@afferens/mcp-server

afferens_verify

Fetch two live perception probes with a delay, compare raw payloads, and validate freshness to confirm real-time data integrity.

Instructions

Fetch the live perception feed twice with a short delay, then return both raw payloads plus a hash-based freshness check. Use this as a demo proof bundle when you need to show the feed is not placeholder data.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "afferens_verify"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Max number of perception events to fetch on each probe (1-10, default 3).
`wait_ms`	No	Delay between the two probes in milliseconds (default 2000).
`modality`	No	Optional sensory modality to constrain the verification probe.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Jul 29, 2026

TDQS

A3.9/5.0

Behavior3/5

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

With no annotations, the description must disclose all behavioral traits. It explains the double fetch, delay, and hash check, but omits side effects (e.g., read-only nature), error handling, or operational constraints (e.g., feed availability). Some transparency is provided, but gaps remain.

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 extremely concise: two sentences that front-load the action and the use case. Every sentence earns its place with no wasted words.

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

Completeness3/5

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

The tool has 3 optional parameters and no output schema. The description explains what is returned (raw payloads + hash freshness check), but does not detail the output format or structure, leaving some incompleteness for an agent invoking 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?

Schema coverage is 100% with well-described parameters (limit, wait_ms, modality). The description adds no extra parameter meaning, so it meets the baseline of 3 without adding value 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 the tool fetches the live perception feed twice with a delay, returns both raw payloads, and includes a hash-based freshness check. It distinguishes from siblings by specifying the double probe and verification purpose, setting it apart from single perception or demo 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 explicitly says 'when you need to show the feed is not placeholder data,' providing clear context. However, it does not mention when not to use this tool (e.g., for single-shot reads) or compare to siblings like afferens_perceive, missing some exclusions.

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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