io.github.egoughnour/massive-context-mcp
PYPI · MASSIVE-CONTEXT-MCP · 2 COMPONENTS
Handles 10M+ token contexts with chunking, sub-queries, and local Ollama inference.
Available components
Last scanned (21 hours ago)
Scores higher than 78% of PyPI servers
Category AI and Machine Learning
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security50
- Malware scan not yet available for this package.Unverified
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs hatchling.build at install time, a recognised build step with no custom scripting around it. View diagnostics → Pass
- 1 of 22 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency95
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Cryptographically verified build provenance (signed, bound to egoughnour/massive-context-mcp). View diagnostics → Pass
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 289 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability79
- AI-judged instruction clarity (excellent).Pass
- Tool/resource definitions use about 1329 tokens (~78/item across 17 items; 17 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management100
- No destabilizing schema changes in the last 30 days.Pass
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety75
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- 0 of 1 tool(s) whose name or description implies an irreversible operation declare an MCP destructiveHint annotation; "rlm_exec" implies "exec" and declares no destructiveHint at all, which the MCP spec reads as destructive by default. See how to fix → Fail
- An AI judge read all 17 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 3 Nov 26 −15
- Malware scan: pass → unverified ▼ security
- 2 Nov 26 +15
- Malware scan: unverified → pass ▲ security
- 1 Nov 26 −15
- Malware scan: pass → unverified ▼ security
- 31 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- 30 Oct 26 0
- Stability: 0.97 → pass security
- 29 Oct 26 −15
- Malware scan: pass → unverified ▼ security
- Stability: pass → 0.97 functional
- 23 Oct 26 0
- Stability: 0.97 → pass security
- 22 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- Stability: pass → 0.97 functional
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 4 Nov 2026 · Analysed pypi/massive-context-mcp@3.0.1
Provenance Verified
A signed build attestation was found and verified, binding this exact artifact to the source repository it claims to come from.
| Result | Verified |
|---|---|
| Ecosystem | pypi |
| Reason | Verified |
| Discovered via | Registry attestation endpoint |
| Source repo | egoughnour/massive-context-mcp |
| Certificate issuer | https://token.actions.githubusercontent.com |
| Certificate SAN | https://github.com/egoughnour/massive-context-mcp/.github/workflows/release.yml@refs/tags/v3.0.1 |
| Rekor log index | 834323483 |
| Predicate type | PyPI publish attestation https://docs.pypi.org/attestations/publish/v1 |
| Subject digest | sha256:7332f0c4a535b7f7c7547b83e0831482dde3aa1a4da0aed76c817b3fbace3343 |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Dependencies 22 packages
| Packages resolved | 22 |
|---|---|
| Stale | 1 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
rlm_auto_analyze Rlm Auto Analyze ~122
Automatically detect content type and analyze with optimal chunking strategy. One-step analysis for common tasks.
| Name | Type | Req | Description |
|---|---|---|---|
| concurrency | integer | – | Max parallel requests (default 4, max 8) |
| content | string | yes | The content to analyze |
| goal | string | yes | Analysis goal: 'summarize', 'find_bugs', 'extract_structure', 'security_audit', or 'answer:<your question>' |
| name | string | yes | Context identifier |
| provider | string | – | LLM provider - 'auto' prefers Ollama if available |
Structured output declared, but exposes no named fields.
No examples provided.
rlm_chunk_context Rlm Chunk Context ~71
Chunk a loaded context by strategy. Returns chunk metadata, not full content.
| Name | Type | Req | Description |
|---|---|---|---|
| name | string | yes | Context identifier |
| size | integer | – | Chunk size (lines/chars depending on strategy) |
| strategy | string | – | Chunking strategy - 'lines', 'chars', or 'paragraphs' |
Structured output declared, but exposes no named fields.
No examples provided.
rlm_exec Rlm Exec ~75
Execute Python code against a loaded context in a sandboxed subprocess. Set result variable for output.
| Name | Type | Req | Description |
|---|---|---|---|
| code | string | yes | Python code to execute. User sets result variable for output. |
| context_name | string | yes | Name of previously loaded context |
| timeout | integer | – | Max execution time in seconds (default 30) |
Structured output declared, but exposes no named fields.
No examples provided.
rlm_filter_context Rlm Filter Context ~72
Filter context using regex/string operations. Creates a new filtered context.
| Name | Type | Req | Description |
|---|---|---|---|
| mode | string | – | 'keep' or 'remove' matching lines |
| name | string | yes | Source context identifier |
| output_name | string | yes | Name for filtered context |
| pattern | string | yes | Regex pattern to match |
Structured output declared, but exposes no named fields.
No examples provided.
rlm_firewall_status Rlm Firewall Status ~46
Check the status of the code execution firewall. Returns information about whether the firewall is enabled, the Ollama endpoint being used, and whether dangerous code patterns will be blocked.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
rlm_get_chunk Rlm Get Chunk ~48
Get a specific chunk by index. Use after chunking to retrieve individual pieces.
| Name | Type | Req | Description |
|---|---|---|---|
| chunk_index | integer | yes | Index of chunk to retrieve |
| name | string | yes | Context identifier |
| Name | Type | Req | Description |
|---|---|---|---|
| result | – | yes | – |
No examples provided.
rlm_get_results Rlm Get Results ~26
Retrieve stored results for aggregation.
| Name | Type | Req | Description |
|---|---|---|---|
| name | string | yes | Result set identifier |
| Name | Type | Req | Description |
|---|---|---|---|
| result | – | yes | – |
No examples provided.
rlm_inspect_context Rlm Inspect Context ~52
Inspect a loaded context - get structure info without loading full content into prompt.
| Name | Type | Req | Description |
|---|---|---|---|
| name | string | yes | Context identifier |
| preview_chars | integer | – | Number of chars to preview (default 500) |
Structured output declared, but exposes no named fields.
No examples provided.
rlm_list_contexts Rlm List Contexts ~19
List all loaded contexts and their metadata.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
rlm_load_context Rlm Load Context ~48
Load a large context as an external variable. Returns metadata without the content itself.
| Name | Type | Req | Description |
|---|---|---|---|
| content | string | yes | The full context content |
| name | string | yes | Identifier for this context |
Structured output declared, but exposes no named fields.
No examples provided.
rlm_ollama_status Rlm Ollama Status ~79
Check Ollama server status and available models. Returns whether Ollama is running, list of available models, and if the default model (gemma3:12b) is available. Use this to determine if free local inference is available.
| Name | Type | Req | Description |
|---|---|---|---|
| force_refresh | boolean | – | Force refresh the cached status (default: false) |
Structured output declared, but exposes no named fields.
No examples provided.
rlm_setup_ollama Rlm Setup Ollama ~160
Install Ollama via Homebrew (macOS). Requires Homebrew pre-installed. Uses 'brew install' and 'brew services'. PROS: Auto-updates, pre-built binaries, managed service. CONS: Requires Homebrew, may prompt for sudo on first Homebrew install.
| Name | Type | Req | Description |
|---|---|---|---|
| install | boolean | – | Install Ollama via Homebrew (requires Homebrew) |
| model | string | – | Model to pull (default: gemma3:12b). Use gemma3:4b or gemma3:1b for lower RAM systems. |
| pull_model | boolean | – | Pull the default model (gemma3:12b) |
| start_service | boolean | – | Start Ollama as a background service via brew services |
Structured output declared, but exposes no named fields.
No examples provided.
rlm_setup_ollama_direct Rlm Setup Ollama Direct ~166
Install Ollama via direct download (macOS). Downloads from ollama.com to ~/Applications. PROS: No Homebrew needed, no sudo required, fully headless, works on locked-down machines. CONS: Manual PATH setup, no auto-updates, service runs as foreground process.
| Name | Type | Req | Description |
|---|---|---|---|
| install | boolean | – | Download and install Ollama to ~/Applications (no sudo needed) |
| model | string | – | Model to pull (default: gemma3:12b). Use gemma3:4b or gemma3:1b for lower RAM systems. |
| pull_model | boolean | – | Pull the default model (gemma3:12b) |
| start_service | boolean | – | Start Ollama server (ollama serve) in background |
Structured output declared, but exposes no named fields.
No examples provided.
rlm_store_result Rlm Store Result ~50
Store a sub-call result for later aggregation.
| Name | Type | Req | Description |
|---|---|---|---|
| metadata | – | – | Optional metadata about this result |
| name | string | yes | Result set identifier |
| result | string | yes | Result content to store |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
rlm_sub_query Rlm Sub Query ~116
Make a sub-LLM call on a chunk or filtered context. Core of recursive pattern.
| Name | Type | Req | Description |
|---|---|---|---|
| chunk_index | – | – | Optional: specific chunk index |
| context_name | string | yes | Context identifier to query against |
| model | – | – | Model to use (provider-specific defaults apply) |
| provider | string | – | LLM provider - 'auto', 'ollama', or 'claude-sdk'. 'auto' prefers Ollama if available (free local inference) |
| query | string | yes | Question/instruction for the sub-call |
Structured output declared, but exposes no named fields.
No examples provided.
rlm_sub_query_batch Rlm Sub Query Batch ~118
Process multiple chunks in parallel. Respects concurrency limit to manage system resources.
| Name | Type | Req | Description |
|---|---|---|---|
| chunk_indices | array | yes | List of chunk indices to process |
| concurrency | integer | – | Max parallel requests (default 4, max 8) |
| context_name | string | yes | Context identifier |
| model | – | – | Model to use (provider-specific defaults apply) |
| provider | string | – | LLM provider - 'auto', 'ollama', or 'claude-sdk' |
| query | string | yes | Question/instruction for each sub-call |
Structured output declared, but exposes no named fields.
No examples provided.
rlm_system_check Rlm System Check ~61
Check if system meets requirements for Ollama with gemma3:12b. Verifies: macOS, Apple Silicon (M1/M2/M3/M4), 16GB+ RAM, Homebrew installed. Use before attempting Ollama setup.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
How do I install the io.github.egoughnour/massive-context-mcp server?
io.github.egoughnour/massive-context-mcp runs locally as a PyPI package, launched with uvx massive-context-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · massive-context-mcp
claude mcp add egoughnour-massive-context-mcp -- uvx massive-context-mcp
What is the io.github.egoughnour/massive-context-mcp server?
io.github.egoughnour/massive-context-mcp is listed in the public MCP registry as io.github.egoughnour/massive-context-mcp. Handles 10M+ token contexts with chunking, sub-queries, and local Ollama inference. This page covers its PyPI package (massive-context-mcp).
Is the io.github.egoughnour/massive-context-mcp server safe to use?
io.github.egoughnour/massive-context-mcp scores 79 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 4 November 2026. Its build provenance is signed and verified. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the io.github.egoughnour/massive-context-mcp server expose?
io.github.egoughnour/massive-context-mcp exposes 17 tools: rlm_system_check, rlm_setup_ollama, rlm_setup_ollama_direct, rlm_ollama_status, rlm_load_context, and 12 more. Their descriptions and schemas cost roughly 1,329 tokens of context every time the server is loaded.
Is the io.github.egoughnour/massive-context-mcp server still maintained?
io.github.egoughnour/massive-context-mcp is still listed as active in the MCP registry. We last reached this channel on 4 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the io.github.egoughnour/massive-context-mcp server under?
io.github.egoughnour/massive-context-mcp declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.