agent-mesh MCP Server (prathamesh-git9/agent-mesh) 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

agent-mesh MCP Server

Enables coordinating specialist agents through an event-driven backend, allowing submission of goals, retrieval of job status and results, and listing of jobs via MCP tools.

→ prathamesh-git9/agent-mesh· listed on glama

Limited view. Automated analysis covers part of this stack. Findings reflect what we verified. Grades reflect the full trust pyramid: code, verification depth, and reputation. New projects cap at C until adoption is earned.

Limited view: static analysis for Python is partially covered.

How we verified

Code Verified⚡ Live Monitored: not connected

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Who stands behind it

prathamesh-git9

Source: Glama

Is this your MCP?

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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.
🔐
You'll be asked for 3 credentials: XAI_API_KEY, GROK_API_KEY, OPENAI_API_KEY
These are read from process.env at runtime. Make sure you trust where they’ll be sent.
// 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.
🔐 secretXAI_API_KEY, falling back to GROK_API_KEY. GrokProvider.DEFAULT_MODEL is
🔐 secretGROK_API_KEYXAI_API_KEY, falling back to . GrokProvider.DEFAULT_MODEL is
configXAI_MODELgrok-4.5, overridable with or a model= argument.
🔐 secretOPENAI_API_KEYOpenAIProvider uses the official OpenAI SDK and reads when an
configOPENAI_MODELgpt-4o-mini, overridable with or a model= argument.
// 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 — submit_job (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); get_job (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); list_jobs (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

Only 0/3 tool handlers wrap calls in try/catch (0%)

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
[![M8ven Score](https://m8ven.ai/badge/mcp/prathamesh-git9/agent-mesh)](https://m8ven.ai/mcp/prathamesh-git9/agent-mesh)
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commit: 3dbfc52574a12b8def2f8036cce46c58ca29c50e
code hash: 6d282955d150c5632aabf1675796d64151ae891d88bd11edc2392bcd5dc1eb6f
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