Skip to main content
Glama

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so safety is covered. The description adds valuable behavioral context: it uses specific data sources (MITRE ATT&CK, GitHub Advisories, Cloudflare Radar), implies potential long execution (async timeout hint), and describes output contents. This goes beyond 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 three sentences, front-loaded with the core purpose and followed by input/output details and operational hints. No redundant or filler content; every sentence adds value.

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?

Given the tool's complexity and the presence of an output schema, the description covers the essential aspects: purpose, inputs, outputs, data sources, and async behavior. It could further clarify when to choose this over siblings, but overall it provides sufficient context for an agent to select and invoke the tool.

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

Parameters4/5

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

Schema description coverage is only 33% (only async has a description). The description compensates by explaining service_components as 'critical components and reliability thresholds', and async as a timeout avoidance mechanism. It also implies time_window_hours with '24 hours in advance', adding meaning beyond the raw schema.

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 clearly states a specific purpose: predict potential SLO breaches 24 hours in advance using public incident reports and MITRE ATT&CK techniques. It specifies inputs (service components, reliability thresholds) and outputs (breach probability scores, TTPs, mitigations), distinguishing it from sibling tools focused on other observability or vulnerability prediction tasks.

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 provides clear usage context: use when you need to predict SLO breaches for your service's critical components. It also gives operational guidance (pass async:true to avoid timeout). However, it does not explicitly mention when not to use or name alternative tools, though the scenario is well-defined.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.