmServiceURL does not include the port number in the path. Correct: "https://www.canvasxpress.org/" — incorrect: "https://www.canvasxpress.org:8100/".

500 error on invalid descriptions Upgrade to the latest server.py — graceful error handling was added so invalid prompts return a 200 with valid: false and a helpful message instead of a 500.

removed_params is non-empty The LLM generated parameter names not in the CanvasXpress schema. They were automatically stripped. The config is still valid — refine the description if needed.

New tool added — Apache returns 404 for the new endpoint Every time a new tool (and its REST endpoint) is added to server.py, the Apache proxy config must be updated as root. Add a new <Location> block for the new endpoint, keeping PassengerEnabled Off and ProxyPass together inside it:

<Location /new-endpoint>
    PassengerEnabled Off
    ProxyPass http://127.0.0.1:8100/new-endpoint
    ProxyPassReverse http://127.0.0.1:8100/new-endpoint
</Location>

Then reload Apache:

apachectl configtest && service httpd restart

See the Apache proxy configuration section above for the complete current config to overwrite the file from scratch if needed.

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