pinterest-vision-mcp

by Kreminskaya

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a distinct purpose: search retrieves pins, download saves images, analyze extracts visual tags, ingest stores results, pipeline runs the full workflow, and visual_search queries stored analyses. No overlap in functionality.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern: 'pinterest_search', 'pinterest_download', 'pinterest_analyze', 'pinterest_ingest', 'pinterest_pipeline', and 'visual_search'. No mixing of conventions.
Tool Count5/5
With 6 tools, the server is well-scoped for its purpose of Pinterest-based visual analysis and retrieval. Each tool covers a necessary step in the pipeline without redundancy.
Completeness4/5
The tool set covers the full workflow from search to semantic querying. However, there is no tool for deleting or updating stored analyses, which could be considered a minor gap.
Average 4/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose behavioral traits. It explains it uses LLM vision and returns tags, but omits important details like error behavior upon invalid paths, rate limits, or whether it is read-only. This is insufficient for full transparency.
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 concise, front-loads the purpose, lists tags and args in a structured manner. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While it covers core functionality and parameters, it lacks usage guidelines and behavioral transparency. Given an output schema exists, return value details are not needed, but the description could be more complete regarding error scenarios and prerequisites.
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?
Schema coverage is 0%, so the description compensates by explaining image_paths as 'local file paths' and model as an optional override with default from env. This adds meaningful context beyond the bare schema.
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 it analyzes images with LLM vision and returns structured visual tags, listing all tags. This distinguishes it from sibling tools like pinterest_download or visual_search, as it focuses on analytical output.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for image analysis but does not explicitly contrast with alternatives like visual_search or state when not to use. The context is clear but lacks exclusions or explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description lacks behavioral details such as whether the tool is read-only, authentication requirements, or rate limits. It only describes what it does without disclosing potential side effects or constraints.
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 concise with three sentences plus a list of args, front-loaded with purpose, and every sentence adds value. No redundant or wasted words.
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?
Given the tool has an output schema (not shown), the description adequately covers purpose and parameters. It does not mention prerequisites or edge cases, but for a search tool with a clear return type, it is reasonably complete.
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?
With 0% schema description coverage, the description compensates by providing meaningful examples and purpose for each parameter (e.g., 'query: e.g. dark editorial masculine streetwear close-up', segment options). This adds significant value beyond the bare schema.
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 performs 'Semantic search across stored visual references' and 'Find past analyses by style, mood, segment, or free-text description', which is a specific verb+resource. It distinguishes from sibling Pinterest tools by not being Pinterest-specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for semantic visual searches but does not explicitly state when to use this tool versus alternatives like pinterest_search. There is no mention of when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It clearly states the tool returns a list of pins with image URLs and metadata, and documents the default limit. It does not explicitly state read-only behavior, but for a search tool, the description is sufficiently transparent about inputs and outputs.
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 extremely concise: three short sentences covering purpose, return value, and parameters. Every sentence adds value with no redundancy or verbosity.
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?
Given the tool's simplicity (2 parameters, output schema exists), the description covers purpose, parameters, and basic return format. It is complete enough for an agent to use correctly, though a note about output schema usage or read-only nature would elevate it further.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description compensates fully. It provides an example for 'query' and explains 'limit' as max pins to return with a default, adding meaningful context beyond the schema's type definitions.
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 'Search Pinterest for visual references' which provides a specific verb and resource. It implicitly distinguishes from sibling tools like pinterest_download or pinterest_analyze, but does not explicitly differentiate from visual_search, so clarity is high but not maximal.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives or when not to use it. The siblings are listed in context but not referenced in the description, leaving the agent without selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses a key side effect: on first run, ChromaDB downloads a ~90 MB embedding model. However, it omits other behaviors like whether storage is additive or destructive, idempotency, or network requirements. The disclosed behavior is valuable but not exhaustive.
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 extremely concise: three sentences total, with the core purpose in the first sentence. The note about the model download is placed after the purpose but is critical. The bullet-style args are clear and avoid fluff. Every sentence is justified.
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?
Given the tool's simplicity (2 params, no nested objects) and the existence of an output schema (described as 'has output schema: true'), the description covers the main functional aspects: what it does, what it takes, and a key behavioral note. It lacks details on storage semantics (e.g., deduplication, overwrite behavior) but is largely sufficient for an agent to use it correctly.
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 0%, so the description must compensate. It explains 'analyses' as output from pinterest_analyze and 'query' as optional label. This adds meaning beyond the schema (which shows only types and required status) but lacks detail on the expected structure of analyses (e.g., exact fields) or query format.
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 stores visual analyses in ChromaDB for future semantic retrieval, distinguishing it from sibling tools like pinterest_analyze (analysis) and visual_search (search). The verb 'store' is specific, and the resource 'ChromaDB vector base' is well-defined, with input tied to pinterest_analyze output.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage after pinterest_analyze (by specifying 'output list from pinterest_analyze') but does not explicitly state when to use or not use this tool versus alternatives. There is no guidance on prerequisites or when to skip this step, leaving the agent to infer context from sibling tool names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclosure behavioral traits. It mentions that ChromaDB will download an embedding model on first run with ingest=True. However, it does not disclose other potential side effects like API rate limits, cost, or data persistence details.
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 concise and well-structured: a single line for the purpose, a note about model download, then a bullet-like list of arguments. Every sentence adds value with no redundancy.
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 tool has 5 parameters (1 required) and an output schema (not shown). The description covers the pipeline steps and parameters adequately. It could mention error conditions or prerequisites (e.g., API key), but overall it is fairly complete.
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?
Schema description coverage is 0%, so the description provides necessary parameter explanations. It describes each parameter's purpose (e.g., 'search query', 'max pins to search') and mentions defaults, adding significant value beyond the schema.
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 it is a 'full visual intelligence pipeline' consisting of search, download, analyze, and store steps. This distinguishes it from sibling tools like pinterest_search, pinterest_download, pinterest_analyze, and pinterest_ingest, which are individual steps.
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 explains the tool's purpose and provides parameter details. It notes a side effect (model download on first run with ingest=True). However, it does not explicitly state when to use this tool versus alternatives or provide exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It details the save directory and default max_images, but omits potential behaviors like overwrite policy, rate limits, or handling of missing images.
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 concise, with three clear sentences listing purpose, save path, and parameter descriptions. No redundant information.
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 output schema exists (not shown), the description need not explain return values. It adequately covers both parameters, save location, and default behavior for a focused download tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description compensates fully. It explains search_result as the output from pinterest_search and max_images with its default and purpose, adding meaning beyond the empty schema.
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 downloads images from pinterest_search results to a local filesystem, specifying the save path. This distinguishes it from sibling tools like pinterest_search, pinterest_analyze, etc.
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 implies usage after obtaining a pinterest_search result, as it explicitly takes search_result as input. No explicit when-not-to-use or alternatives, but the sibling tools cover other operations, making the use case clear.
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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