Loading ToolBench
Model Context Protocol server for coordinating UAV swarm intelligence with context aggregation, message routing, and real-time swarm synchronization
This MCP server has critical definition quality gaps. Tool names lack clarity and proper verb-noun structure. Descriptions are present but generic and lack actionable detail. Input schemas show only high-level object types without detailed parameter documentation. No output schemas are documented. The server appears to be a custom Python implementation rather than a standard MCP server following the official spec. Tools handle swarm context coordination but lack the specificity and LLM-optimization needed for reliable agent interaction.
Handle heartbeat from client - updates client heartbeat timestamp to maintain connection health monitoring
Periodically update and broadcast context with adaptive frequency based on critical events - broadcasts aggregated context to all connected clients at 5Hz default or 20Hz during critical phases
Handle context query from client - retrieves context relevant to the querying client and returns aggregated swarm context
Handle client registration - registers a new client with the MCP server and sends registration confirmation
Handle context update from client - receives context updates, updates context manager, aggregates and broadcasts context to all connected clients
No detailed input parameter schemas: All 5 tools declare parameters as 'message' typed only as 'object' without field-level definitions. ContextMessage structure is referenced but never defined in the source. This forces LLMs to guess at required fields.
No documented output schemas: None of the 5 tools specify what they return, what fields are in the response, or what type aggregated/broadcast context has. LLMs cannot plan downstream tool chains or extract needed data.
Vague and generic descriptions: 4 of 5 tool descriptions lack actionable detail. 'heartbeat' has only 22 characters; 'update_context' mentions 'aggregates and broadcasts' without explaining side effects or preconditions. Descriptions do not answer: What does it do? When should the LLM call it? What could go wrong?
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 25 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 24 | - | v1 |
Ambiguous naming: 'update_context', 'query_context' are generic, LLMs cannot distinguish them from similar tools without reading descriptions. 'periodic_context_broadcast' combines two concerns (scheduling + broadcasting) and violates single-responsibility principle.
Parameter descriptions absent: Input parameters ('client_id', 'message') lack descriptions explaining their purpose or constraints. No guidance on client_id format, message structure, or what fields ContextMessage should contain.
No error handling guidance: Descriptions do not indicate what errors can occur (invalid client_id, malformed message, connection timeout) or what the LLM should do if a call fails. No recovery paths documented.
Missing parameter enums and constraints: 'context_type' and 'message_type' are referenced in descriptions but no enum values are provided. LLMs will hallucinate valid values, causing failures.