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Antonytm

mcp-sitecore-server

by Antonytm

logging-get-logs

Read-only

Retrieves Sitecore CM logs over SPE for environments without direct filesystem access, filtering by date, file name, level, and tail count.

Instructions

Retrieves Sitecore logs from the CM's data folder over SPE. On a local Docker CM the same files are on a mounted volume, so reading them from disk is cheaper and shows the whole file; use this when you have no filesystem access to the CM. On a deployed SitecoreAI environment the platform collects logs and the data folder may hold little or nothing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoThe date of the log file to retrieve. If not provided, defaults to today. Date format should be in ISO 8601 format (e.g., '2023-10-01T00:00:00Z'
nameNoThe name of the log file to retrieve. If not provided, defaults to log.*. Possible options: log, Crawiling.log, Search.log, SPE.log, Client.log, OWin.log, Publising.log.log
tailNoThe number of lines to retrieve from the end of the log file. Defaults to 500.
levelNoThe level of the log to retrieve. Defaults to DEBUG.DEBUG

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.5

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, so safety is covered. The description adds environment-dependent behavior the agent can't infer: retrieval runs over SPE, local Docker duplicates exist on a volume, and SitecoreAI may return little or nothing. It doesn't discuss pagination or output shape, but the added context is meaningful.

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

Conciseness5/5

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

Three sentences, front-loaded with the core action and then the environment caveats. Each sentence earns its place by changing what the agent should do (fetch remotely, prefer disk on Docker, expect emptiness on SitecoreAI).

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?

For a read tool with full schema coverage and readOnlyHint, the description covers the critical nuance of environment-dependent returns. Return format and pagination are not spelled out, but with tail and level documented in the schema the agent has enough to invoke 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 date, name, tail and level are all documented in the schema itself. The description's phrase 'shows the whole file' lightly contrasts disk reads against the tail behavior, but adds no syntax or format detail beyond what the schema provides, making baseline 3 correct.

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

Purpose5/5

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

States a specific verb (Retrieves) plus resource (Sitecore logs) and the exact source (the CM's data folder over SPE). The agent immediately knows this is remote log fetching via SPE rather than a generic log query, and no sibling competes for the same purpose.

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

Usage Guidelines5/5

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

Gives explicit when-to-use ('use this when you have no filesystem access to the CM'), a named alternative condition (Docker CM where reading from disk is cheaper and shows the whole file), and a when-not-to-expect-value case (deployed SitecoreAI where the platform collects logs and the folder may hold nothing).

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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