fetch_schemaFetch a JSON Schema document by URL (must be hosted on json.schemastore.org).
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | e.g. https://json.schemastore.org/package.json |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "url": "https://json.schemastore.org/package.json"
+ },
+ {
+ "url": "https://json.schemastore.org/tsconfig.json"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "JSON Schema document (passthrough from SchemaStore)",
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds the important behavioral constraint about the allowed URL domain, which is beyond what annotations provide. No contradiction.
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?
Single, front-loaded sentence with no wasted words. Every part is essential for understanding the tool.
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 presence of an output schema and annotations, the description is complete. It covers the key constraint and purpose without omission.
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 100% with examples. The description adds minimal value beyond the schema, simply restating that the URL is a fetch target. Baseline 3 is appropriate.
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 action ('Fetch') and resource ('JSON Schema document') with a specific constraint ('must be hosted on json.schemastore.org'). This distinguishes it from sibling tools like 'find_schema_for' and 'list_schemas'.
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 a clear when-to-use constraint (URL must be on json.schemastore.org) but does not explicitly mention alternatives or when not to use this tool. However, the constraint itself guides the agent.
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 overlapping semantic space: ask_pipeworx, ask_pipeworx_beta (currently identical), and ask_pipeworx_grounded are the same router in different modes, while suggest_questions and discover_tools both serve capability discovery. The detailed descriptions help, but an agent could easily select the wrong entry point, especially among the prediction-market and router variants.
All tool names use lowercase snake_case and mostly follow a verb_noun pattern (fetch_schema, resolve_entity, validate_claim), with some domain-prefixed nouns (polymarket_edges, pipeworx_trending) and a few adjective_noun outliers (recent_alerts, recent_changes). The convention is consistent and predictable, with only minor deviations.
At 35 tools, the set is far too large for a server named Schemastore — only four tools relate to the schema catalog while the rest form a sprawling data-research, prediction-market, memory, and subscription platform. The count is heavy and the scope feels unfocused.
The broad data-research and prediction-market domain is well covered — lookup, compare, validate, research, arbitrage, subscriptions, and memory all have lifecycle support — but the server's namesake purpose (schema catalog) is thinly served by four read-only tools with no way to contribute or manage schemas. The domain mismatch makes the surface feel both over- and under-complete.