image_related"Find images similar to [X]" / "more images like this" — related images for a given Openverse image ID. Use for visual-similarity exploration in CC content.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| results | No | Array of related images |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "id": "e8c5b8d9-4f2a-4c1e-9b3a-2d5e7f1a3b6c"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "results": {
+ "description": "Array of related images",
+ "items": {
+ "properties": {
+ "category": {
+ "description": "Image category",
+ "type": "string"
+ },
+ "creator": {
+ "description": "Image creator name",
+ "type": "string"
+ },
+ "creator_url": {
+ "description": "Creator profile URL",
+ "type": "string"
+ },
+ "filesize": {
+ "description": "File size in bytes",
+ "type": "integer"
+ },
+ "filetype": {
+ "description": "File format",
+ "type": "string"
+ },
+ "foreign_landing_url": {
+ "description": "Original source URL",
+ "type": "string"
+ },
+ "height": {
+ "description": "Image height in pixels",
+ "type": "integer"
+ },
+ "id": {
+ "description": "Image unique identifier",
+ "type": "string"
+ },
+ "license": {
+ "description": "License type",
+ "type": "string"
+ },
+ "license_url": {
+ "description": "License details URL",
+ "type": "string"
+ },
+ "license_version": {
+ "description": "License version",
+ "type": "string"
+ },
+ "provider": {
+ "description": "Source provider",
+ "type": "string"
+ },
+ "source": {
+ "description": "Source identifier",
+ "type": "string"
+ },
+ "tags": {
+ "description": "Associated tags",
+ "items": {
+ "properties": {
+ "accuracy": {
+ "description": "Tag accuracy score",
+ "type": "number"
+ },
+ "name": {
+ "description": "Tag name",
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "thumbnail": {
+ "description": "Thumbnail URL",
+ "type": "string"
+ },
+ "title": {
+ "description": "Image title",
+ "type": "string"
+ },
+ "url": {
+ "description": "Image URL",
+ "type": "string"
+ },
+ "width": {
+ "description": "Image width in pixels",
+ "type": "integer"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds that it finds 'related images for a given Openverse image ID,' which is consistent with annotations. It could optionally mention result variability or limitations, but overall no contradictions and adequate transparency.
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 extremely concise, using a common user phrasing pattern ('Find images similar to [X]') and a single additional sentence for context. Every word adds value, with no 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 the tool's simplicity (1 parameter, output schema present), the description covers purpose, usage context, and input semantics completely. The agent has enough information to select and invoke this tool correctly.
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?
The single parameter 'id' has no description in the schema (0% coverage), but the description clarifies it is an 'Openverse image ID,' adding crucial meaning beyond the type. This effectively tells the agent what value to provide.
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 verb ('find'), the resource ('images similar to [X]'), and the specific input ('Openverse image ID'). It distinguishes itself from sibling tools like 'search_images' by focusing on visual-similarity exploration rather than keyword search.
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 explicitly mentions 'Use for visual-similarity exploration in CC content,' providing clear context for when to use it. It implies that this tool is for finding similar images given an ID, contrasting with search-based siblings, but does not explicitly state 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.
Add one secure layer between your agents and this server.
Several tools occupy the same functional space: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer factual questions from Pipeworx data, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The prediction-market tools also heavily overlap in purpose, as do ai_visibility_check and scan_competitor_ai_presence. Only the Openverse media tools and memory/subscription tools are cleanly distinguishable.
All names are lowercase snake_case, but the naming conventions are mixed: verb_noun tools like search_images and resolve_entity coexist with bare verbs like remember, recall, forget, and subscribe, plus noun compounds like entity_profile, polymarket_edges, and bet_research. Subfamilies such as polymarket_* and the audio/image tools are internally consistent, but there is no single predictable pattern across the full set.
37 tools exceeds the 25+ threshold and feels inflated for the surface, especially since several could be consolidated: there are three ask_pipeworx variants and five overlapping prediction-market tools. The Openverse-specific core is only 6 tools, with 31 mostly unrelated Pipeworx and utility tools attached, making the server feel like a grab-bag rather than a purpose-built Openverse integration.
Each embedded subdomain covers its main lifecycle well: Openverse has search/get/related for images and audio, memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe/recent_alerts, and research has ask, grounded, deep_research, entity_profile, compare_entities, resolve_entity, and validate_claim. Minor gaps exist—notably no Openverse video/collection tooling and no explicit memory update—but agents can work around them without hitting dead ends.