Supports running JavaScript code in a sandboxed environment to perform data analysis and script execution.
Provides OS-level sandboxing via bubblewrap for the secure execution of multi-language scripts and system commands.
Leverages sandbox-exec to provide a secure, isolated environment for executing code and shell commands on macOS.
Provides a secure environment to execute Node.js code and scripts for task automation and tool execution.
Enables sandboxed execution of Python scripts for data processing and code verification with high token efficiency.
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., "@Token-Efficient MCP Server Filter sales.csv for rows where price > 500 and show a summary"
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.
A project-agnostic MCP (Model Context Protocol) server that provides 95%+ token savings through sandboxed data processing, progressive tool loading, and multi-language code execution.
Execute code in sandboxed environment:
Python, Bash, Node.js/JavaScript support
Critical for agent systems (initializer, coding, tester, verifier)
Run commands, tests, and validations with 98% token savings
Load tools on-demand to reduce context usage:
Level 1: Tool names only (~100 tokens)
Level 2: Names + summaries (~2K tokens)
Level 3: Full definitions (only when needed)
Process data securely before returning to context:
CSV filtering and aggregation (99% savings)
Log file analysis (95% savings)
Code execution with output filtering
Token measurement and optimization
Works with any project that needs:
Multi-language code execution
Large dataset processing
Log analysis
Token optimization
Related MCP server: Code Execution MCP
# Clone the repository
git clone https://github.com/your-repo/token-efficient-mcp.git
cd token-efficient-mcp
# Install dependencies
npm install
# Build TypeScript
npm run buildAdd to your global ~/.claude.json:
{
"mcpServers": {
"token-efficient": {
"command": "srt",
"args": [
"node",
"/path/to/token-efficient-mcp/dist/index.js"
]
}
}
}Note: The srt command provides OS-level sandboxing via sandbox-exec (macOS) or bubblewrap (Linux).
execute_codeExecute code in multiple languages with sandboxing.
// Run bash commands
execute_code({
code: "npm test",
language: "bash"
})
// Run Python scripts
execute_code({
code: "import sys; print(sys.version)",
language: "python"
})
// Run Node.js code
execute_code({
code: "console.log('Hello from Node')",
language: "node"
})
// Check health endpoint
execute_code({
code: "curl -m 3 http://localhost:8000/api/health",
language: "bash"
})Supported Languages: python, bash, sh, node, javascript
list_token_efficient_toolsDiscover available tools with progressive disclosure.
// Level 1: Names only (100 tokens)
list_token_efficient_tools({ level: "names_only" })
// Level 2: Summaries (2K tokens)
list_token_efficient_tools({ level: "summary" })
// Level 3: Full definitions
list_token_efficient_tools({ level: "full" })process_csvProcess CSV files with filtering and aggregation.
// Example: Find expensive stocks
process_csv({
file_path: "data/stocks.csv",
filter_expr: "price > 100 and volume > 1000000",
columns: ["symbol", "price", "volume", "change"],
limit: 10,
response_format: "summary"
})process_logsFilter and analyze log files efficiently.
// Example: Find all errors with context
process_logs({
file_path: "logs/application.log",
pattern: "ERROR|CRITICAL",
context_lines: 2,
limit: 50,
response_format: "summary"
})get_token_savings_reportGet optimization tips and savings potential.
// Without execute_code: Multi-turn conversation
// Agent: "Should I run npm test?" → User: "Yes" → Run → Parse output
// Estimated: 5,000+ tokens across multiple turns
// With execute_code: Single call
execute_code({ code: "npm test", language: "bash" })
// Returns: { success: true, output: "Tests passed", exit_code: 0 }
// Result: 200 tokens (98% savings)// Without optimization: 200,000 tokens
// All 10,000 rows returned to context
// With token-efficient MCP: 2,000 tokens (99% savings)
// Only 100 filtered rows returned// Without optimization: 500,000 tokens
// All 100,000 log lines returned
// With token-efficient MCP: 5,000 tokens (99% savings)
// Only 500 matching lines with context returned// Traditional MCP: 150,000 tokens
// All tool definitions loaded at startup
// Token-efficient MCP: 2,000 tokens (98.7% savings)
// Tools loaded on-demandThe server uses OS-level sandboxing via srt wrapper:
Filesystem isolation: Limited to temp directories for code execution
Network restrictions: No outbound connections by default
Process monitoring: Timeouts (1-300s) and resource limits
Multi-language support: Sandboxed Python, Bash, Node.js execution
# Build the project
npm run build
# Test execute_code tool
node -e "
const { exec } = require('child_process');
const code = \`echo 'Hello from test'\`;
exec(\`node dist/index.js\`, (err, stdout) => {
console.log(stdout);
});
"
# Or test directly with MCP
# The server will be loaded by Claude Code via ~/.claude.json configThe server tracks and reports:
Input tokens: Size of request
Output tokens: Size of response
Processing efficiency: Items processed per token
Estimated savings: Percentage of tokens saved
Example response:
{
"token_metrics": {
"input_tokens": 250,
"output_tokens": 1500,
"estimated_savings_percent": 98.5
}
}Fork the repository
Create a feature branch
Add tests for new functionality
Ensure token efficiency principles are followed
Submit a pull request
MIT License - see LICENSE file for details.
Process multiple CSV files in a single call with consistent filtering. Achieves 80% token savings for multiple files vs individual calls.
| Name | Required | Description | Default |
|---|---|---|---|
| file_paths | Yes | Paths to CSV files (max 5) | |
| filter_expr | No | Filter expression applied to all files | |
| columns | No | Columns to select from all files | |
| limit | No | Maximum rows per file | |
| aggregate | No | If true, combine results from all files into aggregated summary |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions performance benefits ('80% token savings'), it doesn't disclose important behavioral traits like whether this is a read-only or destructive operation, what permissions are required, error handling behavior, rate limits, or what happens when files have different schemas. For a tool that processes multiple files with filtering and aggregation, this is a significant gap in behavioral transparency.
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 extremely concise and front-loaded with the core functionality in the first sentence. The second sentence adds valuable performance context. Both sentences earn their place, and there's no wasted verbiage or redundant information.
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 tool with 5 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns (aggregated results? individual file results? error handling?), doesn't mention the 5-file limit documented in the schema, and provides minimal behavioral context. The performance benefit mention is helpful but doesn't compensate for the missing operational details.
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 schema description coverage is 100%, so all parameters are well-documented in the schema itself. The description doesn't add any meaningful parameter semantics beyond what's already in the schema - it doesn't explain how parameters interact, provide examples of filter expressions, or clarify the 'aggregate' parameter's effect. With complete schema coverage, the baseline score of 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 tool's purpose: 'Process multiple CSV files in a single call with consistent filtering.' It specifies the verb ('process'), resource ('multiple CSV files'), and scope ('single call with consistent filtering'). However, it doesn't explicitly distinguish this tool from its sibling 'process_csv' tool, which appears to be a single-file version.
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 some usage guidance by mentioning '80% token savings for multiple files vs individual calls,' which implies this tool should be used when processing multiple CSV files rather than making individual calls. However, it doesn't explicitly state when NOT to use this tool or provide clear alternatives (like when to use 'process_csv' instead). 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.
Execute code in sandboxed environment. Supports Python, Bash, Node.js. Now with heredoc support for bash scripts (<<EOF, <<'EOF', <<EOT). Achieves 98%+ token savings by processing in execution environment.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | Code to execute. Supports heredocs for bash/sh: <<EOF, <<'EOF'. | |
| language | No | Programming language | bash |
| timeout | No | Execution timeout in seconds | |
| response_format | No | summary |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'sandboxed environment' (safety context) and '98%+ token savings' (performance trait), but lacks critical details like authentication needs, rate limits, error handling, or what 'sandboxed' entails (e.g., isolation level, resource constraints). For a mutation tool (code execution) with zero annotation coverage, this is insufficient.
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 appropriately sized with three sentences, front-loaded with core functionality. The second sentence adds useful language support, and the third provides performance context, but the token savings claim could be trimmed as it doesn't directly aid tool selection. Overall efficient with minimal waste.
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 code execution tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on return values (e.g., stdout/stderr format), error cases, security implications of sandboxing, and prerequisites. The 98% token savings note is tangential rather than essential for tool understanding, leaving gaps in operational 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 description coverage is 75%, providing good baseline documentation for parameters. The description adds value by explicitly listing supported languages (Python, Bash, Node.js) and detailing heredoc syntax for bash, which clarifies the 'code' parameter beyond the schema's generic description. However, it doesn't explain other parameters like 'timeout' or 'response_format' beyond what the schema already covers.
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 specific action ('Execute code') and resource ('in sandboxed environment'), with explicit mention of supported languages (Python, Bash, Node.js). It distinguishes from sibling tools like batch_process_csv or process_logs by focusing on code execution rather than data processing.
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 for executing code in supported languages, but provides no explicit guidance on when to use this tool versus alternatives like batch_process_csv for data tasks. It mentions heredoc support for bash, which hints at usage for multi-line scripts, but lacks clear when/when-not scenarios or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get detailed token savings report and optimization best practices
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves a report and best practices, implying a read-only operation, but doesn't clarify aspects like authentication needs, rate limits, data freshness, or whether it's a summary or detailed analysis. For a tool with zero annotation coverage, 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 a single, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place, achieving optimal conciseness.
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 has 0 parameters, 100% schema coverage, and no output schema, the description is minimally adequate. It explains what the tool does but lacks details on output format, behavioral constraints, or usage context. For a report-fetching tool with no annotations, it should ideally provide more guidance on what the report contains or how to interpret it.
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 tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it appropriately avoids mentioning any. A baseline score of 4 is applied as it handles the zero-parameter case correctly without unnecessary details.
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 with a specific verb ('Get') and resource ('detailed token savings report and optimization best practices'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_token_efficient_tools' or 'search_tools', which might also relate to token optimization, preventing a perfect score.
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 no guidance on when to use this tool versus alternatives. It doesn't mention context, prerequisites, or exclusions, leaving the agent to infer usage based on the name alone. This lack of explicit direction is a significant gap in helping the agent select the right tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List available token-efficient tools with progressive disclosure (names_only, summary, full)
| Name | Required | Description | Default |
|---|---|---|---|
| level | No | summary |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'progressive disclosure' but doesn't explain what each disclosure level entails, whether the operation is read-only, if there are rate limits, or what format the output takes. The behavioral characteristics are underspecified for a tool with no annotation coverage.
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 extremely concise - a single sentence that efficiently communicates the core functionality and parameter options. Every word earns its place with zero waste or 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 the tool's low complexity (1 parameter with enum), no annotations, and no output schema, the description provides basic but incomplete context. It covers the progressive disclosure concept but lacks details about output format, pagination, or error conditions that would be helpful for an agent.
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?
With 0% schema description coverage, the description must compensate. It explains the 'level' parameter's purpose (progressive disclosure) and lists the three enum values, adding meaningful context beyond the bare schema. However, it doesn't detail what each disclosure level specifically returns.
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: listing available token-efficient tools with progressive disclosure options. It specifies the verb 'list' and resource 'token-efficient tools', though it doesn't explicitly differentiate from sibling tools like 'search_tools' or 'get_token_savings_report'.
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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'search_tools' or 'get_token_savings_report', nor does it specify appropriate contexts or exclusions for using this listing function.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Process CSV files efficiently with filters, groupby aggregation, and pagination. Use offset for large files (>10K rows) to achieve 99% token savings.
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes | Path to CSV file | |
| filter_expr | No | Filter expression (e.g., "price > 100") | |
| columns | No | Columns to select | |
| limit | No | Maximum rows to return | |
| offset | No | Skip first N rows before returning results (pagination) | |
| aggregate_by | No | Column to aggregate by | |
| agg_func | No | Aggregation function | |
| response_format | No | summary = stats + 5 sample rows (for humans), full = all rows in data array (for processing) | summary |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions efficiency, token savings, and pagination for large files, which adds useful context beyond basic functionality. However, it lacks details on error handling, performance limits, or what happens with invalid inputs, leaving gaps for a tool with 8 parameters.
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 front-loaded with key functionality and includes a specific efficiency tip in just two sentences. Every sentence earns its place by conveying essential information without waste, making it appropriately sized for the tool's complexity.
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 (8 parameters, no annotations, no output schema), the description is adequate but has clear gaps. It covers purpose and some usage guidelines but lacks details on return values, error cases, or integration with sibling tools, which would help an agent use it more effectively.
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 all parameters thoroughly. The description adds minimal value by implying the tool uses filters, groupby, and pagination, but doesn't provide additional syntax or usage details beyond what's in the schema. 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 processes CSV files with specific operations (filters, groupby aggregation, pagination), which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'batch_process_csv' or 'process_logs', which might handle similar file processing tasks.
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 clear context for when to use the tool (efficient processing with specific features) and includes a specific guideline for large files (>10K rows) to use offset for token savings. However, it doesn't explicitly mention when not to use it or name alternatives among siblings, such as when to choose 'batch_process_csv' instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Process log files efficiently with pattern matching and pagination. Use offset to skip previous matches.
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes | Path to log file | |
| pattern | Yes | Regex pattern to match | |
| limit | No | Maximum matches to return | |
| offset | No | Skip first N matches before returning results (pagination) | |
| context_lines | No | Lines of context around matches | |
| response_format | No | summary = stats + 5 sample rows (for humans), full = all rows in data array (for processing) | summary |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions 'efficiently' and 'pagination,' but lacks critical details like performance expectations, error handling, file size limits, or output format. For a tool with 6 parameters and no annotations, this leaves significant behavioral gaps.
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 extremely concise—two sentences with zero waste. It front-loads the core purpose and includes a practical usage tip. Every word serves a clear function, making it easy to parse quickly.
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 (6 parameters, no output schema, no annotations), the description is incomplete. It lacks details on return values, error conditions, performance traits, and how it differs from siblings. While concise, it doesn't provide enough context for an agent to use it confidently without trial and error.
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 parameters are well-documented in the schema. The description adds minimal value beyond this, only explicitly mentioning 'offset' for pagination and implying pattern matching. It doesn't clarify interactions between parameters or provide examples, aligning with 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: 'Process log files efficiently with pattern matching and pagination.' This specifies the verb ('process'), resource ('log files'), and key capabilities. However, it doesn't explicitly differentiate from sibling tools like 'process_csv' or 'batch_process_csv' beyond the log file focus, preventing a perfect score.
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 minimal usage guidance. It mentions using 'offset to skip previous matches,' which hints at pagination scenarios, but offers no explicit when-to-use advice, alternatives, or exclusions compared to siblings. Without context on when to choose this over similar tools, guidance is insufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search available tools by keyword with optional category filter. Achieves 95% token savings vs loading all tool definitions.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search keyword(s) to find matching tools | |
| category | No | Optional category filter | |
| level | No | Level of detail for results | summary |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions '95% token savings vs loading all tool definitions' which hints at performance characteristics, but doesn't describe what the search returns (e.g., tool names, descriptions, metadata), whether results are paginated, authentication requirements, rate limits, or error conditions. The behavioral context is insufficient for a tool with no annotation coverage.
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 appropriately concise with two sentences. The first sentence clearly states the core functionality, and the second adds useful performance context. However, the token savings claim feels somewhat promotional and could be integrated more naturally with the tool's purpose.
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 search tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the search returns (e.g., tool metadata, IDs, descriptions), how results are structured, or any limitations. The token savings mention is helpful but doesn't compensate for missing behavioral and output context that an agent needs to use this tool effectively.
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 all three parameters thoroughly. The description mentions 'keyword with optional category filter' which aligns with the schema but adds no additional semantic context beyond what's in the parameter descriptions. The token savings mention doesn't relate to parameter usage. Baseline 3 is appropriate when schema does the heavy lifting.
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 as searching tools by keyword with optional category filtering, using specific verbs ('search') and resources ('tools'). It distinguishes from some siblings like 'batch_process_csv' or 'execute_code' by focusing on discovery rather than execution, though it doesn't explicitly differentiate from 'list_token_efficient_tools' which is also a discovery tool.
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 no guidance on when to use this tool versus alternatives. It mentions token savings, but doesn't specify when to prefer this over sibling tools like 'list_token_efficient_tools' or other discovery methods. There are no explicit when/when-not instructions or named alternatives.
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.
v1.2.1batch_process_csvexecute_codeget_token_savings_reportlist_token_efficient_toolsprocess_csvprocess_logssearch_toolsMost tools have distinct purposes, such as batch_process_csv for multiple files, process_csv for single files, and execute_code for code execution. However, list_token_efficient_tools and search_tools could be confused as both involve tool discovery, though search_tools adds keyword filtering.
All tool names follow a consistent verb_noun pattern using snake_case, such as batch_process_csv, execute_code, and get_token_savings_report. This uniformity makes the set predictable and easy to navigate.
With 7 tools, the count is well-scoped for a server focused on token-efficient operations. Each tool serves a clear purpose, such as processing files, executing code, and reporting savings, without being overly sparse or bloated.
The tool set covers key areas like file processing (CSV/logs), code execution, and tool discovery, with a focus on token efficiency. A minor gap is the lack of tools for updating or deleting processed data, but agents can work around this given the server's optimization-centric domain.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
SaaS intelligence for AI agents. 5 unified tools cover 1,000+ services with 91-96% token savings.
See, price, and control every tool call your AI agents make: policy checks, cost, and audit tools.
Build Apps and run code in 30 languages — sandboxed, with persistent sessions for agent loops.
JSON/YAML, regex, diff, JWT, SQL dialects — the keyless millisecond ops an agent needs mid-task.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/ingpoc/token-efficient-mcp'
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