BrunoSan AI News Intelligence

news_decision_maker_brief

Read-onlyIdempotent

AI news clusters ranked by a transparent composite formula: composite = burst_score * (tier1_source_count + 1) * (hard_numbers_count + 1) * ai_relevance_score

Role-aware via cluster scope filtering (CDO/CTO/CEO/Compliance). The
composite formula itself is identical across roles — what changes is
which clusters are eligible, so the formula stays auditable and
reproducible. The (+1) smoothing prevents zeroing-out for fresh
clusters that have not yet picked up tier-1 coverage or hard data.

Role scopes:
  - cdo:        primary_event_type in (model_release, model_retirement, benchmark_result, infrastructure_expansion, research_breakthrough, product_launch, partnership, security_incident)
  - cto:        primary_event_type in (product_launch, benchmark_result, security_incident, model_release, feature_update, infrastructure_expansion)
  - ceo:        business_relevance_score >= 50
  - compliance: compliance_signal_count > 0 OR risk_signal_count > 0

Example queries:
    - "What does a CDO need to know in AI today?" (role=cdo)
    - "Generate a Monday morning AI briefing for our board — CEO perspective, top 5 items only"
    - "What's the highest-priority AI news for a compliance officer? Clusters with official sources and compliance signals, ranked by urgency"
    - "I'm a CTO preparing for a board meeting. Top 3 most decision-relevant AI developments this week — with hard numbers, not just headlines"
    - "Compare today's priority rankings for role=ceo vs role=cto — where do they diverge? What matters to one but not the other?"

Args:
    params (DecisionMakerInput):
        - role: cdo | cto | ceo | compliance
        - date: YYYY-MM-DD or empty for latest
        - limit: number of clusters to return

Returns:
    str: JSON with date, role, composite_formula, role_filter_applied,
    count, and clusters list (each with composite_score and all input
    components for transparent verification).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`params`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 14, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already mark the tool as read-only, idempotent, and non-destructive, and the description adds substantial behavioral detail: the exact composite formula, the (+1) smoothing rationale, the role-specific filters, and the auditability requirement. This is a strong disclosure of how rankings are produced and what the response envelope contains.

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?

The description is longer than average, but the formula, role scopes, and example queries all serve a functional purpose and are organized in clear sections. The front-loaded formula and role explanation give the key information first; the examples could be slightly trimmed but are not wasteful.

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

Completeness5/5

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

Given the tool's moderate complexity, the description is complete: it explains the ranking formula, role scoping, parameter semantics, filtering logic, and the JSON return structure. An agent has enough information to select the right role, construct a valid query, and interpret the ranked clusters without needing external context.

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?

The description adds important meaning beyond the schema by defining each role's filter semantics and showing role-based example queries. It does not repeat every schema detail like limit bounds or api_key, but the schema already covers those, so the description's role-scope detail is the valuable differentiator.

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 states a specific verb and resource: AI news clusters ranked by a transparent composite formula for role-specific briefs. It clearly differentiates from siblings by emphasizing role-aware filtering and the auditable composite formula, rather than generic news retrieval or search.

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 through role scopes and concrete example queries for each intended audience (CDO, CTO, CEO, Compliance). It does not explicitly name sibling tools or state when not to use it, but the role-aware framing and examples make the intended invocation context unambiguous.

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

All BrunoSan AI News Intelligence tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?