user_profileFetch a LeetCode user's public profile by username via GraphQL; returns real name, bio, avatar, global ranking, country, company, school, and star rating.
| Name | Required | Description | Default |
|---|---|---|---|
| username | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| matchedUser | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "username": "john_doe"
+ },
+ {
+ "username": "algorithm_master"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "matchedUser": {
+ "properties": {
+ "profile": {
+ "properties": {
+ "aboutMe": {
+ "description": "User bio or about section",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "company": {
+ "description": "User's company",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "countryName": {
+ "description": "User's country",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "ranking": {
+ "description": "User's global ranking",
+ "type": [
+ "number",
+ "null"
+ ]
+ },
+ "realName": {
+ "description": "User's real name",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "school": {
+ "description": "User's school",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "starRating": {
+ "description": "User's star rating",
+ "type": [
+ "number",
+ "null"
+ ]
+ },
+ "userAvatar": {
+ "description": "Avatar URL",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "websites": {
+ "description": "User's websites",
+ "items": {
+ "type": "string"
+ },
+ "type": [
+ "array",
+ "null"
+ ]
+ }
+ },
+ "type": "object"
+ },
+ "username": {
+ "description": "LeetCode username",
+ "type": "string"
+ }
+ },
+ "type": "object"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds context by specifying the GraphQL method and the exact return fields, which is valuable beyond the annotations. However, it does not mention potential rate limits or data freshness.
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 a single, well-structured sentence that front-loads the action and resource. Every word adds value, 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?
Given the tool's simplicity (one required param) and the presence of an output schema, the description is adequately complete. It covers what the tool does and what it returns. Minor improvement could mention that the profile is public.
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 0% schema description coverage, the description partially compensates by explaining that the username is for LeetCode and listing the return fields. However, it does not elaborate on the username format or constraints, leaving the agent to infer from the examples.
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 'Fetch', the resource 'LeetCode user's public profile', the input 'by username', and the method 'via GraphQL'. It also lists specific return fields, making the tool's purpose unambiguous and distinct from siblings like 'entity_profile'.
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 provides no guidance on when to use this tool versus alternatives. With 30+ sibling tools, the lack of explicit usage context or exclusions leaves the agent to infer when this is the appropriate choice.
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.
Multiple tools have overlapping roles: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve the same lookup purpose, and there are six different Polymarket tools with similar names and functions. The inclusion of a large unrelated data platform alongside a few LeetCode tools makes selection additionally confusing.
Almost all tools follow a consistent snake_case verb_noun pattern (ask_pipeworx, compare_entities, list_subscriptions, etc.). Minor exceptions like 'problem' and 'daily_question' are still readable and don't break the overall predictability.
37 tools is far too many for a server named 'Leetcode' — the vast majority are unrelated Pipeworx data, prediction-market, and memory tools. Even as a general data server the count is heavy, and for the apparent LeetCode purpose it is severely over-scoped.
The LeetCode-specific tools cover basic user stats and problem details but lack problem listing/search, submissions, or any interaction beyond read-only queries. The Pipeworx side is extensive but irrelevant to the server's stated purpose, so the core domain has significant gaps.