The Coin Daily Research

Explain a selected chart range

research_interpret_range

Read-onlyIdempotent

Use this when the user asks what a specific A/B chart selection means across its enabled metrics. Do not use for a whole-market briefing, predictions, endpoint-only narration or recovering the exact previous chart read. Selection: Use actual A/B candle opening times, timeframe, captured as_of, selection.alignment=candles, all selected layers and exact study IDs/code/calculation/timeframe. Calculated study timeframe must match A/B; both endpoint candles are included. Presentation: Deterministic prominent-move findings from Research, not MIA prose or an LLM call. Explain relevance accessibly without restating every metric. Evidence: This is a fresh history read, not the frozen A/B table. Returned range_end_exclusive and evidence_read_at govern it; /metric_checks provides detail.

Input Schema

Name	Required	Description	Default
`request`	Yes	Original selected A/B candles and all enabled metrics. This requests fresh deterministic range evidence, not a recitation of endpoint differences.

Output Schema

Name	Required	Default
`facts`	No
`status`	Yes
`message`	No
`surface`	Yes
`evidence`	No
`selection`	Yes
`research_url`	Yes
`schema_version`	No	tcd.mcp-research/v2
`retry_after_seconds`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 9, 2026

addedInput schema / $defs / Study / allOf

Added value: +[
+  {
+    "if": {
+      "properties": {
+        "calculation": {
+          "const": "candles"
+        }
+      }
+    },
+    "then": {
+      "properties": {
+        "timeframe": {
+          "enum": [
+            "1h",
+            "4h",
+            "1d"
+          ],
+          "type": "string"
+        }
+      },
+      "required": [
+        "timeframe"
+      ]
+    }
+  }
+]

First observedSep 29, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Beyond the annotations (readOnly, idempotent, non-destructive), it discloses that this performs a fresh history read rather than reciting the frozen A/B table, that findings are deterministic rather than LLM prose, and names the governing fields (range_end_exclusive, evidence_read_at, /metric_checks). These are non-obvious behavioral traits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the trigger and organized under Selection/Presentation/Evidence labels, with no filler sentences. It is dense and jargon-heavy, which costs some readability, but every section carries operative content.

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

Completeness5/5

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

With an output schema present, return values need not be described, and the description still covers selection construction, presentation style, and evidence provenance. Nothing an agent needs to invoke it correctly is missing.

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% and the nested schema already documents a_time, b_time, alignment, timeframe matching, and study IDs in detail. The Selection paragraph largely restates those structured definitions rather than adding new cross-field semantics, so baseline 3 is appropriate.

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 opening line states a specific verb (interpret/explain) and resource (a selected A/B chart range across its enabled metrics), which is precise. It then distinguishes itself from siblings by excluding whole-market briefings, predictions, endpoint-only narration, and replaying the prior chart read.

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

Usage Guidelines5/5

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

The description gives an explicit trigger condition ('when the user asks what a specific A/B chart selection means') and an explicit when-not list mapped to closely related capabilities. This routes the agent away from research_briefing and evidence/recitation variants without ambiguity.

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

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