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mcp-knowledge

by io.github.getgapup

sre_slo_breach_predictor

Read-onlyIdempotent

As a CTO, predict potential SLO breaches 24 hours in advance by analyzing public incident reports and MITRE ATT&CK techniques. Input your service's critical components and reliability thresholds to receive breach probability scores, top contributing TTPs, and recommended mitigations. Uses MITRE ATT&CK, GitHub Advisories, and Cloudflare Radar data. Pass async:true to avoid timeout.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
time_window_hoursNo
service_componentsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
warningsNo
incident_reportsNo
breach_probabilityNo
recommended_actionsNo
top_ttp_contributorsNo

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already mark the tool as readOnlyHint=true, openWorldHint=true, idempotentHint=true, so the safety profile is clear. The description adds value by disclosing data sources (MITRE ATT&CK, GitHub Advisories, Cloudflare Radar), output elements (probability scores, TTPs, mitigations), and timeout behavior with async guidance.

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 well-structured sentences, no wasted words. Front-loaded with purpose, followed by usage context and a critical tip. Ideal length for quick comprehension.

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?

The tool is moderately complex with 3 parameters and a presumed output schema. The description covers inputs, data sources, outputs, and async behavior. While output schema exists, the description still summarizes return values helpfully.

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 coverage is low (33%), so the description must compensate. It explains service_components as 'critical components and reliability thresholds' and mentions async behavior. However, time_window_hours is only implied by the 24-hour horizon, and the description does not detail all parameters fully.

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?

The description uses the specific verb 'predict' and resource 'potential SLO breaches', with a clear time horizon (24 hours). This is distinct from sibling tools, most of which focus on different domains like finance, security, or HR.

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

Usage Guidelines4/5

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

The description targets a specific user role (CTO) and provides a clear scenario (predicting SLO breaches). It also gives a practical tip to use async to avoid timeouts. However, it does not explicitly state when not to use or list alternative tools.

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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TDQS

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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