local-evidence-mcp

local-evidence-mcp

A local-first MCP server for retrieving a small evidence set and recording reviewed conclusions, policy-gated and redacted without giving an agent general filesystem access.

Category
Visit Server

README

Local Evidence MCP

A local-first Model Context Protocol (MCP) server for retrieving a deliberately small evidence set and recording reviewed conclusions without giving an agent general filesystem access.

This repository demonstrates a practical RAG and agent-harness pattern:

  • policy-gated reads instead of whole-vault access;
  • redaction before content reaches the client or search index;
  • optional local Ollama embeddings with deterministic lexical fallback;
  • content-digest embedding caches that do not store source text;
  • create-only notes and an append-only lesson ledger;
  • newline-delimited JSON-RPC over stdio with no runtime dependencies; and
  • explicit rejection of likely credentials and raw challenge flags.

The included vault and policy are synthetic examples. No personal vault data, private policy, embedding cache, agent configuration, or credentials are part of this repository.

Architecture

flowchart LR
    Client["MCP client"] -->|"JSON-RPC over stdio"| Server["Local Evidence MCP"]
    Server --> Policy["Policy validation"]
    Policy --> Guard["Path and symlink guard"]
    Guard --> Read["Allowlisted UTF-8 notes"]
    Read --> Redact["Credential and raw-flag redaction"]
    Redact --> Lexical["Lexical ranking"]
    Redact --> LocalEmbed["Optional loopback embeddings"]
    Lexical --> Results["Source-labelled results"]
    LocalEmbed --> Results
    Server -->|"O_EXCL"| Create["Create-only drafts"]
    Server -->|"O_APPEND"| Append["Append-only lessons"]

The server advertises no shell, SSH, browser, messaging, remote retrieval, or arbitrary filesystem capability. The only optional HTTP call is to an unauthenticated loopback embedding endpoint.

Quick start

Requirements: Python 3.11 or newer. Ollama is optional.

git clone https://github.com/Labeeb2339/local-evidence-mcp.git
cd local-evidence-mcp
./run_server.cmd --check

On macOS or Linux:

chmod +x run_server.sh
./run_server.sh --check

The default health check uses examples/vault and examples/policy.example.json. If Ollama is not available, the check reports that lexical fallback is active; retrieval still works.

Try a JSON-RPC request directly:

'{"jsonrpc":"2.0","id":1,"method":"tools/list"}' | ./run_server.cmd

Configure your own evidence root

Copy the example policy to a local, ignored file and edit only the relative paths that should be visible:

Copy-Item examples/policy.example.json policy.json
$env:LOCAL_EVIDENCE_ROOT = "D:\path\to\your\notes"
$env:LOCAL_EVIDENCE_POLICY = "$PWD\policy.json"
./run_server.cmd --check

The policy separates four decisions:

Section Purpose
read.files Exact files the server may read
read.globs Non-recursive relative globs, such as notes/*.md
excluded Paths denied even if another rule matches
write One create-only directory and one append-only file

Limits cap file size, result count, write size, index size, and chunk length. Absolute paths, traversal components, NTFS stream syntax, recursive read globs, and linked files are rejected.

To force offline lexical mode, set embeddings.enabled to false. When embeddings are enabled, the endpoint must use plain HTTP on 127.0.0.1, localhost, or ::1; remote and credential-bearing URLs are refused.

MCP client configuration

After installing the package with python -m pip install -e ., a client can launch it with the console script:

{
  "mcpServers": {
    "local-evidence": {
      "command": "local-evidence-mcp",
      "args": [
        "--root",
        "<absolute-path-to-evidence-root>",
        "--policy",
        "<absolute-path-to-local-policy.json>"
      ]
    }
  }
}

Keep the real policy and evidence root outside version control. The example policy is safe to publish because it references only the included synthetic fixtures.

Tools

Tool Boundary
evidence_status Reports configured capabilities and exclusions
evidence_search Searches sanitized allowlisted chunks
evidence_read Reads one sanitized allowlisted file
evidence_create_note Creates a new draft with exclusive-create semantics
evidence_append_lesson Appends one evidence-backed entry to a fixed ledger

Tool input is checked again at runtime instead of relying only on the JSON schemas presented to the MCP client.

Security properties

Risk Control
Broad filesystem exposure Relative allowlist plus resolved-root containment
Traversal or linked-file escape Component validation and symlink rejection
Secret leakage in reads Redaction before direct output and chunking
Sensitive query or durable write Credential and raw-flag rejection
Destructive overwrite `O_CREAT
Rewrite of the lesson ledger Fixed destination opened with O_APPEND
Embedding data leakage Loopback-only endpoint and vector-only cache
Prompt injection in notes Evidence warnings and no execution tools

Regex redaction is defense in depth, not a substitute for keeping secrets out of the evidence root. Local operating-system permissions still define who can start the process and edit its policy.

Test

The full suite uses only the standard library:

$env:PYTHONPATH = "src"
python -m unittest discover -s tests -v

CI runs the suite on Python 3.11, 3.12, and 3.13 and performs a Gitleaks scan. Tests cover path containment, exclusions, redaction, lexical fallback, semantic ranking, plaintext-free caching, create-only and append-only writes, sensitive input rejection, symlink handling, and MCP protocol responses.

Design scope

This is a compact reference implementation, not a hosted vector database or an enterprise authorization service. It is most useful when an assistant needs a small, inspectable local knowledge boundary and the operator values safe degradation when the embedding service is offline.

License

MIT

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured