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dpyc-oracle

by io.github.lonniev

list_services

Enumerate the live DPYC service network with self-described summaries.

Reads the member roster from the dpyc-community registry, then (when probe=True) performs a lightweight MCP handshake against each member's public service endpoint to fetch that service's own self-description and tool inventory. Nothing about the services is hardcoded here — descriptions are authored by each service, and prices come from each Operator/Authority pricing model (call the service's check_price).

Resilient by design: per-service timeout, partial results, brief caching, and a registry-only fallback when an endpoint is asleep or unreachable. A sleeping service never breaks the listing. Free, unauthenticated.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNoRole filter — "all", "operator", "authority", or "advocate".all
probeNoHandshake each endpoint for a live self-description. Set False for a fast registry-only listing (no network fan-out).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral disclosure burden and does so thoroughly. It reveals that probing performs a network handshake, that results are partial and cached, that there is a registry-only fallback for unreachable endpoints, and that the operation is free and unauthenticated.

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 organized and efficient: a one-line summary, then mechanism, resilience behavior, and cost/auth status. Every sentence adds useful information, and the most decision-relevant facts are front-loaded.

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

Completeness5/5

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

Given an output schema, low parameter count, and full schema coverage, the description is complete. It explains the discovery mechanism, self-description source, pricing behavior, failure resilience, caching, and access requirements, leaving no practical gap for an agent selecting or invoking the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so both parameters are already documented in the schema. The description adds meaning beyond that by explaining the behavioral difference between probe=True and probe=False, including the fast registry-only fallback, which helps an agent choose correctly.

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 opens with a specific verb and resource: 'Enumerate the live DPYC service network with self-described summaries.' It clearly distinguishes this from sibling tools like lookup_member or resolve_service by describing a network-wide, probe-based listing rather than a single-service lookup.

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 clearly establishes the tool's context and includes actionable guidance for the probe parameter, such as 'Set False for a fast registry-only listing (no network fan-out).' However, it does not explicitly mention alternative sibling tools or state when not to use this tool, so it falls short of full exclusionary guidance.

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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TDQS

A3.6/5.0
Disambiguation4/5

Most tools target a distinct resource or lifecycle step: role-specific registrations, campaign tools, relay tools, and doc/status tools are clearly separated. Minor overlap exists between lookup_member/resolve_service and about/get_rulebook, but the descriptions are detailed enough to route an agent correctly.

Naming Consistency4/5

Almost all tool names use lowercase snake_case with a verb_noun structure (register_*, get_*, list_*, update_*, report_*). A few noun-phrase doc/status tools like about, economic_model, network_advisory, and service_status deviate, but the overall pattern remains readable and predictable.

Tool Count2/5

At 30 tools, this surface is heavy and exceeds the 25-tool threshold, especially for what is mostly a registry/oracle service. Several tools are explicitly not yet implemented, and the count would benefit from deferring those stubs or consolidating related doc/resolution tools.

Completeness3/5

Core areas are covered: member lookup, role registration, operator updates, campaigns, relays, governance docs, and status/advisory queries. However, some workflows are stubs (ban elections, citizen renouncement) and lifecycle gaps remain for authority/advocate updates or deregistration, plus no ban-election status/result tool exists.

Resources