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tosin2013

mcp-adr-analysis-server

by tosin2013

analyze_deployment_progress

Read-onlyIdempotent

Analyze deployment progress using CI/CD logs and task data, then verify completion with outcome rules to confirm deployment success.

Instructions

Analyze deployment progress and verify completion with outcome rules

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cicdLogsNoCI/CD pipeline logs for analysis
todoPathNoPath to TODO.md file for task identificationTODO.md
cicdStatusNoCI/CD pipeline status data
adrDirectoryNoDirectory containing ADR filesdocs/adrs
analysisTypeNoType of deployment analysis to performcomprehensive
outcomeRulesNoOutcome rules for completion verification
actualOutcomesNoActual deployment outcomes
pipelineConfigNoCI/CD pipeline configuration
deploymentTasksNoDeployment tasks for progress calculation
environmentStatusNoEnvironment status data

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the main behavioral risks. The description adds that the tool can 'verify completion with outcome rules,' which is a useful behavioral hint, but it does not explain what the verification outputs look like or how it handles incomplete data. No contradiction with annotations.

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?

The description is a single, compact sentence with no filler or redundancy. It front-loads the main action and then adds the distinguishing verification aspect, making it easy to parse quickly.

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

Completeness2/5

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

Despite having 10 parameters and no output schema, the description does not specify expected return values, default behavior, or which inputs are necessary for a meaningful analysis. The annotations cover safety, but the agent is left without enough context about what the tool will produce or how to interpret its results.

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?

The input schema provides 100% parameter description coverage, including defaults, enums, and type details, so the schema already carries the semantic weight. The description only loosely echoes the 'outcomeRules' parameter without adding new meaning or clarifying how parameters interact.

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 uses a specific verb ('Analyze') and a clear resource ('deployment progress'), and adds a distinct verification aspect ('verify completion with outcome rules'). It is clear about the tool's core function, though it does not explicitly distinguish itself from overlapping sibling tools like deployment_readiness or release_tracking.

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?

The description provides no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. It merely states what the tool does, leaving the agent to infer appropriate usage from the tool name and sibling context.

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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