Docs for hot-module-reload and reactive programming for Python (hmr on PyPI)
A brief and concise explanation of the hmr library.
This tool provides information on how to use reactive programming or use hot module reloading in Python. As long as the user mentions HMR / Reactive Programming, this tool must be called first! Don't manually view the resource, call this tool instead.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already communicates the safe read-only nature. The description adds that it is a 'brief and concise explanation,' which hints at output size, but it does not disclose other behavioral traits like rate limits, auth requirements, or return format. It does not contradict the annotation, but adds limited value beyond 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 very concise: two short sentences plus a directive. It is front-loaded with the purpose, then gives usage guidelines and an alternative. Every sentence earns its place—no filler or repetition.
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 simplicity (0 params, no output schema, read-only annotation), the description covers the core facts: what it does, when to use it, and the nature of the output ('brief and concise explanation'). It does not explicitly state the output format, but 'explanation' strongly implies textual content. For this complexity level, it is sufficiently 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?
The tool has zero parameters, and the empty schema fully covers this. Per the rubric, a 0-parameter tool gets a baseline of 4. The description does not need to explain parameters, as there are none.
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 explicitly states it is 'A brief and concise explanation of the hmr library' and that it provides information on reactive programming/HMR in Python. This is a specific verb-resource pairing that distinguishes it from sibling tools like view-hmr-core-sources and view-hmr-unit-tests, which are likely source/tests viewers. The instruction to call this tool first further sets it apart.
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 a clear when-to-use condition: 'As long as the user mentions HMR / Reactive Programming, this tool must be called first!' It also provides an alternative: 'Don't manually view the resource, call this tool instead.' However, it does not explicitly name sibling tools or describe when not to use this tool beyond the manual-viewing alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The full source code (core only) of the HMR library.
Always call learn-hmr-concepts to learn the core concepts before calling this tool. These files are the full source code of the HMR library, which would be very helpful because good code are self-documented. For a brief and concise explanation, please refer to the hmr-docs://about MCP resource. Make sure you've read it before calling this tool. To learn how to use HMR for reactive programming, read the unit tests later. The response is identical to the MCP resource with the same name. Only use it once and prefer this tool to that resource if you can choose.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given the readOnlyHint annotation, the bar is lower, but the description adds useful operational context: the response is identical to the MCP resource, the tool should be used only once, and it should be preferred over the resource. These details go beyond the annotation and help set agent expectations.
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 somewhat verbose and mixes multiple instructions without clear separation. The first sentence states the core purpose well, but the subsequent sentences about ordering and alternatives could be more tightly organized.
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 parameterless, read-only tool, the description provides sufficient context: what the tool returns, how to sequence it with other resources, and its relationship to alternatives. The lack of an output schema makes the description's naming of the identical resource helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description correctly adds no parameter details, as there is nothing to explain beyond the schema's empty properties.
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 identifies the tool's purpose as providing the full core source code of the HMR library. It distinguishes itself from siblings like unit tests and basics by emphasizing 'core only.' While the phrasing is a noun phrase rather than an explicit verb, the intent is unambiguous.
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 explicit sequencing: call learn-hmr-concepts first, read hmr-docs://about, and use the tool only once. It also states a preference for this tool over the named MCP resource. However, it references 'learn-hmr-concepts' while the sibling list shows 'learn-hmr-basics,' which could be an inconsistency.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The unit tests (code examples) for HMR.
Always call learn-hmr-basics and view-hmr-core-sources to learn the core functionality before calling this tool. These files are the unit tests for the HMR library, which demonstrate the best practices and common coding patterns of using the library. You should use this tool when you need to write some code using the HMR library (maybe for reactive programming or implementing some integration). The response is identical to the MCP resource with the same name. Only use it once and prefer this tool to that resource if you can choose.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations declare readOnlyHint=true, so the tool is known to be safe. The description adds that the response is identical to the MCP resource of the same name and that it should be used only once, offering behavioral context beyond the annotation.
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 three sentences long, front-loaded with the core purpose. Every sentence adds value: definition, prerequisite/usage context, and response/usage caveats.
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 simplicity and read-only annotation, the description covers its purpose, prerequisites, usage conditions, and relationship to the resource. The statement about the response being identical to the resource compensates for the absent output schema.
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 no parameters, so the empty schema is entirely complete (100% coverage). The description adds no parameter details because none are needed, aligning with the baseline for zero-parameter tools.
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 precisely that the tool provides 'the unit tests (code examples) for HMR', and differentiates itself from the sibling tools by specifying that it contains code examples demonstrating best practices. This clearly identifies the resource being accessed.
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?
It explicitly instructs to always call `learn-hmr-basics` and `view-hmr-core-sources` first, and states when to use this tool: 'when you need to write some code using the HMR library'. It also advises to use it only once and prefer it over the equivalent resource, providing clear selection guidance.
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.
learn-hmr-basicsview-hmr-core-sourcesview-hmr-unit-testsClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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Hot Module Reload (HMR) for Python with reactive programming and MCP tools
Docs for agent-manager, the terminal UI that runs AI coding agents as live tmux sessions.
Squire Docs — the durable, attributed spec layer for agentic development.
Medius docs: the binary control protocol, device behavior, and the medius Rust library.
Add one secure layer between your agents and this server.
Each tool provides a distinctly different type of content: conceptual overview, source code, and usage examples. The purposes are clear, though the distinction between viewing core sources and unit tests is somewhat subtle as both are code.
All tool names follow a consistent verb-noun pattern with hyphens (learn-hmr-basics, view-hmr-core-sources, view-hmr-unit-tests), making the naming predictable and easy to understand.
With 3 tools, the server is well-scoped for its purpose of educating users about the HMR library. Each tool covers a necessary aspect - explanation, implementation, and examples - without any redundancy.
The server provides a complete learning path for the HMR library: a conceptual introduction, the full core source code, and unit tests demonstrating usage. There are no obvious gaps in this educational scope.
