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EyalSec MCP Server

EyalSec MCP Server

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by EyalSec
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Python
Remote

CI findings

ci_findings
Read-onlyIdempotent

Report what programs on one machine flagged since a given time: severity counts, how many findings reach a threshold, and which are new.

Instructions

What programs on one es-python machine reported since a moment: counts by severity, how many are at or above a threshold, which ones no earlier run had reached, and the findings themselves (ids, not values).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "machine": "<machine>",
      "since": "<since>"
    },
    "name": "ci_findings"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoHow many findings to list, worst first.
sinceYesStart of the window: RFC3339 (2026-10-07T12:00:00Z), Unix seconds or an HTTP date.
machineYesThe machine's name.
min_severityNoThreshold; the default is 'high'.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the safety profile (readOnly, idempotent, openWorld, non-destructive), so the description is free to add semantics instead of repeating them. It does: results are aggregates by severity, a count at or above a threshold, newly-reached findings since an earlier run, and findings returned as ids rather than values. That novelty and id-only behavior is useful context beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single dense but well-structured sentence that front-loads the subject ('what programs on one es-python machine reported') before enumerating outputs. Nothing is wasted, though the enumeration is long.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return structure need not be spelled out, and the description still clarifies the id-not-values convention and the novelty aspect. Combined with fully documented parameters and annotations, an agent has what it needs to call the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so every parameter is already documented in the schema, including min_severity's default of 'high' and limit's worst-first ordering. The description reinforces the threshold concept but adds no format or semantics beyond the schema, so the baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the resource (findings from programs), the scope (one machine, since a moment), and the shape of the result (severity counts, threshold counts, novel findings, finding ids). It is specific enough that an agent can distinguish it from generic event/runs siblings, though it never names those siblings explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use or when-not-to-use guidance. The agent must infer from 'one es-python machine reported since a moment' that this is the machine-scoped CI findings tool, and nothing points to alternatives such as list_runs or search_events for related questions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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