Borrowing it
Nothing to install: this file belongs to jtv4k/mongodb-memory-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/jtv4k/mongodb-memory-mcp/main/.claude/skills/ingestion-pipeline/SKILL.mdgit clone --depth 1 https://github.com/jtv4k/mongodb-memory-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/skills/jtv4k/mongodb-memory-mcp/ingestion-pipeline)<a href="https://agentmods.dev/skills/jtv4k/mongodb-memory-mcp/ingestion-pipeline"><img src="https://agentmods.dev/badge/skills/jtv4k/mongodb-memory-mcp/ingestion-pipeline/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/skills/jtv4k/mongodb-memory-mcp/ingestion-pipeline"><img src="https://agentmods.dev/badge/skills/jtv4k/mongodb-memory-mcp/ingestion-pipeline.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00075 | $0.02809 |
| Opus 5 | $0.00037 | $0.01404 |
| Sonnet 5 | $0.00015 | $0.00562 |
| Haiku 4.5 | $0.00007 | $0.00281 |
Grade A, and why
ingestion-pipeline 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 10d 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 — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The ingestion pipeline
validate -> chunk (pure) -> embed (contextual, grouped by document) -> persist
One implementation, in storeContent() in src/services/knowledge-service.ts.
Every surface — MCP store_content, POST /api/content, the web UI — goes
through it. There is no second ingestion path and there must never be one.
Each stage may assume exactly what the previous one guarantees, and nothing more. The failures that matter here are the silent ones: a violated invariant usually does not throw, it just degrades retrieval in a way no test notices.
Stage 1 — Validate
Where: parseInput(storeContentSchema, args, 'store_content') in the
transport, before the service is called.
In: untrusted arguments from an AI client. Out: StoreContentInput.
Guarantees the rest of the pipeline relies on:
contentis a non-empty string of at mostMAX_CONTENT_CHARS(5,000,000) and contains at least one non-whitespace character.contentTypeis one ofCONTENT_TYPES— the chunker switches on it and has no default branch worth relying on.tagsare trimmed, lowercased and deduplicated. Filters compare against the normalised form; skipping this makestags: ['MongoDB']unfindable.metadatais JSON-serialisable, under 32KB, has no key starting with$and no prototype-pollution key. It is persisted verbatim.chunkOverlapTokens < chunkSizeTokenswhen both are overridden.
What breaks if you skip it: the MCP SDK validates against
z.object(storeContentShape) and cannot see the superRefine rules. Removing
the handler's parseInput call silently disables every cross-field check while
leaving the tool apparently working. Validation failures must be
ValidationError — they log at warn with event input.validation_failed,
which is what keeps a caller's bad argument distinguishable from an outage.
Stage 2 — Chunk
Where: chunkContent({ content, contentType, options }) in
src/chunking/index.ts, called through runChunking() in the service.
In: normalised content (normalizeContent has already stripped a BOM and
converted CRLF to LF). Out: ChunkingResult { chunks, strategy, stats }.
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.
- 10d ago First seen · 237 lines · 75 tokens per session scan A 066075b4ea12
ingestion-pipeline is a skill published in the GitHub repository jtv4k/mongodb-memory-mcp (0 stars, last pushed 4d ago), licensed Apache-2.0. It adds 75 tokens to every session and 2,809 once invoked, about $0.0004 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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