Пакеты Python для платформы реагирования на стихийные бедствия ResQ, опубликованные в PyPI под организацией resq-software.
Пакет | Описание | Версия |
Сервер FastMCP — подключает ИИ-агентов к парку дронов, симуляциям и данным о стихийных бедствиях | ||
Структуры данных и алгоритмы без зависимостей для поиска, спасения и геопространственных операций |
Related MCP server: Example MCP SSE Server
graph TB
subgraph "resq-software/pypi"
subgraph "packages/resq-mcp"
MCP[resq-mcp<br/><i>FastMCP Server</i>]
DTSOP[DTSOP<br/>Digital Twin Simulations]
HCE[HCE<br/>Hybrid Coordination]
PDIE[PDIE<br/>Predictive Intelligence]
DRONE[Drone Fleet<br/>Telemetry & Control]
MCP --> DTSOP
MCP --> HCE
MCP --> PDIE
MCP --> DRONE
end
subgraph "packages/resq-dsa"
DSA[resq-dsa<br/><i>Zero-Dep DSA</i>]
BF[BloomFilter]
CMS[CountMinSketch]
GR[Graph + A*]
HP[BoundedHeap]
TR[Trie]
DSA --> BF
DSA --> CMS
DSA --> GR
DSA --> HP
DSA --> TR
end
end
AI[AI Clients<br/>Claude / VS Code / Cursor] -->|MCP protocol| MCP
APP[Python Applications] -->|pip install| DSA# Install a package
pip install resq-mcp # MCP server for AI agents
pip install resq-dsa # Data structures (zero dependencies)# Clone and setup
git clone https://github.com/resq-software/pypi.git && cd pypi
./bootstrap.sh
# Work on a package
cd packages/resq-mcp && uv sync && uv run pytest
cd packages/resq-dsa && uv sync && uv run pytestgraph LR
PUSH[Push to main] --> SR[Semantic Release]
SR -->|feat: / fix:| BUMP[Version Bump + Changelog]
BUMP --> BUILD[Build sdist + wheel]
BUILD --> ATTEST[Sigstore Attestation]
ATTEST --> PYPI[Publish to PyPI]
PYPI --> DOCKER[Docker Image<br/><i>resq-mcp only</i>]Оба пакета используют python-semantic-release с OIDC Trusted Publisher. Стандартные коммиты в ветку main автоматически версионируют, создают список изменений и публикуют пакеты.
Apache-2.0 — Авторское право 2025 ResQ Software
Generate an RL-optimized drone deployment and evacuation strategy.
Uses reinforcement learning models trained on thousands of simulated disasters to recommend optimal resource allocation, routing, and risk parameters for a specific incident or pre-alert.
Args: incident_id: Incident identifier (INC-XXX) or pre-alert ID (PRE-XXX) to generate strategy for.
Returns: OptimizationStrategy: Complete strategy recommendation with: - strategy_id: Unique identifier - related_alert_id: Original incident/alert ID - recommended_deployment: Drone type counts - evacuation_routes: Prioritized route list - estimated_success_rate: Predicted success (0.0-1.0) - simulation_proof_url: NeoFS evidence link
Example: >>> strategy = await get_deployment_strategy("PRE-ABC123") >>> print(strategy.strategy_id) >>> print(strategy.recommended_deployment) # {"surveillance": 2, ...} >>> print(f"Success rate: {strategy.estimated_success_rate:.0%}")
Use Cases: - Pre-positioning drones before predicted disasters (PDIE alerts) - Active response optimization for confirmed incidents - Multi-objective optimization (speed, safety, resource efficiency) - Scenario comparison and sensitivity analysis
Integration: Strategy linked to blockchain for immutable audit trail. After approval, use update_mission_params to push to drones.
| Name | Required | Description | Default |
|---|---|---|---|
| incident_id | Yes |
| Name | Required | Description |
|---|---|---|
| strategy_id | Yes | |
| related_alert_id | No | |
| evacuation_routes | Yes | |
| simulation_proof_url | No | |
| estimated_success_rate | Yes | |
| recommended_deployment | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool uses reinforcement learning models trained on simulated disasters, generates recommendations (not direct actions), links to blockchain for audit trails, and requires approval before implementation. It doesn't mention rate limits, authentication needs, or potential side effects, leaving some gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (Args, Returns, Example, Use Cases, Integration) and front-loads the core purpose. While comprehensive, some sections could be more concise - the example shows multiple print statements that could be streamlined. Overall, most sentences earn their place by adding value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (RL-optimized strategy generation) and the presence of an output schema (which covers return values), the description is complete enough. It explains the tool's purpose, usage guidelines, behavioral context, parameter semantics, and integration workflow without needing to detail return values since those are covered by the output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must fully compensate. It provides excellent parameter semantics in the 'Args' section, explaining that incident_id accepts either incident identifiers (INC-XXX) or pre-alert IDs (PRE-XXX) and that it's used to generate a strategy for a specific incident or pre-alert. This adds crucial meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('generate', 'recommend') and resources ('RL-optimized drone deployment and evacuation strategy', 'resource allocation, routing, and risk parameters'). It distinguishes from sibling tools by focusing on strategy generation rather than simulation execution (run_simulation) or incident validation (validate_incident).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool through the 'Use Cases' section, listing four specific scenarios including pre-positioning drones, active response optimization, multi-objective optimization, and scenario comparison. It also mentions integration with other tools ('use update_mission_params to push to drones'), giving clear context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Trigger a Digital Twin physics simulation for disaster scenario modeling.
Queues a high-fidelity simulation job and returns immediately with a job ID. Clients should subscribe to the simulation resource URI for real-time progress updates and result notification.
Workflow: 1. Validate simulation request parameters 2. Generate unique simulation ID 3. Queue job to DTSOP backend (Unity/Unreal Engine) 4. Store job metadata in simulation registry 5. Return simulation ID and subscription URI 6. Background processor updates status → processing → completed 7. Client fetches results from NeoFS when completed
Args: request: SimulationRequest with: - scenario_id: Unique scenario identifier - sector_id: Geographic sector to simulate - disaster_type: Physics model (flood/wildfire/earthquake) - parameters: Scenario params (wind_speed, water_level, etc.) - priority: "standard" or "urgent" ctx: Optional FastMCP context for logging.
Returns: str: Message with simulation ID and subscription instructions: "Simulation queued with ID: SIM-XXXXXXXX. Subscribe to resq://simulations/SIM-XXXXXXXX for updates."
Example: >>> from resq_mcp.models import SimulationRequest >>> request = SimulationRequest( ... scenario_id="flood-001", ... sector_id="Sector-1", ... disaster_type="flood", ... parameters={"water_level": 2.5}, ... priority="urgent" ... ) >>> result = await run_simulation(request) >>> print(result) # "Simulation queued with ID: SIM-ABCD1234..."
Integration: Production would: - Validate request against simulation templates - Check cluster capacity and queue position - Store job in Redis with priority - Submit to Unity/Unreal Engine processing cluster - Return estimated completion time
| Name | Required | Description | Default |
|---|---|---|---|
| request | Yes | Request for high-fidelity physics simulation in digital twin. Part of DTSOP system. Triggers physics-based simulation in Unity/Unreal Engine for accurate disaster propagation modeling and strategy validation. Attributes: scenario_id: Unique scenario identifier for this simulation. sector_id: Geographic sector to simulate. disaster_type: Type of disaster to model (e.g., "flood", "wildfire"). parameters: Simulation parameters (e.g., {"wind_speed": 15.5, "water_level": 2.3}). priority: Processing priority (standard queued, urgent fast-tracked). Note: Simulations run asynchronously. Monitor progress via the returned simulation ID and resource subscription (resq://simulations/{id}). |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It thoroughly explains the tool's behavior: it queues jobs asynchronously, returns a job ID immediately, requires clients to subscribe for updates, and outlines a detailed workflow from validation to result fetching. This covers critical aspects like async processing, job tracking, and result retrieval.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections like 'Workflow,' 'Args,' 'Returns,' 'Example,' and 'Integration,' but it is overly detailed and lengthy. Some sections, such as the extensive 'Integration' details, may be unnecessary for basic tool understanding, reducing conciseness despite good organization.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of an async simulation tool with no annotations, the description is highly complete. It explains the purpose, usage, behavior, parameters, return values, and provides an example. With an output schema present, it doesn't need to detail return values extensively, and it adequately covers all necessary contextual aspects for effective tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the single parameter 'request' and its nested properties. The description adds some context by listing the attributes of SimulationRequest and providing an example, but does not significantly enhance the semantic understanding beyond what the schema provides, aligning with the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Trigger a Digital Twin physics simulation for disaster scenario modeling.' It specifies the verb 'trigger' and the resource 'simulation job,' distinguishing it from sibling tools like 'get_deployment_strategy' and 'validate_incident' by focusing on execution rather than retrieval or validation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use this tool: for queuing high-fidelity simulation jobs that run asynchronously. It mentions monitoring progress via subscription, but does not explicitly state when not to use it or compare it to alternatives like the sibling tools, which could help differentiate further.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Submit validation result for an incident report.
Used by human operators or automated validation systems (HCE) to confirm or reject incident reports before triggering full response.
Args: val: IncidentValidation with: - incident_id: ID of incident being validated - is_confirmed: True=confirmed, False=rejected/false positive - validation_source: Who/what validated (e.g., "Human-Operator") - correlated_pre_alert_id: Optional linked PDIE alert - notes: Validation reasoning and evidence
Returns: str: Confirmation message indicating action taken: "Incident {id} successfully CONFIRMED." or "Incident {id} successfully REJECTED."
Example: >>> from resq_mcp.models import IncidentValidation >>> validation = IncidentValidation( ... incident_id="INC-123", ... is_confirmed=True, ... validation_source="Human-Operator-Alice", ... notes="Confirmed via video evidence and ground reports" ... ) >>> result = await validate_incident(validation) >>> print(result) # "Incident INC-123 successfully CONFIRMED."
Workflow: 1. Edge AI detects incident (low confidence) 2. HCE cross-references with PDIE/sensors 3. If ambiguous → human review required 4. Operator submits validation via this tool 5. If confirmed → trigger response strategy 6. If rejected → log as false positive, update ML model
Audit Trail: All validations logged with timestamp, source, and reasoning for post-incident analysis and ML model refinement.
| Name | Required | Description | Default |
|---|---|---|---|
| val | Yes | Validation result after cross-referencing an incident report. Part of HCE system. Produced after comparing incident reports against PDIE predictions, sensor networks, and historical data to confirm authenticity and trigger appropriate response protocols. Attributes: incident_id: ID of the incident being validated. is_confirmed: Whether the incident is confirmed as genuine. validation_source: System or agent that performed validation (e.g., "SpoonOS-HCE-Validator", "Human-Operator"). correlated_pre_alert_id: Related PDIE pre-alert if correlation found. notes: Detailed validation reasoning and cross-reference results. Example: >>> validation = IncidentValidation( ... incident_id="INC-123", ... is_confirmed=True, ... validation_source="SpoonOS-HCE-Validator", ... notes="Confirmed via PDIE correlation and sensor data" ... ) |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it submits validation results, returns confirmation messages, logs all validations for audit trails, and triggers downstream actions (response strategy or false positive logging). However, it doesn't explicitly mention potential side effects like rate limits, authentication requirements, or error conditions, though the workflow implies some system integration.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (description, args, returns, example, workflow, audit trail) and front-loaded key information. While comprehensive, it includes some redundant details (e.g., the example partially repeats schema information) that could be trimmed. Every sentence contributes to understanding, but it's slightly verbose compared to ideal conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (validation with workflow implications), no annotations, rich input schema (100% coverage), and presence of an output schema, the description is complete. It covers purpose, usage context, parameter semantics, return values, examples, workflow integration, and audit logging. The output schema handles return value documentation, so the description appropriately focuses on operational context without duplicating structured data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the single parameter 'val' and its nested properties thoroughly. The description adds value by explaining the parameter's role ('Validation result after cross-referencing an incident report') and providing a concrete example with context. However, it doesn't add significant semantic information beyond what's in the schema descriptions, keeping it at a strong but not exceptional level.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Submit validation result for an incident report' with specific verbs ('submit validation result') and resources ('incident report'). It distinguishes from siblings (get_deployment_strategy, run_simulation) by focusing on validation rather than retrieval or simulation. The description elaborates on the validation context, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: 'Used by human operators or automated validation systems (HCE) to confirm or reject incident reports before triggering full response.' It provides a detailed workflow (steps 1-6) showing the tool's role in the incident validation process, including prerequisites (e.g., 'If ambiguous → human review required') and alternatives (e.g., 'If rejected → log as false positive'). This gives clear context for when this tool should be invoked versus other actions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v2.0.0get_deployment_strategyrun_simulationvalidate_incidentEach tool has a clearly distinct purpose with no overlap: get_deployment_strategy generates optimized drone strategies, run_simulation triggers physics simulations, and validate_incident handles incident report validation. The descriptions clearly differentiate their roles in the disaster response workflow.
All three tools follow a consistent verb_noun naming pattern (get_deployment_strategy, run_simulation, validate_incident) with clear, descriptive names that accurately reflect their functions. There are no deviations in naming conventions.
With only 3 tools, the server feels somewhat thin for a disaster response domain that includes strategy generation, simulation, and incident validation. While each tool is valuable, additional tools for mission execution, status monitoring, or data retrieval would provide more complete coverage.
The tools cover key phases of disaster response (validation, strategy generation, simulation), but there are notable gaps in mission execution and monitoring. The get_deployment_strategy description mentions using update_mission_params to push to drones, but this tool is not included, creating a workflow dead end.
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