context-aware-mcp

smart_api_call

Translates natural language queries into executable API calls, routing them to the appropriate microservice endpoint (os-order) in local or QA environments.

Instructions

Intelligent API routing from natural language queries. Available services: os-order. Environments: local, qa.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "smart_api_call"
  }
}

Input Schema

Name	Required	Description
`body`	No	Request body for POST/PUT
`query`	Yes	Natural language query
`headers`	No	Additional headers
`autoExecute`	No	Auto-execute detected call

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 20, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description bears full responsibility for disclosing behavior. It only mentions routing and available environments, but fails to state that the tool may execute API calls (autoExecute), any side effects, authentication needs, or failure modes. This is a significant gap for an 'intelligent' routing tool.

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 two short sentences with no filler. It front-loads the primary purpose and then adds concrete scope information (services, environments). It is efficient and well-structured, though it could include more useful detail without becoming bloated.

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

Completeness2/5

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

This is a complex tool with multiple parameters, nested objects, and no output schema. The description omits essential context such as how the natural language query is interpreted, the behavior of autoExecute, what response the caller gets, and error handling. An agent would struggle to use this correctly with the given information.

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?

Schema description coverage is 100%, so all four parameters have descriptions in the schema. The tool description adds no additional parameter semantics beyond the schema, placing it at the baseline of 3. It neither clarifies nor contradicts parameter usage.

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 states the tool's core function: intelligent API routing from natural language queries. It also specifies available services and environments, which gives a concrete scope. However, it doesn't fully clarify whether the tool executes the call itself or just constructs one, leaving some ambiguity for an agent.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus its siblings like call_api or list_services. It doesn't mention any prerequisites, exclusions, or specific scenarios, so an agent would have to infer when this is the right choice.

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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