Multi-provider AI agent bridge for Claude. Dynamically register and execute AI agents from multiple providers (OpenAI, Anthropic, Bedrock, Gemini) through a unified MCP interface with support for sequential, iterative, and parallel pipeline execution modes.
Server has 8 tools with complete input schemas and descriptions, but quality is inconsistent. Tool names follow verb_noun convention (register_, list_, update_, remove_, execute_, set_, get_). Descriptions are present but generic (avg ~80 chars, below 194-char baseline). Parameters have types and descriptions, but lack constraints (enums, ranges, patterns). Output schemas are partially documented in code but not formally declared. Error handling returns success/error flags but lacks recovery guidance. No tool annotations (readOnlyHint, destructiveHint). update_agent accepts **kwargs without schema visibility, violating parameter clarity. Schemas are visible in source but not formally registered with MCP decorators.
Execute a single agent.
Execute a pipeline with multiple agents.
Get summary of all registered models and their capabilities for Claude.
List all registered agents.
Register a new AI agent.
Remove an agent.
Update runtime safety configuration for pipeline execution.
update_agent accepts **kwargs without formal schema. Parameters to update are not declared, violating parameter clarity and making it impossible for LLMs to know what fields can be modified.
No tool annotations (readOnlyHint, destructiveHint, idempotentHint). Destructive tools like remove_agent and write tools like register_agent lack explicit risk markers, forcing LLMs to infer intent from names alone.
Error responses return generic {success: false, error: str(e)} without recovery guidance. LLMs cannot determine if errors are retryable, user-fixable, or fatal.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 51 | <=2025-11-25 | v2 |
Update an existing agent.
Parameter descriptions lack constraints. 'provider' accepts free-form strings instead of enum (openai_compat|anthropic|bedrock|gemini). 'temperature' lacks range validation (0.0-2.0 stated but not enforced). 'mode' in execute_pipeline lacks enum constraint.
Output schemas not formally documented. Tool descriptions state 'Returns: ...' in docstrings but MCP tool registration does not include output schema declarations, limiting LLM planning.