Provides tools for searching and retrieving Python Enhancement Proposals (PEPs), including the ability to list active PEPs and fetch document excerpts or full content from the official Python PEP index.
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., "@PEP MCP Server Search for PEPs related to type hinting"
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.
MCP server that exposes on-demand Python PEP lookup tools backed by the live PEP index.
list_peps: list only active PEPs
search_peps: search active PEP titles
get_pep: fetch a PEP document by number, optionally returning focused
excerpts for a query
Related MCP server: PyPI Package MCP Server
PEP index JSON: https://peps.python.org/api/peps.json
PEP content:
https://raw.githubusercontent.com/python/peps/main/peps/pep-XXXX.rst
Fallback: https://github.com/python/peps/blob/main/peps/pep-XXXX.rst?plain=1
python -m venv .venv
.venv/bin/pip install -e ".[dev]".venv/bin/pep-mcp-serveror:
.venv/bin/python -m pep_mcp_serverBuild and tag:
docker build -t pep-mcp-server:latest .MCP uses stdio, so the container must keep stdin open (-i). Example:
docker run --rm -i pep-mcp-server:latestYou do not put PEP or GitHub URLs in mcp.json. Cursor only needs the command that runs the server; listing and fetching PEPs happens inside the process when tools run.
Use -i (required for stdio). Optional -e lines silence the startup banner and pin transport:
{
"mcpServers": {
"pep": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"-e",
"FASTMCP_TRANSPORT=stdio",
"-e",
"FASTMCP_SHOW_SERVER_BANNER=false",
"pep-mcp-server:latest"
]
}
}
}If the image is not on this machine yet, build it once from the project directory (see above).
If the MCP log shows Found 0 tools but listOfferingsForUI / Not connected warnings, that is often a Cursor UI race or a separate UI listing path; try reloading the window or invoking a tool from chat. The server still exposes three tools over stdio (verified with the MCP Python client).
list_peps() -> list[dict]Returns active PEPs with:
number
title
type
topic
created
url
(status is omitted; every row is active.)
search_peps(query: str) -> list[dict]Case-insensitive substring search on active PEP titles.
get_pep(pep, query=None, max_full_content_chars=None) -> dictAccepts 8, 0008, or pep-0008.
Returns metadata and:
content when query is not provided (capped by default for token efficiency)
excerpt when query is provided and matches
content fallback when query has no matches (also capped by default)
Optional max_full_content_chars: omit or None for the default cap; use 0 for the full document (can be very large).
.venv/bin/pytest -qGet a PEP by number. Use query for excerpts; full body is capped by default.
Pass max_full_content_chars=0 for an uncapped full document (can be very large).
| Name | Required | Description | Default |
|---|---|---|---|
| pep | Yes | ||
| query | No | ||
| max_full_content_chars | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses critical behavioral traits: full body is capped by default, setting max_full_content_chars=0 removes the cap, and uncapped documents 'can be very large.' Does not mention rate limits or auth requirements.
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?
Three sentences with zero waste. Front-loaded with core purpose ('Get a PEP by number'), followed by usage guidance for specific parameters. Every sentence 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?
Given the output schema exists, the description appropriately omits return value details. With 0% schema coverage, it compensates by explaining parameter semantics. Could strengthen by explicitly contrasting with list_peps for enumeration use cases.
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 0%, requiring description to compensate. Successfully adds meaning for all three parameters: 'pep' is referenced as 'by number,' 'query' is for 'excerpts,' and 'max_full_content_chars' controls capping behavior with explicit warning about large document sizes.
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?
States specific verb ('Get') + resource ('PEP') + identifier method ('by number'). The mention of using 'query for excerpts' effectively distinguishes this retrieval tool from the sibling search_peps and list_peps 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?
Provides clear guidance on when to use the 'query' parameter (for excerpts) versus full retrieval, and explains the 'max_full_content_chars' parameter behavior. Lacks explicit guidance on when to prefer search_peps over this tool for search scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List active Python PEPs with compact metadata.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses behavioral traits by specifying 'active' PEPs only and 'compact metadata' output format, but fails to state safety properties (read-only, non-destructive) or pagination 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?
Single sentence, front-loaded with verb, no redundant words. Appropriate length for a zero-parameter listing tool.
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 presence of an output schema (which handles return value documentation) and zero input parameters, the description is sufficiently complete. Mentions 'compact metadata' to set expectations, though could note pagination if applicable.
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?
Input schema contains zero parameters. Per evaluation rules, zero-parameter tools receive a baseline score of 4.
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?
States specific verb ('List'), resource ('Python PEPs'), and scope ('active', 'compact metadata'). The 'compact metadata' phrase helps distinguish from sibling get_pep which likely returns full details, though explicit differentiation from search_peps is missing.
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?
Provides no guidance on when to select this tool versus siblings (get_pep for individual retrieval, search_peps for filtered queries). No mention of prerequisites or when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search active PEP titles for a query string.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 'active' PEPs and 'titles' as scope filters, but fails to explain matching logic (partial vs exact), case sensitivity, or what 'active' specifically means. The existence of an output schema reduces some burden, but safety/permission hints are absent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with the action front-loaded. There is no redundant or wasted text; every word contributes to understanding the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (single string parameter) and the presence of an output schema, the description is minimally adequate. However, the lack of annotations (safety hints) and zero schema descriptions leaves gaps that the description does not fully fill, preventing a higher score.
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 0%, requiring the description to compensate. It references a 'query string' which maps to the 'query' parameter and implies its purpose (searching titles), providing basic semantic context. However, it lacks format details, examples, or constraints that would fully compensate for the bare 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 clearly states the verb (Search), resource (active PEP titles), and scope limitation (titles only, active status). It implicitly distinguishes from sibling 'get_pep' (retrieval by ID) and 'list_peps' (enumeration) by specifying a text search function, though it doesn't explicitly name the 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 implies when to use the tool (when searching for PEPs by title text), but provides no explicit guidance on when to prefer 'get_pep' or 'list_peps' instead, nor does it mention prerequisites or limitations that would help an agent select correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The three tools have clearly distinct purposes: 'get' retrieves a specific PEP by number, 'list' enumerates all active PEPs, and 'search' finds PEPs by title query. No functional overlap exists between them.
All tools follow a consistent snake_case verb_noun pattern (get_pep, list_peps, search_peps). The pluralization appropriately matches the return cardinality (singular for single retrieval, plural for collection operations).
Three tools is the ideal minimum for a read-only document server, covering the essential access patterns: enumeration, search, and specific retrieval. The scope is tightly focused without bloat.
While the basic read operations are present, notable gaps exist: list_peps and search_peps are restricted to 'active' PEPs only with no way to access other statuses, search_peps only searches titles (not content or authors), and there are no filtering options for metadata like author or Python version.
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