global-sentinel-mcp

Server Quality Checklist

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Latest release: v0.1.1
Disambiguation5/5
Each tool targets a distinct domain: network anomalies, aviation identifiers, geopolitical events, composite alerts, and prediction markets. No two tools perform overlapping functions, so agents can easily distinguish them.
Naming Consistency4/5
All tool names use snake_case with descriptive verbs like 'check_', 'convert_', 'get_', and 'lookup_'. While different verbs are used, they are appropriate to each action and follow a predictable pattern, with only minor variation.
Tool Count5/5
With 7 tools, the set is well-scoped for a global monitoring server. Each tool provides a specific, non-redundant capability, and the count is manageable for an agent to navigate without being overwhelming or sparse.
Completeness3/5
The tool surface covers network anomalies, aviation, geopolitical events, and prediction markets, but lacks some expected features like news search or natural disaster monitoring. For a 'sentinel' purpose, there are notable gaps, though core workflows are supported.
Average 4.3/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on April 20, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It states it fetches implied probability, implying a read operation, but does not detail response format or other traits. For a simple fetch tool, this is adequate but not rich.
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?
Two sentences, front-loaded with the core action. Every word is necessary; no redundancy.
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 no output schema and only one parameter, the description is nearly complete: it specifies input (id/slug), output (implied probability), and usage context. The only minor gap is not mentioning the output format explicitly, but it's acceptable for a simple fetch.
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 schema coverage is 0% (no description for event_id), but the description clarifies that the parameter accepts both id and slug, adding meaning beyond the schema's type definition.
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 clearly states the action (Fetch) and resource (single Polymarket event's implied probability). It specifies identification by id or slug, which distinguishes it from potential siblings like get_prediction_odds that might use different keys. However, it does not explicitly contrast with siblings.
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 includes a usage scenario: when you already have an identifier and want current odds without re-searching. This provides clear context but does not list alternatives or when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden. It discloses that it returns up to 'limit' active markets with specific fields and that data is crowd-sourced. Lacks details on error handling, pagination, or rate limits.
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?
Description is concise, well-structured, and front-loaded. Each sentence serves a purpose: purpose, usage, return values, caveat, and example. No wasted words.
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 simple parameters and no output schema, description covers the main return fields and data source. It lacks detail on return format (e.g., is it a list?) and error conditions, but suffices for basic usage.
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 coverage is 0%, but description adds meaning by explaining 'limit' controls number of results and providing an example usage. It does not explicitly define 'query' but context makes it clear.
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 clearly states it searches Polymarket for probability estimates, specifying the resource (Polymarket) and action (search). It provides a usage scenario (user asks 'what are the odds of X?'), which helps differentiate from siblings like get_prediction_event_by_id, but does not explicitly contrast.
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?
Explicitly says to use when user asks 'what are the odds of X?' for macro events, and notes data is crowd-sourced. However, it does not mention when not to use or compare with alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses key behaviors: concurrent queries, partial failure tolerance, error recording, scoring formula, and interpretation of zero. This exceeds the burden of transparency.
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 compact and front-loaded, covering all essential aspects in a single paragraph. Minor improvement could be achieved with bullet points, but it is already concise and well-structured.
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?
Despite no output schema and no annotations, the description provides thorough context: how the tool works, what it returns, failure handling, scoring logic, and the meaning of zero. It is fully sufficient for an agent to understand and invoke the tool.
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 schema has zero description coverage for the single 'region' parameter, but the description mentions 'for a region' without specifying format or examples. Given the simplicity of the parameter, the baseline is 3, but more explicit guidance would improve it.
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 the tool's verb ('composite macro-inflection signal') and resource ('region'), and distinguishes it from siblings by detailing the three concurrent queries. No other sibling tool offers this composite functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a concise macro signal is needed, but it lacks explicit guidance on when to use this tool vs. alternatives like get_global_events. No when-not-to-use scenarios are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses authentication requirements (OAuth2 or Basic Auth) and fallback logic for N-number conversion. Without annotations, it covers key behavioral traits, though rate limits or data freshness are not mentioned.
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?
Concise, front-loaded with purpose, and uses structured list for return fields. Every sentence adds value with no redundancy.
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?
Covers inputs, conversion, auth, and output fields adequately. Lacks mention of error handling or data source limitations, but is sufficient for typical use.
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?
With 0% schema coverage, the description compensates by explaining the single parameter's format and providing examples (N12345, a061d9), adding significant meaning beyond the 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 it returns the latest public ADS-B state vector for a registered aircraft, specifying verb and resource. It distinguishes from siblings by being the only tool focused on aircraft state lookup.
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?
Provides clear accepted input types (N-Number or ICAO24 hex) and explains conversion process. However, it does not explicitly state when not to use or mention alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description covers behavior: it details different behaviors for country vs ASN inputs and requires CLOUDFLARE_API_TOKEN with Radar read permission. This provides good transparency.
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 concise and well-structured, using bullet points for input formats. It is efficient and front-loaded.
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 description covers the main functionality and authorization. It lacks information about output format or error scenarios, but given the tool's simplicity, it is fairly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The sole parameter 'country_or_asn' is not described in the schema (0% coverage), but the description fully explains its meaning and valid formats (2-letter country code or ASN string).
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 it checks for BGP and internet-outage anomalies, and explains what each input type checks. It is specific and distinct from sibling tools.
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 explains input formats and what each does, providing clear guidance on when to use the tool. It does not explicitly mention when not to use or alternatives, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that results are sorted by Goldstein scale descending, capped at limit, and returns specific fields. Mentions GDELT's 15-minute update cycle. No annotations, so description carries burden; no destructive behavior implied.
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?
Six sentences across two paragraphs, front-loaded with purpose, all sentences add value: data source, scale, code options, sorting, limits, return fields. No fluff.
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?
No output schema, but description explains return fields (actor country codes, Goldstein score, article count, source URL). Lacks error handling or missing data mentions, but sufficient for intended use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but description fully explains country_code (code format), min_goldstein_scale (threshold with default), and limit (capping). Adds value beyond schema's type info.
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?
Clearly states the tool fetches recent high-impact GDELT 2.0 events for a country, explains the Goldstein scale, and describes output formatting. Distinct from siblings like check_network_anomalies and get_macro_alert.
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?
Provides clear instructions on using country codes (3-letter GDELT or 2-letter FIPS) and explains sorting and capping. Lacks explicit mention of when not to use this tool, but sibling tools are sufficiently different.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses automatic detection, returns both forms plus 'mode' flag. No annotations provided, so description carries full burden. For a simple conversion, it adequately covers behavior.
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?
Two sentences plus a usage note; no wasted words. Front-loaded with purpose and ends with practical usage context.
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?
Complete for a simple one-parameter tool: explains input format, output fields, and use case. No output schema needed given straightforward return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'identifier' is fully explained in description: accepts N-number (N + digits/letters) or ICAO24 (6-char hex). Compensates for 0% schema description coverage.
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 the function: convert between FAA N-Number and ICAO24 hex address. It specifies the verb 'Convert' and the two identifiers, distinguishing it from sibling tools like lookup_aircraft_state.
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?
Describes automatic input detection based on format (N-prefix vs 6-char hex). Provides use case for cross-referencing aviation data. No explicit exclusions needed given distinct siblings.
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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