validate_json_schemaValidate a JSON value against a JSON Schema (draft-07 subset: type, required, properties, additionalProperties, items, enum, const, min/max, minLength/maxLength, pattern, minItems/maxItems, uniqueItems, format). Returns {valid, errors:[{path,message}]}. Keyless, offline.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | The JSON value to validate (any type). | |
| schema | Yes | The JSON Schema to validate against. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "data": {
+ "age": 30,
+ "email": "john@example.com",
+ "name": "John Doe"
+ },
+ "schema": {
+ "properties": {
+ "age": {
+ "minimum": 0,
+ "type": "number"
+ },
+ "email": {
+ "format": "email",
+ "type": "string"
+ },
+ "name": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "name",
+ "age"
+ ],
+ "type": "object"
+ }
+ },
+ {
+ "data": [
+ 1,
+ 2,
+ 3,
+ 4,
+ 5
+ ],
+ "schema": {
+ "items": {
+ "type": "number"
+ },
+ "maxItems": 10,
+ "minItems": 1,
+ "type": "array"
+ }
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
While annotations declare readOnlyHint/idempotentHint, the description adds substantial context: supported draft-07 subset, offline/keyless behavior, and exact return format. This goes beyond annotations and helps predict tool behavior accurately.
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?
Two sentences only: first states purpose and supported keywords, second gives return format and key constraints. Every sentence earns its place 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 no output schema, the description fully compensates by specifying the return shape ({valid, errors}). It also covers supported subset and offline/keyless nature, making the tool fully understandable. Siblings are unrelated, so no missing context.
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 already provides 100% coverage with descriptions for both parameters. The description enriches the schema parameter by listing supported JSON Schema keywords, clarifying which schema features are accepted, adding value beyond the structured schema.
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?
Description clearly states the tool validates a JSON value against a JSON Schema, with a specific verb+resource. It also distinguishes itself from siblings by being a unique validation utility, not related to AI/prediction tools.
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 context ('Keyless, offline') but doesn't explicitly state when to use this tool over alternatives or exclude any use cases. No clear sibling alternatives exist, so the guidance is implied rather than explicit.
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 entry-point role (ask_pipeworx, ask_pipeworx_beta, deep_research, discover_tools, suggest_questions), and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The Polymarket family is carefully described but still has heavily overlapping discovery/edge surfaces. Only validate_json_schema is unambiguous, and it has no related siblings to clarify its position.
Tool names mix verb-first forms (discover_tools, generate_llms_txt, resolve_entity), noun-first forms (entity_profile, pipeworx_feedback), and bare verbs (remember, recall, forget). Prefix conventions are inconsistent—ask_pipeworx, pipeworx_feedback, polymarket_edges, recent_changes—and almost none of the names reflect the server name Jsonschema.
32 tools is already too many, but for a server named Jsonschema only one tool belongs to that domain; the rest form a broad data-research and prediction-market suite. This is an extreme scoping mismatch: the set is simultaneously oversized and almost entirely off-purpose.
For a JSON Schema server, the surface is essentially one operation: validate_json_schema. Common schema lifecycle operations—generation, parsing, conversion, ref resolution, linting—are missing, leaving most JSON Schema tasks impossible. The unrelated data-research tools are internally rich, but they do not complete the apparent domain.