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., "@MCP Utility Tools cache the GitHub API response for my repos with a 5-minute TTL"
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 collection of utility tools for the Model Context Protocol (MCP) that provide caching, retry logic, batch operations, and rate limiting capabilities to enhance any MCP-based workflow.
🔄 Retry with Exponential Backoff - Automatically retry failed operations with configurable delays
💾 TTL-based Caching - Cache expensive operations with automatic expiration
🚀 Batch Operations - Process multiple operations in parallel with concurrency control
🚦 Rate Limiting - Prevent API abuse with sliding window rate limiting
🔍 Full TypeScript Support - Type-safe with comprehensive TypeScript definitions
Related MCP server: slop-mcp
npm install mcp-utility-tools
# or with yarn
yarn add mcp-utility-tools
# or with bun
bun add mcp-utility-toolsAdd the utility tools to your Claude Desktop configuration:
{
"mcpServers": {
"utility-tools": {
"command": "npx",
"args": ["mcp-utility-tools"]
}
}
}Once configured, Claude can use these tools to enhance any workflow:
# Check cache before expensive operation
cache_result = mcp_cache_get(key="api-response", namespace="github")
if not cache_result["found"]:
# Fetch data with retry
response = fetch_with_retry("https://api.github.com/user/repos")
# Cache for 5 minutes
mcp_cache_put(
key="api-response",
value=response,
ttl_seconds=300,
namespace="github"
)Retry operations with exponential backoff and jitter.
{
"tool": "retry_operation",
"arguments": {
"operation_id": "unique-operation-id",
"operation_type": "http_request",
"operation_data": {
"url": "https://api.example.com/data",
"method": "GET"
},
"max_retries": 3,
"initial_delay_ms": 1000
}
}Features:
Tracks retry attempts across multiple calls
Exponential backoff with configurable delays
Optional jitter to prevent thundering herd
Prevents duplicate retries for successful operations
Retrieve values from cache with TTL support.
{
"tool": "cache_get",
"arguments": {
"key": "user-data-123",
"namespace": "users"
}
}Store values with automatic expiration.
{
"tool": "cache_put",
"arguments": {
"key": "user-data-123",
"value": { "name": "John", "role": "admin" },
"ttl_seconds": 300,
"namespace": "users"
}
}Features:
Namespace support to prevent key collisions
Automatic cleanup of expired entries
Configurable TTL (1 second to 24 hours)
Memory-efficient storage
Process multiple operations with controlled concurrency.
{
"tool": "batch_operation",
"arguments": {
"operations": [
{ "id": "op1", "type": "fetch", "data": { "url": "/api/1" } },
{ "id": "op2", "type": "fetch", "data": { "url": "/api/2" } },
{ "id": "op3", "type": "fetch", "data": { "url": "/api/3" } }
],
"concurrency": 2,
"timeout_ms": 5000,
"continue_on_error": true,
"use_cache": true
}
}Features:
Configurable concurrency (1-20 operations)
Per-operation timeout
Continue or fail-fast on errors
Optional result caching
Maintains order of results
Implement sliding window rate limiting.
{
"tool": "rate_limit_check",
"arguments": {
"resource": "api.github.com",
"max_requests": 60,
"window_seconds": 60,
"increment": true
}
}Features:
Per-resource tracking
Sliding window algorithm
Automatic reset after time window
Check without incrementing option
// Cache GitHub API responses
async function getRepositoryWithCache(owner: string, repo: string) {
const cacheKey = `github:${owner}/${repo}`;
// Check cache first
const cached = await mcp_cache_get({
key: cacheKey,
namespace: "github"
});
if (cached.found) {
return cached.value;
}
// Fetch with retry
const data = await retryableGitHubCall(owner, repo);
// Cache for 10 minutes
await mcp_cache_put({
key: cacheKey,
value: data,
ttl_seconds: 600,
namespace: "github"
});
return data;
}// Rate-limited Slack notifications
async function sendSlackNotifications(messages: string[], channel: string) {
for (const message of messages) {
// Check rate limit
const canSend = await mcp_rate_limit_check({
resource: `slack:${channel}`,
max_requests: 10,
window_seconds: 60,
increment: true
});
if (!canSend.allowed) {
console.log(`Rate limited. Retry in ${canSend.reset_in_seconds}s`);
await sleep(canSend.reset_in_seconds * 1000);
}
await mcp_slack_post_message({
channel_id: channel,
text: message
});
}
}┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ Claude/Client │────▶│ MCP Utility Tools│────▶│ Cache Storage │
│ │ │ │ │ (In-Memory) │
└─────────────────┘ └──────────────────┘ └─────────────────┘
│ │
│ │
▼ ▼
┌─────────────────┐ ┌──────────────────┐
│ Other MCP │ │ Retry/Rate │
│ Servers │ │ Limit Tracking │
└─────────────────┘ └──────────────────┘# Clone the repository
git clone https://github.com/haasonsaas/mcp-utility-tools.git
cd mcp-utility-tools
# Install dependencies
npm install
# Build the project
npm run build
# Run tests
npm test
# Run in development mode
npm run devRun the comprehensive test suite:
# Unit tests
npm test
# Integration tests with test harness
npm run test:integration
# Test with MCP Inspector
npx @modelcontextprotocol/inspector build/index-v2.jsWe welcome contributions! Please see our Contributing Guide for details.
🔌 Storage Backends: Add Redis, SQLite support
🔧 New Tools: Circuit breakers, request deduplication
📊 Metrics: Add performance tracking and analytics
🌐 Examples: More integration examples with other MCP servers
MIT © Jonathan Haas
Built on top of the Model Context Protocol SDK by Anthropic.
Process multiple operations with configurable concurrency and error handling
| Name | Required | Description | Default |
|---|---|---|---|
| operations | Yes | Array of operations to process | |
| concurrency | No | Maximum number of concurrent operations | |
| timeout_ms | No | Timeout per operation in milliseconds | |
| continue_on_error | No | Continue processing even if some operations fail | |
| use_cache | No | Cache successful results | |
| cache_ttl_seconds | No | TTL for cached results |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must carry full burden. It mentions concurrency and error handling but fails to disclose ordering, atomicity, side effects, or detailed failure behavior beyond schema defaults.
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?
Single sentence, no waste, but could benefit from more structure or front-loading key differentiators. Appropriate length given tool 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?
Tool has 6 parameters, nested schema, no output schema, and no annotations. Description is minimal and leaves out return value, error result format, and interactions with sibling tools, making it incomplete for effective selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for all parameters. Description adds high-level context but does not enhance understanding of any specific parameter beyond what schema already provides.
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?
Description clearly states it processes multiple operations with configurable concurrency and error handling, distinguishing it from sibling tools like cache_clear, cache_delete, etc., which serve different purposes.
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?
Implied usage from the general description but no explicit guidance on when to use batch_operation over alternatives like retry_operation or individual calls. Lacks when-not and exclusion context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Clear all entries from the cache or a specific namespace
| Name | Required | Description | Default |
|---|---|---|---|
| namespace | No | Clear only this namespace, or all if not specified |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only states it clears entries, but does not disclose destructiveness, auth requirements, rate limits, or side effects. The burden is entirely on the description, which 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?
Single sentence is concise but front-loads the core action; however, it omits potentially useful details like return value or confirmation. Adequate but not exceptional.
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 simplicity (1 optional param, no output schema), the description is minimally complete. It covers the basic operation but lacks any additional context that would aid an agent in understanding implications.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description adds no extra meaning beyond the schema's own description. Baseline score of 3 applies as schema already explains the parameter.
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?
Description clearly states the tool clears cache entries, either all or a specific namespace, distinguishing it from siblings like cache_delete which likely targets individual entries.
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 when-to-use or when-not guidance; lacks mention of alternatives or prerequisites. Agent has no context on when cache_clear is appropriate versus cache_delete.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete a key from the cache
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | Cache key to delete | |
| namespace | No | Optional namespace | default |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It only states the operation (delete) but does not disclose behavioral traits such as idempotency, persistence, error handling (e.g., behavior on non-existent key), or any constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise (one sentence) and front-loaded, but it is under-specified. While it gets to the point, it lacks depth that could be added without significant length.
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 two parameters and no output schema or annotations, the description should provide more context about behavior (e.g., namespacing, return value, effects). It is incomplete for a mutation tool.
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 100% description coverage for both parameters. The description adds no additional meaning beyond 'Delete a key from the cache', which is already implied. 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 'Delete a key from the cache' clearly states the verb (delete) and resource (key from cache). It is distinguishable from siblings like cache_clear (which likely clears all keys) and cache_get/put, but does not explicitly differentiate.
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 on when to use this tool versus alternatives (e.g., cache_clear for deleting all keys, or batch_operation for multiple operations). There are no exclusions or context for when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a value from the cache by key. Returns null if not found or expired.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | Cache key to retrieve | |
| namespace | No | Optional namespace to prevent key collisions | default |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses return null on missing/expired keys, which addresses key behavioral trait. No annotations provided, so description carries full burden; adequate for a simple read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with purpose front-loaded. No wasted words; every sentence adds value.
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 cache get tool, description covers purpose, return behavior, and key parameter. Could mention TTL expiration mechanism, but not critical given simplicity. No output schema needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. Description does not add meaning beyond schema descriptions; 'Cache key' and 'Optional namespace' are already clear from 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?
Description clearly states 'Get a value from the cache by key' and distinguishes from siblings like cache_put and cache_delete. Specific verb+resource with additional behavior (returns null if not found/expired).
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?
Clear when to use (retrieve cached value) and what to expect (null if missing/expired). Lacks explicit when-not-to-use or alternatives, but sibling names provide implicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Store a value in the cache with TTL. Useful for caching API responses, computed values, etc.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | Cache key | |
| value | Yes | Value to cache (any JSON-serializable data) | |
| ttl_seconds | No | Time to live in seconds | |
| namespace | No | Optional namespace to prevent key collisions | default |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It mentions TTL and examples but omits details like overwrite behavior, error handling, or what happens upon expiration.
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?
Two sentences, no unnecessary words. Information is front-loaded and every part contributes.
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 4 parameters, no output schema, and no annotations, the description is adequate but lacks details on return value or error conditions. Could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds no extra meaning beyond what the schema already documents (e.g., default TTL, namespace optionality).
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 action ('Store') and resource ('cache'), and specifies the TTL feature. It distinguishes from siblings like cache_get, cache_delete, and cache_clear by emphasizing storage with expiration.
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 examples of use cases ('caching API responses, computed values') but does not explicitly state when to use this tool over alternatives or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check if an operation should be rate-limited
| Name | Required | Description | Default |
|---|---|---|---|
| resource | Yes | Resource identifier (e.g., 'api.github.com') | |
| max_requests | No | Maximum requests allowed | |
| window_seconds | No | Time window in seconds | |
| increment | No | Increment the counter if allowed |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description implies a read-only check ('check if'), but the increment parameter can mutate state. Without annotations, this discrepancy is not disclosed.
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?
Single sentence is concise but under-specified for a 4-parameter tool. Lacks structured 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?
No output schema; description does not explain return value (e.g., boolean or status). Incomplete for understanding tool behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline 3. Description adds no extra meaning beyond parameter names and default values.
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 'Check if an operation should be rate-limited' clearly states the verb (check) and resource (operation), but does not differentiate from siblings like retry_operation.
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 on when to use this tool vs alternatives (e.g., retry_operation). The description lacks context for appropriate invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retry an operation with exponential backoff. Use this for operations that might fail temporarily (API calls, network requests, etc.)
| Name | Required | Description | Default |
|---|---|---|---|
| operation_id | Yes | Unique identifier for this operation (used for tracking retries) | |
| operation_type | Yes | Type of operation being retried | |
| operation_data | Yes | Data specific to the operation (e.g., URL for HTTP, query for DB) | |
| max_retries | No | ||
| initial_delay_ms | No | ||
| should_execute | No | If false, just returns retry metadata without executing |
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 exponential backoff but lacks details on behavior after all retries, success/failure outcomes, synchronicity, or side effects. Overall, it offers minimal behavioral context.
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?
Two concise sentences with no redundancy. The first sentence states the core function, and the second provides use context. All content earns its place.
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?
Without an output schema, the description should cover more behavioral aspects. It explains purpose and when but omits details like retry delay formula, return format, and error handling. This is adequate but not complete for a tool with 6 parameters and no annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no information about parameters beyond what the input schema provides. With 67% schema description coverage, the description should supplement missing parameter descriptions for max_retries and initial_delay_ms, but it does not, leaving gaps.
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 defines the tool's purpose: retrying operations with exponential backoff for temporary failures. It includes examples of use cases (API calls, network requests), making it specific and not a tautology.
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 guidance on when to use ('operations that might fail temporarily'), implying not for permanent failures. However, it does not explicitly state when not to use or suggest alternatives among sibling tools like cache_* or rate_limit_check.
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.
batch_operationcache_clearcache_deletecache_getcache_putrate_limit_checkretry_operationEach tool targets a distinct utility operation: caching operations (get, put, delete, clear), batch processing, retry logic, and rate limiting. There is no overlap or ambiguity among them.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., cache_get, retry_operation). The naming is predictable and uniform across the set.
With 7 tools covering caching, batching, retry, and rate limiting, the count is well-scoped for a utility toolkit. No tool feels extraneous or missing.
The set covers essential utility operations: complete CRUD for cache, plus batch, retry, and rate limiting. Minor gaps exist (e.g., no locking or logging), but the core domain is well addressed.
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