userFetch a single fake user by numeric id from DummyJSON. Returns full profile including name, email, phone, address, bank, company, and crypto wallet.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | User ID | |
| No | Email address | ||
| image | No | Profile image URL | |
| phone | No | Phone number | |
| gender | No | Gender | |
| lastName | No | Last name | |
| username | No | Username | |
| firstName | No | First name |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "id": 1
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "email": {
+ "description": "Email address",
+ "type": "string"
+ },
+ "firstName": {
+ "description": "First name",
+ "type": "string"
+ },
+ "gender": {
+ "description": "Gender",
+ "type": "string"
+ },
+ "id": {
+ "description": "User ID",
+ "type": "number"
+ },
+ "image": {
+ "description": "Profile image URL",
+ "type": "string"
+ },
+ "lastName": {
+ "description": "Last name",
+ "type": "string"
+ },
+ "phone": {
+ "description": "Phone number",
+ "type": "string"
+ },
+ "username": {
+ "description": "Username",
+ "type": "string"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, openWorld, idempotent hints. The description adds value by enumerating specific fields (name, email, etc.) beyond what annotations provide.
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?
A single concise sentence that is front-loaded with the verb and resource, with no wasted words.
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?
With an output schema present, the description covers input semantics and return contents sufficiently. Annotations fully describe safety and idempotency, making it complete.
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 0%, so the description compensates by specifying the parameter is a numeric ID and that it is required, adding meaning beyond the schema's type definition.
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 fetching a single fake user by ID from DummyJSON, distinguishing it from sibling tools like 'users' (plural) and listing returned fields.
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 (fetch single user by ID) but does not explicitly mention when not to use or compare to alternatives, though context is clear.
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.
Many tools have overlapping purposes, e.g., multiple tools for data retrieval (ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile) that differ only in nuance, and the inclusion of both DummyJSON and Pipeworx tools creates confusion about which domain to use for what. Agents will struggle to select the correct tool.
Naming conventions are mixed: Pipeworx tools use diverse patterns (verb_noun like 'validate_claim', noun like 'entity_profile', verb like 'forget'), while DummyJSON tools use simple nouns (posts, comments). No consistent pattern across the set.
43 tools is excessive for a server named 'Dummyjson'. The majority are Pipeworx tools unrelated to fake data, making the set feel bloated and unfocused. The count is too large for the apparent scope.
For a fake data API, the set is incomplete: it only provides read operations (fetch, search) with no create, update, or delete capabilities. However, for the Pipeworx portion, the read coverage is extensive, so it's not severely lacking overall.