Format/lint JS, Python, HubL for automation platforms. Tools: format_code, format_json, ask_codefmt
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Average 4.3/5 across 3 of 3 tools scored.
Each tool has a clearly distinct purpose: ask_codefmt answers questions about the tool itself, format_code formats and lints code in specific languages/platforms, and format_json handles JSON formatting. There is no overlap between these three tools.
All tool names follow a consistent verb_target pattern using lowercase and underscores (ask_codefmt, format_code, format_json). The verbs are descriptive and the naming style is uniform throughout.
Three tools is an appropriate, well-scoped count for a formatting-focused server. Each tool covers a distinct, necessary operation without redundancy or bloat.
The tool set covers the core functionality: asking questions about the server, formatting code with lint diagnostics (in supported languages/platforms), and formatting JSON. For the stated domain, there are no obvious missing operations.
ask_codefmtAsk codefmtAAsk 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; off-topic questions are refused.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The natural-language question about codefmt. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation readOnlyHint=true already signals no mutation, and the description adds valuable context: returns grounded answers with cited source URLs and refuses off-topic questions. This fully covers 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler: purpose, scope, behavior, and constraints are all efficiently presented. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no output schema, the description adequately covers purpose, input, and output (grounded answer with URLs). It doesn't detail answer format or length limits, but is sufficient for this complexity level.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the schema already describes the query parameter. The description adds context about the scope (codefmt, languages, platforms) and return behavior, enhancing understanding beyond the schema's brief note.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: asking natural-language questions about codefmt (formatting and linting JS, Python, HubL for automation platforms). It distinguishes itself from siblings by focusing on Q&A rather than direct formatting.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates when to use it (questions about codefmt) and that off-topic questions are refused, but doesn't explicitly contrast with the sibling formatting tools. The guidance is clear enough for an agent to infer appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
format_codeFormat code for automation platformsAInspectFormat and lint JavaScript, Python, or HubL code for Zapier, n8n, Pipedream, Make, or HubSpot. Wraps the source in each platform's runtime shell before formatting so top-level await, bare return, and injected globals don't break the formatter. Returns formatted code plus lint diagnostics.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | Source code to format (≤50KB). | |
| language | No | Defaults to javascript. zapier, n8n, pipedream, make, and hubspot support python; hubl does not. | |
| platform | Yes | Target automation platform. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully carries the transparency burden. It clearly explains the critical wrapping behavior (top-level await, bare return, injected globals) that is not obvious from parameter names alone. This is excellent disclosure for a formatting tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences long and front-loads the purpose. Every sentence adds unique value, though the second and third sentences could be slightly more concise without losing meaning. No waste, but minor redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given three parameters, full schema coverage, and no output schema, the description explains the input requirements well and provides essential behavioral context about wrapping. It lacks details about return format or error handling for malformed code, but the wrapping behavior is the core complexity addressed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents all parameters. The description adds value by explaining the wrapping context and why parameters matter, but it doesn't add new semantics beyond what the schema provides for individual parameters. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses clear verbs ('format and lint'), specifies the exact supported languages and platforms, and distinguishes itself by detailing the wrapping behavior in platform-specific shells. This purpose is unique and well-defined, especially compared to siblings like ask_codefmt and format_json.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (for automation platform code) and the specific platforms/languages. It does not explicitly state when not to use it or mention alternatives, but the sibling context and specific constraints provide adequate guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
format_jsonFormat a JSON documentAFormat (pretty-print or minify) a JSON document, optionally sorting object keys. Number literals are preserved exactly (64-bit IDs safe); parse errors report the exact line and column.
| Name | Required | Description | Default |
|---|---|---|---|
| json | Yes | The JSON text to format. | |
| indent | No | Indentation: '2' or '4' spaces, 'tab', or 'minify'. Defaults to '2'. | |
| sort_keys | No | Sort object keys alphabetically. Defaults to false. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true (safe read operation). The description adds valuable behavioral details beyond annotations: number literals are preserved exactly (64-bit safe) and parse errors report exact line and column. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two concise sentences that front-load the core action (format JSON), then add key behavioral traits. Every sentence is purposeful with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 3-parameter tool with no output schema, the description covers the main functionality, error behavior (parse errors), and a notable edge case (64-bit number safety). Sibling tools exist but do not demand more detail. The description is fully adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline 3 is appropriate. The description adds minimal parameter insight beyond the schema—it mentions 'optionally sorting object keys' (sort_keys) and 'pretty-print or minify' (indent), which are already documented in the schema. No additional semantic depth.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it formats a JSON document (pretty-print or minify), with optional sorting of keys. This verb+resource combination is specific to JSON, distinguishing it from sibling tools ask_codefmt and format_code, which likely handle other code formats.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for JSON formatting, and siblings likely cover other languages/formats, but it does not explicitly state when to use this versus ask_codefmt or format_code. No when-not or alternative guidance is provided, leaving the agent to infer usage.
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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