Provides tools for working with Markdown documents, including reformatting content to Markdown format and processing Markdown-based documents through the MCP server's resource and prompt system.
Enables Python-based implementation of the MCP server with tools for document management, resource handling, and prompt execution within a Python runtime environment.
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 Chat CLI format the quarterly report in markdown"
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 command-line chat interface that connects to an MCP server using the Anthropic API. Built while working through Anthropic's Introduction to Model Context Protocol course, extended with custom tools, resources, and prompts.
MCP (Model Context Protocol) is an open standard for connecting AI models to external tools and data sources. This project implements both sides of that connection — a FastMCP server that exposes documents as resources and defines tools for reading and editing them, and a client that connects to the server and makes those capabilities available inside a chat interface.
The server defines:
Tools — read and edit documents
Resources — list all documents or fetch a specific one by URI
Prompts — reformat a document to markdown, or summarize its contents
The client implements the full MCP client session, including tool calls, resource reads, prompt retrieval, and command autocompletion.
Related MCP server: MCP Chat
Starting from a course starter pack, I implemented the missing pieces on both sides:
read_resource, list_prompts, and get_prompt on the client
Resource endpoints (docs://documents and docs://documents/{doc_id}) on the server
Two prompts (format and summarize) that instruct the model to use the available tools
The main thing this project made concrete for me is the separation between the server (which defines what's available) and the client (which knows how to call it) — and how prompts are just structured messages that give the model a starting context, not magic.
Python 3.9+
Anthropic API key
Clone the repo and navigate into the project folder.
Create a virtual environment and activate it:
uv venv
.venv\Scripts\activate # Windows
source .venv/bin/activate # Mac/LinuxInstall dependencies:
uv pip install -e .Create a .env file in the project root:
ANTHROPIC_API_KEY="your-key-here"Run the app:
uv run main.pyType a message to chat. Use @doc_id to include a document in your query, and /command to trigger a prompt. Tab autocompletes available commands.
> Tell me about @deposition.md
> /summarize report.pdf
> /format plan.mdTo add your own documents, edit the docs dictionary in mcp_server.py.
mcp dev mcp_server.pyThis opens the MCP Inspector in your browser where you can test tools, resources, and prompts without the chat interface.
mcp_chat_cli/
├── main.py # entrypoint
├── mcp_server.py # FastMCP server — tools, resources, prompts
├── mcp_client.py # MCP client session wrapper
├── core/
│ ├── chat.py # chat loop logic
│ ├── claude.py # Anthropic API integration
│ ├── cli.py # CLI setup and input handling
│ ├── cli_chat.py # connects CLI and chat
│ └── tools.py # tool call handling
├── .env # API key (not committed)
└── pyproject.toml # dependenciesEdit a document by replacing a string in the documents content with a new string.
| Name | Required | Description | Default |
|---|---|---|---|
| doc_id | Yes | Id of the document that will be edited | |
| old_str | Yes | The text to replace. Must match exactly, including whitespace. | |
| new_str | Yes | The new text to insert in place of the old text. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must disclose behavioral traits. It only states 'replacing a string' without indicating whether it replaces all occurrences, behavior on missing string, or side effects like auto-save. Missing important mutation details.
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 sentence that is concise and front-loaded with the essential action and resource. No wasted 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?
For a simple edit tool with fully documented parameters, the description is adequate but lacks details on error handling (e.g., what if old_str not found) and whether it replaces all occurrences or just the first.
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 adds value by clarifying that old_str must match exactly including whitespace. This goes beyond the schema's basic descriptions.
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 (edit), resource (document), and method (string replacement). It effectively distinguishes from the sibling tool 'read_doc_contents' which is read-only.
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 does not explicitly state when to use this tool vs alternatives. While the sibling tool name implies a read vs. edit distinction, no further guidance is provided on prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read the contents of a document and return it as a string.
| Name | Required | Description | Default |
|---|---|---|---|
| doc_id | Yes | Id of the document to read |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Only states basic read operation; no mention of error handling, read-only guarantee, or behavior on invalid inputs.
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 redundant information. Could be improved with additional context without harming conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, description mentions return value as string, which is helpful. Lacks details on error conditions or edge cases, but adequate for a simple read 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?
Schema description coverage is 100% for the single parameter; description does not add additional meaning 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?
Description clearly states the action (read), resource (document), and output format (string). Distinguishes from sibling tool 'edit_document' which implies modification.
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 versus the sibling 'edit_document'. Does not specify any prerequisites or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0edit_documentread_doc_contentsThe two tools have completely distinct purposes: one reads document contents, the other edits by replacing text. There is no functional overlap.
Both tool names follow the verb_noun pattern (edit_document, read_doc_contents), with consistent snake_case and clear action-object structure.
With only two tools, the server feels thin for a document utility, though it could be a minimal implementation. It falls into the borderline category (1-2 tools).
The server lacks basic CRUD operations like create and delete documents. While read and edit are provided, agents cannot create new documents or remove them, which are significant gaps.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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