Provides pre-commit hooks to run quick algorithm benchmark validations before commits.
Enables automated algorithm benchmarking and performance regression detection in CI/CD workflows.
Click on "Deploy 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., "@Algorate MCP Server Compare QuickSort and MergeSort performance on random arrays"
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 Model Context Protocol (MCP) server for comprehensive algorithm benchmarking, performance analysis, and optimization. Compare multiple implementations, detect performance bottlenecks, and get AI-driven optimization insights across JavaScript, TypeScript, and Python code.
Install globally via npm:
npm install -g @cmiretf/algorate-mcpOr add to your project:
npm install @cmiretf/algorate-mcpTest the server interactively:
npm install -g @cmiretf/algorate-mcp
npx @modelcontextprotocol/inspector algorateOr if installed locally:
npx @modelcontextprotocol/inspector node node_modules/algorate/dist/index.jsAdd to your claude_desktop_config.json:
{
"mcpServers": {
"algorate": {
"command": "npx",
"args": ["-y", "@cmiretf/algorate-mcp"]
}
}
}Or with a local installation:
{
"mcpServers": {
"algorate": {
"command": "node",
"args": ["/path/to/node_modules/algorate/dist/index.js"]
}
}
}Add to your mcp.json:
{
"servers": {
"algorate": {
"command": "npx",
"args": ["-y", "@cmiretf/algorate-mcp"]
}
}
}Or with a local installation:
{
"mcpServers": {
"algorate": {
"command": "node",
"args": ["/path/to/node_modules/algorate/dist/index.js"]
}
}
}Related MCP server: GitHub Copilot Response Time Statistics MCP Server
Register Algorithms: Define custom algorithms with unique identifiers
Multiple Implementations: Compare 2+ implementations of the same algorithm
Language Support: JavaScript, TypeScript, and Python
Automatic Detection: AI-powered algorithm detection from code
Execution Metrics: Precise timing with warmup and measurement runs
Memory Profiling: Track peak memory usage and memory trends
Statistical Analysis: Mean, median, std deviation, P95, P99 percentiles
Consistency Tracking: Identify variability and outliers
Workload Generation: Automatic test data generation for different input sizes
Output Validation: Ensure correctness across all implementations
Isolated Execution: Worker-based isolation for accurate measurements
Result Storage: Persistent storage of benchmarks with versioning
Performance Insights: Automatic detection of performance patterns
Code Optimization: Receive specific optimization recommendations
Performance Comparison: Automated ranking and insights
Bottleneck Detection: Identify slow operations and memory issues
Query Engine: Natural language queries on benchmark results
register_algorithmRegister a new algorithm for benchmarking.
Parameters:
name (string): Algorithm name (e.g., "Sorting", "Searching")
description (string, optional): Detailed description
Example:
{
"name": "QuickSort",
"description": "Fast sorting algorithm using divide and conquer"
}register_implementationAdd an implementation of an algorithm.
Parameters:
algorithmId (string): ID of the algorithm
name (string): Implementation name
language (string): "javascript", "typescript", or "python"
code (string): Function code
functionName (string): Name of the exported function
register_test_caseCreate a test case for benchmarking.
Parameters:
name (string): Test case name
inputSize (number): Size of input
inputType (string): "array", "number", "string", "object"
inputData (any): The actual input
expectedOutput (any): Expected result for validation
run_benchmarkExecute a complete benchmark comparing implementations.
Parameters:
algorithmId (string): Algorithm to benchmark
testCaseId (string): Test case to use
warmupRuns (number, optional): Warmup executions (default: 3)
measurementRuns (number, optional): Measurement runs (default: 10)
timeoutMs (number, optional): Timeout per execution in ms (default: 30000)
validateOutput (boolean, optional): Enable validation (default: true)
Example:
{
"algorithmId": "algo-123",
"testCaseId": "test-456",
"warmupRuns": 3,
"measurementRuns": 10,
"validateOutput": true
}list_algorithmsList all registered algorithms.
Parameters:
None
list_implementationsList implementations, optionally filtered by algorithm.
Parameters:
algorithmId (string, optional): Filter by algorithm ID
list_test_casesList all registered test cases.
Parameters:
None
get_resultsGet benchmark results for a specific implementation and test case.
Parameters:
implementationId (string): Implementation ID
testCaseId (string): Test case ID
get_algorithm_insightsGet AI-powered insights about algorithm performance.
Parameters:
algorithmId (string): Algorithm to analyze
optimize_codeReceive specific optimization recommendations.
Parameters:
code (string): Code to optimize
language (string): "javascript", "typescript", or "python"
benchmarkResults (object, optional): Previous benchmark results
query_resultsQuery benchmark results with natural language.
Parameters:
query (string): Natural language question about results
algorithmId (string, optional): Specific algorithm to query
generate_summaryGenerate a comprehensive benchmark summary report.
Parameters:
algorithmId (string): Algorithm to summarize
includeCharts (boolean): Include visualization data
generate_workloadGenerate test data for different input sizes.
Parameters:
type (string): "random", "sorted", "reverse", "nearly_sorted"
size (number): Input size
complexity (string): "low", "medium", "high"
detect_algorithmAI-powered algorithm detection from code.
Parameters:
code (string): Code to analyze
language (string): "javascript", "typescript", or "python"
auto_detect_algorithmsAutomatically detect algorithms in project files.
Parameters:
directories (array of strings, optional): Directories to scan (default: src, examples)
auto_benchmarkAutomatically run benchmarks for detected or registered algorithms.
Parameters:
algorithmIds (array of strings, optional): Algorithm IDs to benchmark (empty = all)
forceRefresh (boolean, optional): Force refresh even if cached results exist
generate_chartGenerate performance chart for benchmark results.
Parameters:
algorithmId (string): Algorithm ID
testCaseId (string, optional): Test case ID
query_performanceQuery performance analysis with automatic benchmark and summary.
Parameters:
query (string): Query about algorithm performance (e.g., "sorting algorithms", "all algorithms")
forceRefresh (boolean, optional): Force refresh even if cached results exist
directories (array of strings, optional): Directories to scan for algorithms
benchmark_allAutomatically detect all algorithms, run benchmarks, and return formatted results (ONE-CLICK BENCHMARK).
Parameters:
directories (array of strings, optional): Directories to scan (default: src, examples)
filePath (string, optional): Specific file path to analyze (if provided, only analyzes this file)
forceRefresh (boolean, optional): Force refresh even if cached results exist
Execution Time (ms): Average time to run the algorithm
Memory Peak (MB): Maximum memory used during execution
Success Rate (%): Percentage of successful executions
Std Deviation (ms): Consistency of results (lower = better)
P95/P99: Latency in worst-case scenarios
Lower Score = Better overall performance
Mean < Median = Some slower outliers detected
High StdDev = Inconsistent results (increase warmup runs)
High Success Rate = Stable implementation
Node.js 18+
npm or yarn
# Clone the repository
git clone <your-repo-url>
cd algorate
# Install dependencies
npm install
# Build the project
npm run build
# Development with auto-reload
npm run dev
# Build TypeScript
npm run build
# Run built version
npm start
# Test with MCP Inspector (built version)
npm run inspect
# Test with MCP Inspector (dev version)
npm run inspect:dev
# Run examples
npm run example:simple
npm run example:sorting
# Run tests
npm testTest the server interactively with the MCP Inspector:
npm run inspectOr run the examples:
# Quick validation
npm run example:simple
# Complete benchmark with multiple implementations
npm run example:sortingSee TESTING_GUIDE.md for comprehensive testing instructions.
Add to .git/hooks/pre-commit:
#!/bin/bash
# Run quick benchmark validation
npm run example:simple
# GitHub Actions example
- name: Run Algorithm Benchmarks
run: |
npm install
npm run build
npm test
npm run example:sortingTesting Guide - Comprehensive testing and validation guide
Inspector Guide - MCP Inspector usage and tips
API Reference - Detailed tool documentation
Examples - Code examples and usage patterns
Algorithm Comparison: Compare 2+ implementations objectively
Performance Regression: Detect performance degradation in CI/CD
Code Optimization: Get specific recommendations for improvement
Learning: Understand algorithm performance characteristics
Benchmark Storage: Track performance over time and versions
Team Standards: Enforce performance baselines across teams
JavaScript (ES6+)
TypeScript
Python (3.7+)
import { Orchestrator } from "@cmiretf/algorate";
const orchestrator = new Orchestrator();
// 1. Register algorithm
const algo = orchestrator.registerAlgorithm("BubbleSort");
// 2. Register implementations
const impl1 = orchestrator.registerImplementation(
algo.id,
"Basic Implementation",
"javascript",
"function bubbleSort(arr) { /_ code _/ }",
"bubbleSort"
);
const impl2 = orchestrator.registerImplementation(
algo.id,
"Optimized Implementation",
"javascript",
"function bubbleSortOptimized(arr) { /_ code _/ }",
"bubbleSortOptimized"
);
// 3. Create test cases
const test = orchestrator.registerTestCase(
"Random array 1000 elements",
1000,
"array",
Array.from({ length: 1000 }, () => Math.random()),
"sorted array"
);
// 4. Run benchmark
const result = await orchestrator.runBenchmark(algo.id, test.id, {
warmupRuns: 3,
measurementRuns: 10,
validateOutput: true,
});
// 5. Get insights
const insights = await orchestrator.getAlgorithmInsights(algo.id);
console.log(insights); // Performance analysis and recommendationserror: Critical execution failures or validation errors
warning: Performance anomalies or high variability
info: Optimization suggestions and observations
Warmup Runs: Use 2-3 warmup runs to stabilize the JIT
Measurement Runs: 5-10 runs for reliable statistics
Consistent Environment: Close unnecessary applications
Large Inputs: Test with representative data sizes
Validation: Always validate correctness before measuring
See TESTING_GUIDE.md for detailed best practices.
Contributions are welcome! Please:
Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request
This project is licensed under the MIT License - an open source license that allows you to use, modify, and distribute this software freely.
✅ Free to use: You can use this software in any project, commercial or personal
✅ Open source: The source code is publicly available and can be inspected, modified, and improved
✅ Modify freely: You can adapt the code to fit your specific needs
✅ Distribute: You can share the original or modified versions
✅ Private use: You can use it in proprietary projects without disclosing your source code
Copyright (c) 2026 Carlos Miret Fiuza
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Node.js 18+
Deno (with appropriate configuration)
Browser environments (with bundling)
⚠️ Remote Code Execution Risk: This server compiles and runs JavaScript supplied by the MCP caller inside the Node.js process. Worker threads provide fault isolation, not a security sandbox — injected code still has full access to the filesystem, network, environment variables, and child processes of the user running the server. Treat this tool as you would a local REPL.
For safety, the tools that accept or execute caller-supplied code are disabled by default. Enable them only when the MCP client is fully trusted by setting the environment variable:
ALGORATE_ALLOW_CODE_EXECUTION=1Affected tools: register_implementation, run_benchmark, auto_benchmark, benchmark_all.
Example claude_desktop_config.json entry:
{
"mcpServers": {
"algorate": {
"command": "npx",
"args": ["-y", "@cmiretf/algorate-mcp"],
"env": {
"ALGORATE_ALLOW_CODE_EXECUTION": "1"
}
}
}
}Without this variable the execution tools respond with a safety error. Read-only tools (list_algorithms, get_results, query_performance, generate_chart, etc.) keep working.
Isolated Execution: Uses Worker threads to reduce fault blast radius (not a security boundary).
Timeout Protection: Prevents infinite loops and hanging processes.
Memory Monitoring: Tracks memory consumption during runs.
UUID-validated paths: generate_chart rejects non-UUID algorithmId / testCaseId to prevent path traversal into chart file writes.
Input Validation: All tool arguments are parsed with zod before use.
For issues, questions, or suggestions:
Open an issue on GitHub
Check TESTING_GUIDE.md for troubleshooting
Review INSPECTOR_GUIDE.md for MCP usage
This project is developed and maintained by Carlos Miret Fiuza.
Feel free to connect on LinkedIn for collaborations, suggestions, or any questions related to Algorate MCP Server!
Automatically run benchmarks for detected or registered algorithms
| Name | Required | Description | Default |
|---|---|---|---|
| algorithmIds | No | Algorithm IDs to benchmark (empty = all) | |
| forceRefresh | No | Force refresh even if cached results exist |
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 only says 'Automatically run benchmarks' without stating side effects, caching behavior, resource implications, or whether prior registration is required. The forceRefresh parameter hints at caching, but the description does not elaborate.
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 compact sentence with no filler words, and it front-loads the core action ('Automatically run benchmarks'). It is as short as possible, though it sacrifices necessary contextual details.
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 annotations, no output schema, and sibling tools that overlap in function, the description is incomplete. It does not explain what the tool returns, whether it writes results to storage, or how it relates to auto_detect_algorithms. An agent cannot fully judge consequences or prerequisites from this text alone.
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 input schema already documents both parameters clearly: algorithmIds ('empty = all') and forceRefresh ('Force refresh even if cached results exist'). With 100% schema description coverage, the tool description adds no additional parameter meaning, which matches the baseline score of 3.
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 a specific action ('Automatically run benchmarks') and a resource scope ('detected or registered algorithms'), so an agent can tell it is about running benchmarks. However, it does not differentiate from siblings like run_benchmark or benchmark_all, so it is clear but not fully distinctive.
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 instead of alternatives such as run_benchmark or benchmark_all. It does not mention exclusions or prerequisites, leaving the agent to guess the appropriate context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Automatically detect algorithms in project files
| Name | Required | Description | Default |
|---|---|---|---|
| directories | No | Directories to scan (default: src, examples) |
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 but only says 'Automatically detect algorithms in project files'. It does not state whether detection is read-only, whether results are returned or stored, or whether it integrates with register_algorithm. This leaves significant behavioral uncertainty.
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, front-loaded sentence with no filler words. It states the core action and target resource efficiently, making it highly scannable for an agent.
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?
The tool is simple in terms of parameters, but the definition omits what happens after detection—whether algorithms are registered, listed, or returned. Without an output schema or annotations, and with sibling tools like register_algorithm and auto_benchmark present, the description is incomplete for an agent to fully understand the tool's role.
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 input schema already describes the only parameter (directories, default src, examples) with 100% coverage, so the description adds no additional parameter meaning. According to the baseline for high schema coverage, a 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 uses the specific verb 'detect' with object 'algorithms in project files', which clearly suggests discovering algorithms rather than registering or listing them. It is distinguishable from siblings like register_algorithm and list_algorithms, though it does not explicitly differentiate itself by naming alternatives.
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 gives no guidance on when to use this tool versus alternatives such as auto_benchmark or register_algorithm. There are no prerequisites, exclusions, or contextual cues beyond the tool's name and one-line description, leaving usage largely to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Automatically detect all algorithms, run benchmarks, and return formatted results (ONE-CLICK BENCHMARK)
| Name | Required | Description | Default |
|---|---|---|---|
| filePath | No | Specific file path to analyze (optional - if provided, only analyzes this file) | |
| directories | No | Directories to scan (default: src, examples) | |
| forceRefresh | No | Force refresh even if cached results exist |
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 automatic detection, running benchmarks, and returning formatted results, but does not disclose whether the tool is read-only, whether it caches or modifies state, whether benchmark execution has side effects, or what the formatted results look like. The forceRefresh parameter hints at caching, but the description does not acknowledge it.
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 compact sentence that front-loads the core behavior: detection, benchmarking, and formatted results. The 'ONE-CLICK BENCHMARK' label adds useful positioning without wasting words.
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?
The tool has three optional parameters, no output schema, no annotations, and several closely related sibling tools, so the description needs to offer more context. It does not clarify what 'formatted results' means, which algorithms are detected, how caching and forceRefresh behave, or how this tool differs from auto_benchmark and auto_detect_algorithms.
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 baseline is 3 even though the tool description itself adds no parameter-level meaning. The description's phrase 'all algorithms' loosely aligns with the optional filePath and directories parameters, but it does not explain how they interact or refine the behavior.
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 a specific verb-resource combination: automatically detects algorithms, runs benchmarks, and returns formatted results. However, it does not explicitly distinguish itself from sibling tools like auto_detect_algorithms, run_benchmark, and auto_benchmark, which all overlap in 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 'ONE-CLICK BENCHMARK' tag and 'automatically detect all algorithms' phrase imply this is the all-in-one convenience tool, which gives some usage context. There are no explicit when-to-use or when-not-to-use instructions, nor any mention of alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate performance chart for benchmark results
| Name | Required | Description | Default |
|---|---|---|---|
| testCaseId | No | Test case ID (optional) | |
| algorithmId | Yes | Algorithm ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It does not mention whether the operation is read-only, what output format is returned (e.g., image, file, URL), or whether it depends on previously stored benchmark results. The description is too sparse to inform an agent about side effects or return behavior.
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 concise sentence with no waste or redundant phrasing. It is front-loaded with the main action, although it is so brief that it provides minimal context, which is penalized in other dimensions.
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?
With no output schema and no annotations, the description should explain what the chart represents, how the result is returned, and any prerequisites or relationships with sibling tools. None of this is present, leaving significant gaps for an agent trying to invoke the tool correctly.
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 (algorithmId and testCaseId). The description does not add any semantic detail beyond what the schema provides, but since the schema fully covers parameters, the baseline 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 states a specific verb ('Generate') and resource ('performance chart') for benchmark results, making the core action clear. It does not explicitly differentiate itself from siblings like get_results or query_performance, but the notion of a 'chart' implies visualization, which is distinct enough from raw result retrieval.
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?
There is no guidance on when to use this tool versus siblings such as run_benchmark, get_results, or query_performance. The description only says what it does, not when it is appropriate or what prerequisites exist (e.g., benchmarks must already be run).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get benchmark results for a specific implementation and test case
| Name | Required | Description | Default |
|---|---|---|---|
| testCaseId | Yes | Test case ID | |
| implementationId | Yes | Implementation ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. The verb 'Get' implies a read-only lookup, but the description provides no details about side effects, data availability, error behavior, or what happens if the benchmark results do not yet exist.
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 focused sentence with no filler, no repetition of the tool name, and the object of the operation is stated immediately. It is concise and to the point.
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 two-parameter lookup, the description is minimally adequate, but with no output schema and no annotations it leaves out useful context such as the shape of returned results, whether the benchmark must have been run beforehand, and how missing results are handled. This is a clear but not severe completeness gap.
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%, and the description loosely restates the two required parameters as 'implementation and test case' without adding new meaning. It does not clarify ID formats, sources, or expected values, so it adds only minimal value beyond the 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?
The description uses a specific verb ('Get') and a clear resource ('benchmark results') scoped to an implementation and test case, making the tool's core purpose obvious. However, it does not explicitly distinguish itself from siblings like query_performance or benchmark_all, so it stops short of full differentiation.
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?
No guidance is provided about when to use this tool versus alternatives such as query_performance, list_test_cases, or run_benchmark. There is no mention of preconditions, exclusions, or typical scenarios, so the agent must infer usage from the name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all registered algorithms
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. 'List all registered algorithms' clearly signals a read-only action and the scope, but it does not mention return format, ordering, pagination, or what happens if no algorithms are registered.
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, front-loaded sentence with no wasted words. Every word contributes to meaning.
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 zero-parameter, no-output-schema list operation, the description is nearly complete. It would be slightly improved by noting what fields or format are returned, but this is a minor gap given the tool's simplicity.
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 input schema has zero parameters and 100% schema coverage, so there is nothing for the description to add about parameters. The baseline of 4 applies here.
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 uses a specific verb 'List' and names the resource 'all registered algorithms', making the operation clear. It does not explicitly contrast with sibling tools like list_implementations, but the resource is unambiguous enough for an agent to distinguish it.
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 intended use is implied: call this when you need the set of registered algorithms. However, there is no explicit guidance about when not to use it or how it differs from related list-family tools, so usage guidance is only implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List implementations, optionally filtered by algorithm
| Name | Required | Description | Default |
|---|---|---|---|
| algorithmId | No | Filter by algorithm ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the basic listing action but does not disclose return format, default behavior when no filter is applied, ordering, pagination, or any side effects.
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 clear sentence with no unnecessary words. The main action and optional filter are front-loaded, making it easy to parse.
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 list tool with one optional parameter, the description is minimally adequate. However, with no output schema and no annotations, it would benefit from explaining what an implementation is, what fields are returned, and how filtering behaves.
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 baseline is 3. The description adds nothing beyond the schema: both say the parameter filters by algorithm ID. No additional semantics about match behavior or value format are provided.
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 uses a specific verb ('List') and resource ('implementations') and states the optional filter. It is clearly distinct from siblings like list_algorithms and list_test_cases, though it doesn't explicitly name them.
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 usage context is implied: this is the tool for listing implementations. However, it gives no explicit guidance on when to use this vs list_algorithms or other sibling tools, and no exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all registered test cases
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. 'List all' communicates a read-only, unfiltered operation, but it does not disclose details about ordering, pagination, size limits, or whether returned entries include full test-case definitions or only references.
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, front-loaded sentence with no wasted words. 'List', 'all', 'registered', and 'test cases' are all informative and directly support agent decision-making.
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 zero-parameter listing tool, this is nearly complete: it states the resource, scope, and action. The only gap is the lack of any return-structure detail, which is somewhat mitigated by the absence of output schema and the simplicity of the operation.
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 zero parameters, so the baseline is 4. The empty input schema and the description align; there are no parameter semantics to explain, and the description correctly adds nothing about parameters.
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 uses a specific verb 'List' and a clear resource 'registered test cases', with the scope word 'all' making it explicit. This distinguishes it from sibling listing tools like list_algorithms and list_implementations by naming the test-case resource directly.
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?
Usage is implied by the verb 'List': an agent can infer to use this when it needs all registered test cases. However, there is no explicit guidance about when not to use it or how it compares to sibling tools like register_test_case or list_algorithms.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Query performance analysis with automatic benchmark and summary
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Query about algorithm performance (e.g., "sorting algorithms", "all algorithms") | |
| directories | No | Directories to scan for algorithms | |
| forceRefresh | No | Force refresh even if cached results exist |
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 explaining behavior. It mentions "automatic benchmark" which hints that invoking the tool may run benchmarks, but it does not disclose potential side effects, whether cached results are used, whether forceRefresh writes new results, performance costs, or what the summary contains.
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 short and free of fluff, which is positive, but it is under-specified rather than efficiently complete. It is a single vague phrase that omits important details without earning its brevity through precise wording.
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?
With no output schema, no annotations, three parameters, and many related sibling tools, the description is insufficiently complete. It does not clarify the return value, the difference from run_benchmark/get_results, or whether automatic benchmarking creates side effects, leaving an agent without enough context to invoke it confidently.
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. The description adds no parameter-level detail beyond the schema, but that is acceptable because the schema is fully self-describing; baseline 3 applies.
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 says "Query performance analysis with automatic benchmark and summary," which gives a general sense that the tool analyzes performance and runs benchmarks, but it is phrased as a noun phrase rather than a clear verb+resource action. It does not distinguish this tool from siblings like run_benchmark, auto_benchmark, or get_results, all of which could plausibly perform similar work.
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?
There is no guidance about when to use query_performance versus the many related tools such as run_benchmark, benchmark_all, auto_benchmark, or get_results. The schema's query parameter implies it is for asking performance questions about algorithms, but the description itself does not state usage context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Register a new algorithm to benchmark
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Algorithm name | |
| category | No | Algorithm category | |
| description | No | Algorithm description |
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 only says 'register,' which suggests a mutating operation, but it does not disclose side effects, whether duplicates are rejected, whether the registration persists, or what happens after registration.
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 short sentence with no filler, but it is under-specified rather than genuinely concise. It front-loads the core action but omits behavioral and contextual details that would make the definition useful.
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 registration tool with no annotations and no output schema, the description is too thin. It does not explain the registration workflow, required inputs beyond the obvious, potential error conditions, or how it differs from auto_detect_algorithms and register_implementation.
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 adequately. The description adds no parameter-specific meaning beyond the schema, which is acceptable given full schema coverage, but it does not compensate or elaborate on parameter relationships.
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 a clear verb ('register') and resource ('algorithm') and adds the purpose 'to benchmark.' This distinguishes it from sibling tools like register_implementation and register_test_case by resource type, though it relies on the tool name for full differentiation.
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?
There is no guidance on when to use this tool versus alternatives such as auto_detect_algorithms or register_implementation. The description implies the action but provides no context about prerequisites, intended workflow, or situations where a sibling tool would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Register an implementation of an algorithm
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | Implementation code | |
| name | Yes | Implementation name | |
| language | Yes | Programming language | |
| algorithmId | Yes | Algorithm ID | |
| description | No | Implementation description | |
| entryFunction | Yes | Entry function name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry the behavioral burden. 'Register' implies a persistent or mutating action, but the description does not disclose whether existing implementations are overwritten, whether the algorithm ID must already exist, whether validation is performed, or what the tool returns.
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, front-loaded sentence with no redundant wording. It earns conciseness, but it is so brief that it leaves important context unstated; conciseness is good without sacrificing necessary 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 six-parameter registration tool with no annotations and no output schema, the description is too sparse: it omits side effects, prerequisites, relation to register_algorithm, and expected return value. The schema's field descriptions help parameter-level understanding but do not fill the missing 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?
The input schema already describes all six parameters at 100% coverage, so the description does not need to repeat their meaning. It also does not add clarifying context such as how algorithmId, code, and entryFunction relate, but the baseline of 3 is appropriate because the schema carries parameter documentation.
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 identifies a specific action ('Register') and resource ('implementation of an algorithm'), which is more informative than the bare tool name and distinguishes it from register_algorithm and register_test_case. However, it does not explain what 'register' means operationally, so it stops short of a fully specific definition.
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?
There is no guidance on when to use this tool versus alternatives such as register_algorithm or run_benchmark, and no mention of prerequisites like the referenced algorithm already existing. The description states only what the tool does, never when or when not to invoke it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Register a test case for benchmarking
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Test case name | |
| input | Yes | Test input data | |
| inputSize | Yes | Input size | |
| inputType | Yes | Input type | |
| description | No | Test case description | |
| expectedOutput | No | Expected output for validation |
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 only says 'Register', which implies a mutation, but does not disclose whether existing test cases are overwritten, what validation occurs, what side effects result, or what the tool returns. This is a significant transparency gap for a registration tool.
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, front-loaded sentence with no filler or repetition. Every word earns its place, and it is appropriately sized for a tool whose schema already documents the parameters.
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?
The tool has six parameters, four required, no output schema, and no annotations, so the description should compensate with richer context. It does not explain registration semantics, side effects, required relationships between parameters, return behavior, or when registration is valid. The description is too minimal for an agent to invoke this tool with confidence.
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 documents all six parameters individually. The description adds no extra parameter-level meaning beyond the schema, which matches the baseline expectation of 3 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 uses a specific verb ('Register') with a clear resource ('test case') and states the purpose ('for benchmarking'). It is unambiguous and clearly distinguishes this tool from sibling tools like register_algorithm and register_implementation.
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 phrase 'for benchmarking' implies the tool is used when adding a test case to the benchmark suite, but there is no explicit guidance about when to use this tool versus alternatives, nor any exclusions or prerequisites. Usage context is only implied, not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run benchmark comparing all implementations of an algorithm
| Name | Required | Description | Default |
|---|---|---|---|
| timeoutMs | No | Timeout per execution in ms | |
| testCaseId | Yes | Test case ID | |
| warmupRuns | No | Number of warmup runs | |
| algorithmId | Yes | Algorithm ID | |
| validateOutput | No | Validate output against expected | |
| measurementRuns | No | Number of measurement runs |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of behavioral disclosure. It only says 'Run benchmark comparing all implementations' and does not disclose whether results are stored, whether existing registrations are required, whether output validation is performed, or what side effects occur.
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 concise sentence that front-loads the core action and scope. It wastes no words, though it arguably under-specifies the tool's behavior, which is penalized in other dimensions rather than here.
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 an execution tool with 6 parameters and no output schema or annotations, this description is incomplete. It does not mention what the benchmark returns, how results are retrieved (e.g., via get_results), whether registrations are prerequisite, or how to handle failures. An agent could invoke it but would be guessing about the outcome.
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 parameters are already well documented in the schema. The description adds no additional meaning about timeoutMs, warmupRuns, measurementRuns, or validateOutput, but it does not need to since the schema handles this; 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 states a specific action ('Run benchmark') and a specific resource ('all implementations of an algorithm'), making it clear this tool targets a single algorithm's implementations. This also differentiates it from siblings like benchmark_all (likely broader) and list_algorithms/get_results (non-execution 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?
No guidance is provided about when to use this tool versus alternatives such as benchmark_all, auto_benchmark, or query_performance. The description implies a usage context but does not state prerequisites, exclusions, or conditions that would route an agent to a sibling.
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.
v1.0.5auto_benchmarkauto_detect_algorithmsbenchmark_allgenerate_chartget_resultslist_algorithmslist_implementationslist_test_casesquery_performanceregister_algorithmregister_implementationregister_test_caserun_benchmarkScored across 13 tools
The registration and listing tools are clearly distinct, but run_benchmark, auto_benchmark, query_performance, and benchmark_all all trigger benchmarking with only subtle differences in scope and output. An agent could easily select the wrong high-level tool without carefully reading each description.
Most tools follow a predictable snake_case verb_noun pattern such as register_algorithm, list_test_cases, and generate_chart. The auto_ prefix and benchmark_all break the pattern slightly, but the naming convention remains mostly coherent and readable.
13 tools is a reasonable size for an algorithm benchmarking server that covers registration, execution, listing, results, and visualization. A few overlapping benchmark/query tools could be consolidated, but the count is not excessive or thin.
The core workflow is well covered: register algorithms/implementations/test cases, run benchmarks, list entities, fetch results, auto-detect, chart, and run one-click benchmarks. Missing delete/update operations for registered entities and single-entity getters are minor gaps that agents can likely work around.
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