Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add rules/wdm0006/writing-tools-mcp/writing_tools_servergit clone --depth 1 https://github.com/wdm0006/writing-tools-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/wdm0006/writing-tools-mcp/writing_tools_server)<a href="https://agentmods.dev/rules/wdm0006/writing-tools-mcp/writing_tools_server"><img src="https://agentmods.dev/badge/rules/wdm0006/writing-tools-mcp/writing_tools_server.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00543 | $0.00543 |
| Opus 5 | $0.00271 | $0.00271 |
| Sonnet 5 | $0.00109 | $0.00109 |
| Haiku 4.5 | $0.00054 | $0.00054 |
Grade A, and why
writing_tools_server scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 37 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing Tools MCP Server (server.py)
This file defines a FastMCP server (server.py) that provides various tools for text analysis. It uses libraries like spacy, textstat, pyspellchecker, and markdown-it-py.
Main Components
mcp = FastMCP(...): The main server instance.nlp = spacy.load("en_core_web_sm"): Loads the spaCy language model for natural language processing tasks. It handles downloading the model if it's not present.preprocess_text(text, ...): A helper function used by several tools to tokenize, lemmatize, and optionally remove stopwords from text.parse_markdown_sections(text): Parses Markdown text into sections based on headings (H1-H6), returning a dictionary. It uses_render_tokens_to_textinternally.split_paragraphs(text): Splits text into paragraphs based on double newlines.
Available Tools
The server exposes the following tools via the @mcp.tool() decorator:
list_tools(): Lists all available tools.character_count(text): Counts characters.word_count(text): Counts words (split by whitespace).spellcheck(text): Finds potentially misspelled words.readability_score(text, level): Calculates Flesch Reading Ease, Flesch-Kincaid Grade, and Gunning Fog scores. Can analyze thefulltext, bysection(Markdown headings), or byparagraph.reading_time(text, level): Estimates reading time. Can analyze thefulltext, bysection, or byparagraph.keyword_density(text, keyword): Calculates the density of a specific keyword.keyword_frequency(text, remove_stopwords): Counts the frequency of each word/lemma.top_keywords(text, top_n, remove_stopwords): Finds the most frequent keywords.keyword_context(text, keyword): Extracts sentences containing a specific keyword or its lemma.passive_voice_detection(text): Detects sentences potentially in passive voice.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 37 lines · 543 tokens per session scan A cbf162f30dfe
writing_tools_server is a cursor rule published in the GitHub repository wdm0006/writing-tools-mcp (10 stars, last pushed yesterday), licensed MIT. It adds 543 tokens to every session, about $0.0027 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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