The Custom Context MCP Server transforms unstructured text into structured JSON formats based on predefined templates. It offers:
Text-to-JSON Transformation: Convert AI-generated text into structured JSON objects using templates with placeholders
Information Extraction: Intelligently extract key-value pairs from unstructured text
Complex Template Support: Handle arbitrary JSON structures with nested placeholders at any level
Workflow Automation: Process AI outputs into structured data for downstream applications
Development Tools: Features like hot reloading and MCP Inspector for debugging
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Custom Context MCP Server group this product review into JSON with template: {"product": {"name": "", "rating": "", "comment": ""}}"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
This Model Context Protocol (MCP) server provides tools for structuring and extracting data from text according to JSON templates.
Group and structure text based on JSON templates with placeholders
Extract information from AI-generated text into structured JSON formats
Support for any arbitrary JSON structure with nested placeholders
Intelligent extraction of key-value pairs from text
Process AI outputs into structured data for downstream applications
Related MCP server: Unstructured Document Processor MCP
npm installnpm startFor development with hot reloading:
npm run dev:watchThis MCP server provides two main tools:
group-text-by-json)This tool takes a JSON template with placeholders and generates a prompt for an AI to group text according to the template's structure.
{
"template": "{ \"type\": \"<type>\", \"text\": \"<text>\" }"
}The tool analyzes the template, extracts placeholder keys, and returns a prompt that guides the AI to extract information in a key-value format.
text-to-json)This tool takes the grouped text output from the previous step and converts it into a structured JSON object based on the original template.
{
"template": "{ \"type\": \"<type>\", \"text\": \"<text>\" }",
"text": "type: pen\ntext: This is a blue pen"
}It extracts key-value pairs from the text and structures them according to the template.
Define a JSON template with placeholders:
{
"item": {
"name": "<name>",
"price": "<price>",
"description": "<description>"
}
}Use group-text-by-json to create a prompt for AI:
The tool identifies placeholder keys: name, price, description
Generates a prompt instructing the AI to group information by these keys
Send the prompt to an AI model and receive grouped text:
name: Blue Pen
price: $2.99
description: A smooth-writing ballpoint pen with blue inkUse text-to-json to convert the grouped text to JSON:
Result:
{
"item": {
"name": "Blue Pen",
"price": "$2.99",
"description": "A smooth-writing ballpoint pen with blue ink"
}
}Templates can include placeholders anywhere within a valid JSON structure:
Use angle brackets to define placeholders: <name>, <type>, <price>, etc.
The template must be a valid JSON string
Placeholders can be at any level of nesting
Supports complex nested structures
Example template with nested placeholders:
{
"product": {
"details": {
"name": "<name>",
"category": "<category>"
},
"pricing": {
"amount": "<price>",
"currency": "USD"
}
},
"metadata": {
"timestamp": "2023-09-01T12:00:00Z"
}
}The server works by:
Analyzing JSON templates to extract placeholder keys
Generating prompts that guide AI models to extract information by these keys
Parsing AI-generated text to extract key-value pairs
Reconstructing JSON objects based on the original template structure
Node.js v18 or higher
npm or yarn
# Install dependencies
npm install
# Build the project
npm run build
# Run the server
npm start
# Development with hot reloading
npm run dev:watchThis project includes a custom hot reloading setup that combines:
nodemon: Watches for file changes in the src directory and rebuilds TypeScript files
browser-sync: Automatically refreshes the browser when build files change
Concurrent execution: Runs both services simultaneously with output synchronization
The setup is configured in:
nodemon.json: Controls TypeScript watching and rebuilding
package.json: Uses concurrently to run nodemon and browser-sync together
To use the custom hot reloading feature:
npm run dev:watchThis creates a development environment where:
TypeScript files are automatically rebuilt when changed
The MCP server restarts with the updated code
Connected browsers refresh to show the latest changes
You can use the MCP Inspector for debugging:
npm run devThis runs the server with the MCP Inspector for visual debugging of requests and responses.
Gives a prompt text for AI to group text based on JSON placeholders. This tool accepts a JSON template with placeholders.
| Name | Required | Description | Default |
|---|---|---|---|
| template | Yes | JSON template with placeholders |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool 'gives a prompt text' but does not explain what happens next—e.g., whether it returns a prompt for AI processing, requires additional steps, or has any side effects like rate limits or authentication needs. This leaves key behavioral traits unspecified.
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 concise with two sentences that directly address the tool's function and input. It avoids unnecessary details and is front-loaded with the core purpose. However, it could be more structured by explicitly separating usage guidelines or behavioral context.
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 lack of annotations and output schema, the description is incomplete. It does not explain what the tool returns (e.g., the prompt text format or any output structure), nor does it cover behavioral aspects like error handling or prerequisites. For a tool with no structured support, the description should provide more context to be fully helpful.
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?
The description adds minimal meaning beyond the input schema, which has 100% coverage for the single parameter 'template.' It restates that the tool 'accepts a JSON template with placeholders,' mirroring the schema's description. Since schema coverage is high, the baseline score is 3, as the description does not significantly enhance parameter understanding.
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 states the tool 'gives a prompt text for AI to group text based on JSON placeholders,' which provides a vague purpose. It mentions the action ('gives a prompt text') and resource ('JSON placeholders'), but lacks specificity about what 'group text' means or how the prompt is used. It does not clearly distinguish from sibling tool 'text-to-json,' leaving ambiguity in its exact function.
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 offers no guidance on when to use this tool versus alternatives. It does not mention the sibling tool 'text-to-json' or provide context for choosing between them. Without explicit usage instructions or exclusions, users must infer when this tool is appropriate, leading to potential misuse.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Converts groupped text from group-text-by-json tool to JSON. This tool accepts a JSON template with placeholders and groupped text from group-text-by-json tool.
| Name | Required | Description | Default |
|---|---|---|---|
| template | Yes | JSON template with placeholders | |
| text | Yes | Groupped text from groupTextByJson tool |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the conversion process but lacks details on error handling, output format, or any constraints (e.g., template syntax, validation rules). For a tool with no annotations, this leaves significant gaps in understanding how it behaves beyond the basic operation.
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 concise with two sentences that directly state the tool's function and inputs. It avoids unnecessary words and is front-loaded with the core purpose. However, the repetition of 'group-text-by-json tool' could be slightly streamlined, and it lacks structural elements like bullet points for clarity.
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 complexity (data transformation with two parameters), lack of annotations, and no output schema, the description is incomplete. It covers the basic operation but misses details on output format, error cases, or example usage. While it mentions the sibling tool, more context on the overall workflow would improve completeness for effective agent use.
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 description coverage is 100%, so the schema already documents both parameters ('template' and 'text') with clear descriptions. The description adds minimal value by repeating that it accepts a 'JSON template with placeholders' and 'groupped text from groupTextByJson tool', but doesn't provide additional semantics beyond what the schema states. This meets the baseline for high schema coverage.
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 tool's purpose: converting grouped text to JSON using a template. It specifies the verb 'converts' and the resource 'groupped text from group-text-by-json tool', making the action and input source explicit. However, it doesn't fully distinguish from its sibling 'group-text-by-json' beyond mentioning it as the source, missing an opportunity to clarify the workflow relationship more distinctly.
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 implies usage by specifying that it accepts input from 'group-text-by-json tool', suggesting a sequential workflow. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., direct JSON creation tools) or any prerequisites beyond having the grouped text. No exclusions or clear when-not-to-use scenarios are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0group-text-by-jsontext-to-jsonThe two tools have clearly distinct purposes: group-text-by-json prepares text for grouping based on a JSON template, while text-to-json converts that grouped text into JSON. There is no overlap or ambiguity between them, as they represent sequential steps in a workflow.
Both tools follow a consistent verb-noun pattern with hyphens (group-text-by-json and text-to-json), clearly indicating their actions and targets. The naming is predictable and readable across the set.
With only 2 tools, the server feels thin for its apparent purpose of custom context management. This minimal set may limit functionality and require agents to work around gaps, as typical context-related operations might include more than just grouping and conversion.
The tool surface is severely incomplete for a custom context server. It lacks core operations such as creating, updating, deleting, or retrieving context entries, and only covers a narrow text-to-JSON conversion workflow, leaving significant gaps that will likely cause agent failures.
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