chartlink-mcp

Make a chart self-updating: attach a source URL, fetched on schedule

set_data_source

Attach a public CSV or JSON URL to a chart as its data source. The chart fetches it on a schedule (hourly, daily, or weekly), replaces rows, and republishes automatically.

Instructions

The chart refetches this URL (CSV or JSON) hourly/daily/weekly, replaces its rows, and republishes itself if published — refreshes are free, updates are never metered. Columns stay as defined on the asset: CSV headers / JSON object keys match column ids or labels case-insensitively, with spaces, underscores and hyphens interchangeable (births_per_woman matches a column labeled 'Births per woman'). Fetches once immediately and returns that outcome — check fetch.ok and fix fetch.error before moving on. get_asset shows dataSource + dataSourceState when a source is attached. Clear with clear_data_source.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "id": "<id>",
      "url": "<url>"
    },
    "name": "set_data_source"
  }
}

Input Schema

Name	Required	Description
`id`	Yes
`url`	Yes	Public http(s) URL serving CSV, a JSON array of objects, or {rows:[[...]]}
`format`	No
`refresh`	No	Default daily

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 18, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations are minimal (only openWorldHint false), so the description carries the full burden of behavioral disclosure. It discloses that fetches are immediate, that the tool returns an outcome to check (fetch.ok and fetch.error), that data is replaced and republished if published, and that refreshes are free. It also details column-matching rules (case-insensitive, space/underscore/hyphen interchangeable), which is critical for correct usage. No contradiction with annotations exists.

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 well-structured: it opens with the core action and schedule, then explains data mapping rules, then immediate fetch behavior and error handling, and finally related-tool pointers. Every sentence adds value, and the main behavior is front-loaded. It is appropriately sized for the tool's complexity without unnecessary fluff.

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?

The description covers the essential aspects for an agent: what the tool does, the matching rules, immediate fetch and error handling, and how to verify the attachment (get_asset). It does not explicitly describe the full response structure (only mentions fetch.ok and fetch.error), and it does not discuss edge cases like invalid URLs or what happens when the chart is unpublished. Given the lack of an output schema, slightly more detail on the immediate return would improve completeness, but it is largely sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers only 50% of parameters (url and refresh have descriptions; id and format lack them). The description adds significant meaning for url (explains CSV/JSON formats and column-matching) and refresh (implies hourly/daily/weekly), and indirectly clarifies format via 'CSV or JSON'. However, it does not explicitly explain the id parameter or the exact syntax of format. The description compensates well for the coverage gap but leaves a couple of parameters under-explained.

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's purpose: making a chart self-updating by attaching a source URL that is fetched on a schedule. It explicitly names the resource (data source) and the action (set), and differentiates from siblings like clear_data_source and fetch_data_source. The core behavior is front-loaded, leaving no ambiguity about what the tool does.

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 gives clear context on when to use this tool (to attach a data source for self-updating) and mentions related tools for clearing (clear_data_source) and inspecting (get_asset). However, it does not explicitly state when to prefer alternatives like fetch_data_source (for a one-time fetch without attaching) or when not to use this tool. The usage guidance is implicit rather than explicit, but the context provided is strong enough for an agent to decide appropriately in most cases.

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