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.
git clone --depth 1 https://github.com/fasuizu-br/speech-ai-examplesWrote 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/mcp/fasuizu-br/speech-ai-examples/nlp-tools)<a href="https://agentmods.dev/mcp/fasuizu-br/speech-ai-examples/nlp-tools"><img src="https://agentmods.dev/badge/mcp/fasuizu-br/speech-ai-examples/nlp-tools/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/mcp/fasuizu-br/speech-ai-examples/nlp-tools"><img src="https://agentmods.dev/badge/mcp/fasuizu-br/speech-ai-examples/nlp-tools.svg" alt="Reviewed on agentmods" width="80" height="20"></a>Grade A, and why
nlp-tools 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 8d 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.
What it actually says
{ "nlp-tools": { "url": "https://apim-ai-apis.azure-api.net/mcp/nlp/mcp", "type": "streamable-http" } }
What else
This page is one entry in a file that holds 3. Installing the file brings all of them; each is measured and scanned on its own page.
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.
- 8d ago First seen · 6 lines scan A b088799255c9
nlp-tools is an MCP server published in the GitHub repository fasuizu-br/speech-ai-examples (2 stars, last pushed 1mo ago), licensed MIT. Its token cost is not measured: an MCP server costs its tool schemas, not its config file. 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.
Other mcp servers, from other repositories
polyprompt
MCP server "polyprompt" as configured in DisSa12/polyprompt. Launched with polyprompt mcp.
pronunciation
Local MCP voice coach with English pronunciation, grammar, fluency, phoneme-level feedback, and practice drills. Runs locally from the mcp-server-pronunciation Python package. Needs 2 environment variables to run.
knowledge_catalog
MCP server "knowledgecatalog", hosted remotely at dataplex.googleapis.com, as configured in gemini-cli-extensions/data-agent-kit-starter-pack. Needs 1 environment variable to run.
dataproc
MCP server "dataproc", hosted remotely at dataproc-$gcpregion.googleapis.com, as configured in gemini-cli-extensions/data-agent-kit-starter-pack. Needs 1 environment variable to run.
rag-rat
CLI and MCP entrypoint for indexing repositories into local source, graph, history, and memory evidence. Runs locally from the @rag-rat/bin npm package.
Pinecone
Model Context Protocol server for Pinecone - enables AI assistants to interact with Pinecone indexes and documentation. Runs locally from the @pinecone-database/mcp npm package. Needs 1 environment variable to run.