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Mobbin

by com.mobbin
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Search Sections

search_sections
Read-only

Search Mobbin for website sections (e.g. About, Pricing, Footer) using natural language. Returns section images inline along with metadata. Examine the returned images to understand each section's actual content — do not describe or summarize sections based solely on metadata. Inline images are low-res previews for you to read, not for the user. Each result's image_url is the high-resolution image. Whenever the user wants to save, export, embed, or paste a result (files, Figma, Notion, docs, slides), download it from image_url instead of reusing the inline preview. Image URLs expire after 30 days, so download the file rather than linking to it; link to mobbin_url when citing. On hosts that support MCP Apps, also renders an interactive gallery of the results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number for paginating through results.
limitNoMaximum number of sections to return. Higher number of sections returned causes increased context usage.
queryYesDescribe one website section in plain language — the content and elements you'd see. Be specific; detail helps. Good: "pricing page with plan comparison table", "hero section with signup form". Avoid: combining multiple sections (search separately), negations, vague style words, disconnected keyword lists.
output_toolNoThe product the results go into next, if known, e.g. Figma, Paper, Notion. Product name only. When the results go into more than one product, name the one the user asked for first. Not the search source: Mobbin only when the results go back to Mobbin. Omit when unknown. MUST be the same across all calls for the same task.
task_intentNoOne short sentence summarizing the user's overall task. Helps return more relevant results. Write it in English even when the conversation is in another language. MUST be the same across all calls for the same task. Do NOT include verbatim user messages, conversation history, file contents, or personal data.
image_formatNoImage format. Use jpg if your client does not support webp.webp
output_destinationNoWhat you will do with the results after this call. Choose by what gets made with the results, not by the search itself. When the request does not say, go by where you are running: a coding agent in a repository means `code`, a design tool means `design_tool`, a plain chat host means `chat`. Helps return results in the form the workflow needs. `code`: implement or change UI in a codebase or coded prototype, e.g. React, Swift, HTML/CSS, v0, Lovable. `design_tool`: recreate or put results on a design canvas, e.g. Figma, Paper, Pen.dev, MagicPath. `doc`: write a report, PRD, spec, audit or slides, e.g. in Notion, Google Docs or Slides. `reference_library`: only when saving is the ask: the user wants the results kept for later in a folder, notes or a Mobbin collection. Looking things up to build, design or write from is NOT this. `chat`: only answer in the conversation; the user has not asked you to build, design, write or save anything. `other`: an unlisted destination, none of the above, e.g. posting the results to Slack or email. MUST be the same across all calls for the same task.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageYes1-based page number of this batch of search results.
queryYesThe natural language query used for this search.
sectionsYes
has_next_pageYesWhether more pages of results are available.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / output_destination
      Added value: +{
+  "description": "What you will do with the results after this call. Choose by what gets made with the results, not by the search itself. When the request does not say, go by where you are running: a coding agent in a repository means `code`, a design tool means `design_tool`, a plain chat host means `chat`. Helps return results in the form the workflow needs. `code`: implement or change UI in a codebase or coded prototype, e.g. React, Swift, HTML/CSS, v0, Lovable. `design_tool`: recreate or put results on a design canvas, e.g. Figma, Paper, Pen.dev, MagicPath. `doc`: write a report, PRD, spec, audit or slides, e.g. in Notion, Google Docs or Slides. `reference_library`: only when saving is the ask: the user wants the results kept for later in a folder, notes or a Mobbin collection. Looking things up to build, design or write from is NOT this. `chat`: only answer in the conversation; the user has not asked you to build, design, write or save anything. `other`: an unlisted destination, none of the above, e.g. posting the results to Slack or email. MUST be the same across all calls for the same task.",
+  "enum": [
+    "code",
+    "design_tool",
+    "doc",
+    "reference_library",
+    "chat",
+    "other"
+  ],
+  "type": "string"
+}
    • addedInput schema / properties / output_tool
      Added value: +{
+  "description": "The product the results go into next, if known, e.g. Figma, Paper, Notion. Product name only. When the results go into more than one product, name the one the user asked for first. Not the search source: Mobbin only when the results go back to Mobbin. Omit when unknown. MUST be the same across all calls for the same task.",
+  "type": "string"
+}
  2. Changed1 schema field changed
    • changedOutput schema / properties / sections / items / properties / image_url / description
      Previous value: -"Image URL for the section. Expires after 30 days."New value: +"High-resolution image URL (up to 1920px wide). Download from it whenever the user wants to save, export, embed, or paste the image. Expires after 30 days."
  3. Changed1 schema field changed
    • changedInput schema / properties / task_intent / description
      Previous value: -"One short sentence summarizing the user's overall task. Helps return more relevant results. MUST be the same across all calls for the same task. Do NOT include verbatim user messages, conversation history, file contents, or personal data."New value: +"One short sentence summarizing the user's overall task. Helps return more relevant results. Write it in English even when the conversation is in another language. MUST be the same across all calls for the same task. Do NOT include verbatim user messages, conversation history, file contents, or personal data."
  4. Changed1 schema field changed
    • addedInput schema / properties / task_intent
      Added value: +{
+  "description": "One short sentence summarizing the user's overall task. Helps return more relevant results. MUST be the same across all calls for the same task. Do NOT include verbatim user messages, conversation history, file contents, or personal data.",
+  "type": "string"
+}
  5. First observed

TDQS

A4.4/5.0
Behavior5/5

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

The description goes far beyond the annotations (readOnlyHint, destructiveHint) by disclosing critical behaviors: inline images are low-res previews for the agent, image_url is the high-res version to use for saving/exporting, URLs expire after 30 days so files must be downloaded, and the recommendation to link to mobbin_url when citing. It also mentions the interactive gallery on MCP Apps. This is rich behavioral context not captured in annotations.

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?

The description is relatively long but every sentence earns its place, covering purpose, result interpretation, image handling, expiration, and output behavior. It is front-loaded with the core purpose and then addresses important operational details. No filler or redundancy, though it could be trimmed slightly without losing value.

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?

Given the tool's moderate complexity, the presence of an output schema, and rich annotations, the description is highly complete. It tells the agent exactly how to interpret results, when to download from image_url, how to cite, and what to expect regarding image previews and gallery rendering. No critical information for correct invocation 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%, so the schema already documents all parameters in detail (including query formulation, output_destination choices, etc.). The tool description adds no new parameter-level semantics; it focuses on result handling and usage behavior. Since the schema carries the burden, a baseline of 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 description clearly states the tool searches for website sections via natural language, with examples ('About, Pricing, Footer'). It distinguishes from sibling tools by its specific resource type (sections vs flows/screens) and explicitly mentions returning section images. The purpose is unambiguous and actionable.

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 what the tool is for and includes query-construction guidance (e.g., avoid combining sections, search separately). However, it does not explicitly contrast with sibling tools like search_flows or search_screens, leaving the selection decision to inference from the name and purpose. No exclusions are stated, but the context is clear enough for most agents.

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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