LTM MCP Server (mklcapital/ltm-mcp) is an MCP server listed on the M8ven Trust Index. It scores 69 out of 100, grade C. It declares 3 tools. No publisher has claimed this listing.

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C
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69/100

LTM MCP Server

Enables MCP-capable AI to perform read-only semantic search over a local document corpus stored in Postgres with pgvector, using mxbai-embed-large embeddings and optional reranking for confidence-gated results.

→ mklcapital/ltm-mcp· listed on glama

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Limited view: static analysis for Python is partially covered.

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Who stands behind it

mklcapital

Source: Glama

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Install from

Glamanot scored here

The grade above is for the source repository. Registries can serve a different version, so we mark the ones we were not able to read.

// key findings
✅
No credential exfiltration, no sensitive file access, no obfuscation
Static analysis found nothing flowing your secrets to unexpected places.
✅
Open source with a license and README
Anyone can audit the code, the license is declared, and the publisher documents what it does.
// environment variables
To run this server yourself, you supply these values. They go in your own MCP client configuration and stay on your machine. The secret label means the value is sensitive, not that the server mishandles it.
configLTM_PGHOST/ LTM_PGPORT 127.0.0.1 / 5433 Postgres
configLTM_PGPORTLTM_PGHOST / 127.0.0.1 / 5433 Postgres
configLTM_PGUSER/ LTM_PGDATABASE postgres / brain Postgres
configLTM_PGDATABASELTM_PGUSER / postgres / brain Postgres
configLTM_EMBED_MODELmxbai-embed-large embedding model
configLTM_RERANK_PYTHON/ LTM_RERANK_SCRIPT unset (rerank off) cross-encoder worker
configLTM_RERANK_SCRIPTLTM_RERANK_PYTHON / unset (rerank off) cross-encoder worker
configLTM_RERANK_CANDIDATES50 rerank pool size
configLTM_RERANK_TIMEOUT_S
configLTM_LOW_RERANK/ LTM_HIGH_RERANK 3 / 6 confidence gates
configLTM_HIGH_RERANKLTM_LOW_RERANK / 3 / 6 confidence gates
configLTM_LOW_COSINE
configLTM_HIGH_COSINE
configLTM_HTTP_HOST/ LTM_HTTP_PORT 127.0.0.1 / 8322 HTTP mode
configLTM_HTTP_PORTLTM_HTTP_HOST / 127.0.0.1 / 8322 HTTP mode
configLTM_PUBLIC_HOSTport-forward. Set to your tunnel hostname so it passes the
// quality suggestions

Tool annotations

No tools have read-only/destructive annotations

Add readOnlyHint or destructiveHint annotations to every tool so hosts can warn users before invoking.

All four hints declared on every tool

3/3 tools missing one or more hints — ltm_search (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); ltm_get_document (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); ltm_stats (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint). OpenAI's directory rejects tools where any of the four hints are missing or non-boolean.

For every tool, set all four hints (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) to explicit true/false values that match the handler’s actual behaviour.

Tool inputs are validated

2/3 tool handlers declare input schemas (67%)

Declare an inputSchema with zod/joi/yup on every tool definition.

Tool handlers catch errors

2/3 tool handlers wrap calls in try/catch (67%)

Wrap each tool handler body in try/catch and return a structured error response.

Tests exist

No test files found

Add tests that exercise each declared tool.

Claim the listing to review these findings one by one and send us a correction where you disagree, straight to the team. Claiming also means we tell you when the grade moves, and reach you first if we find anything urgent.

// full audit trail
The findings above are the summary. The full trail, every check we ran, each deduction, the network hosts observed and the dependency advisories, goes to verified publishers, along with an alert whenever a new one lands. Verified publishers can also review each finding and dispute it in one click. Publisher corrections have sharpened several of our checks this month, because the maintainer knows the codebase better than any scanner.
// improvement guidance — verified publishers only
We have 5 concrete improvements we can share with the publisher of this MCP. Each comes with specific guidance to raise the trust score.
// embed badge in your README
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commit: 973b55c01c10be60b6ee9a0dde16d25104f291a0
code hash: 25b072015622e20d1e26677e36e6c78009b1827701c7d0c609dc4f7103bd0e82
view raw JSON →
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