JSON MCP Boilerplate
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| json_readA | Read and analyze JSON. Always use this tool to explore JSON structure, understand data schema, or get high-level overviews of large JSON. Use this for initial data exploration or when you need to understand the shape and types of data before extracting specific values. |
| json_extractA | Extract specific data using paths, filters, patterns, or slices from JSON files. Always use this tool when you need to retrieve particular values, filter arrays/objects by conditions, search for patterns, or slice data. Ideal for targeted data extraction, data transformation, and focused analysis of specific JSON elements. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: json_extract is for targeted data extraction and transformation, while json_read is for initial exploration and schema understanding. Their descriptions explicitly guide when to use each, eliminating any ambiguity or overlap in functionality.
Both tools follow a consistent verb_noun pattern (json_extract and json_read) with the same prefix 'json_' and clear action verbs. This predictable naming makes it easy for agents to understand and select the appropriate tool.
With only two tools, the server feels too thin for a JSON processing domain, lacking essential operations like json_write, json_validate, or json_transform. While the tools cover reading and extraction, the scope is incomplete for typical JSON workflows, making it borderline inadequate.
The tool surface has significant gaps: it supports reading and extraction but lacks write, update, validation, or advanced transformation capabilities. This incomplete coverage will likely cause agent failures when tasks require modifying or validating JSON data, limiting practical utility.