query_jsonExtract a value from JSON at a path using dot/bracket notation, e.g. "user.addresses[0].city". Keyless, offline.
| Name | Required | Description | Default |
|---|---|---|---|
| json | Yes | The JSON text. | |
| path | Yes | A path like "a.b[0].c". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "json": "{\"user\": {\"name\": \"Bob\", \"address\": {\"city\": \"NYC\"}}}",
+ "path": "user.address.city"
+ },
+ {
+ "json": "{\"items\": [{\"id\": 1, \"title\": \"First\"}, {\"id\": 2, \"title\": \"Second\"}]}",
+ "path": "items[0].title"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, etc. The description adds 'Keyless, offline' as extra behavioral context, but does not discuss edge cases like missing keys or invalid paths. Moderate value beyond annotations.
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 very short, consisting of one sentence with an example. No wasted words, but could be slightly more structured. Effectively concise.
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?
For a simple tool with 2 fully described parameters and no output schema, the description is mostly complete. It covers the core functionality, example, and key behavioral traits (keyless, offline). Could mention error handling, but adequate.
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 descriptions for both parameters. The description adds an example but no new semantic meaning beyond what the schema provides. 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 it extracts a value from JSON using dot/bracket notation, with a concrete example. It distinguishes from siblings like format_json and validate_json by specifying the action and resource.
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 mentions 'Keyless, offline', implying no auth or network needed, but does not explicitly state when to use vs alternatives or when not to use it. Usage context is somewhat implied by the sibling tools' purposes.
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.
Each tool has a clearly distinct purpose, from querying data (ask_pipeworx) to entity profiling (entity_profile) to prediction market analysis (polymarket_arbitrage, polymarket_edges). Even similar tools like deep_research and ask_pipeworx are differentiated by scope (single vs. multi-facet). There is no ambiguity.
The vast majority of tools use snake_case (e.g., validate_claim, compare_entities). However, a few tools are single words (forget, recall, remember, subscribe, unsubscribe) which breaks the pattern slightly. This is a minor inconsistency.
With 33 tools, the set is on the larger side but well-justified by the broad scope of the server (data querying, entity research, prediction markets, JSON utilities, subscriptions, etc.). Each tool serves a specific need, making the count appropriate.
The tool surface covers virtually all expected operations for the domain: querying, profiling, comparison, change tracking, validation, discovery, subscriptions, memory, and utilities. No obvious gaps are present for the intended use case of accessing Pipeworx data and related tasks.