codefmt

Ask codefmt

ask_codefmt

Read-only

Ask a natural-language question about codefmt: formatting and linting JavaScript, Python, and HubL for automation platforms (Zapier, n8n, Pipedream, Make, HubSpot). Returns a grounded answer drawn from codefmt's own content with cited source URLs and a confidence score (0-1, how much of the question the answer covers, weighted toward its rarer terms); off-topic or too-weakly-matched questions are refused rather than guessed.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`query`	Yes	The natural-language question about codefmt.

Output Schema

TableJSON Schema

Name	Required	Description
`answer`	Yes	The grounded answer, or the refusal text.
`results`	Yes	Ranked matching entries.
`sources`	Yes	Cited source URLs.
`confidence`	No	How much of the question the answer covers, weighted toward its rarer terms.

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedOct 2, 2026

addedOutput schema / properties / results / items / properties

Added value: +{
+  "label": {
+    "type": "string"
+  },
+  "score": {
+    "type": "number"
+  },
+  "source": {
+    "type": "string"
+  }
+}

addedOutput schema / properties / results / items / required
```
Added value: +[
+  "label",
+  "source",
+  "score"
+]
```

Changed1 schema field changedSep 28, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "answer": {
+      "description": "The grounded answer, or the refusal text.",
+      "type": "string"
+    },
+    "confidence": {
+      "description": "How much of the question the answer covers, weighted toward its rarer terms.",
+      "maximum": 1,
+      "minimum": 0,
+      "type": "number"
+    },
+    "results": {
+      "description": "Ranked matching entries.",
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "sources": {
+      "description": "Cited source URLs.",
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "answer",
+    "sources",
+    "results"
+  ],
+  "type": "object"
+}

First observedAug 18, 2026

TDQS

A4.1/5.0

Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses return behavior (grounded answer with cited source URLs), the meaning and scale of the confidence score (0-1, weighting toward rarer terms), and the refusal policy for weak matches. That is exactly the behavioral context an agent needs and cannot get from the annotation or schema.

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?

It is a single dense paragraph with the core purpose front-loaded, followed by domain scope, return behavior, and refusal policy. Every sentence carries information, though the heavy parenthetical listing makes it slightly more crowded than necessary.

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?

For a one-parameter Q&A tool, the description covers domain, expected input, return shape, confidence semantics, and failure behavior. Even though an output schema exists (so return values needn't be explained), the added explanation of the confidence score is genuinely helpful and nothing material 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?

There is a single required parameter with 100% schema description coverage, so the schema already documents "query" fully. The description reinforces that the query must be natural-language and about codefmt, but adds no format, length, or syntax detail beyond the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ("Ask a natural-language question about codefmt") and scopes the domain precisely (formatting/linting JS, Python, HubL for Zapier, n8n, Pipedream, Make, HubSpot). It is clearly distinct from format_code and format_json by the verb alone, but it never names or contrasts those siblings explicitly.

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?

It gives clear context for when the tool applies (NL questions about codefmt's subject matter) and a when-not rule ("off-topic or too-weakly-matched questions are refused rather than guessed"). It does not, however, tell the agent when to prefer this over the sibling formatting tools.

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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