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Verify a server live

live_verify

Verifies generated API-to-MCP servers by running all read operations against the live platform and validating responses against output schemas, without calling write tools.

Instructions

Start the entry over stdio (platform-mcp-hub serve --entry, Python and TypeScript) and call every READ tool against the real platform (search -> get by an id from the search -> page 2, a bad id), validating each result against the vocabulary's output schema; write tools are never called. plan sets arguments for this platform: {"args": {verb: {...}}, "config": {field: value}, "ids": {verb: id}, "skip": {verb: why}, "no_page2": why}. record: true writes the live_check block into the entry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
langNoboth
planNo
egressNo
recordNo
categoryYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior4/5

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

Annotations declare readOnlyHint=false and openWorldHint=true; the description adds real behavioral context beyond them: it spawns the entry over stdio, hits the real platform, never calls write tools, validates results against the output schema, and record:true writes a live_check block into the entry. This explains why the operation is not read-only despite not calling write tools. Missing details on failure modes or runtime cost.

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?

Dense but front-loaded: the core procedure comes first, then the plan/record modifiers. The inline plan JSON earns its space by defining an otherwise opaque parameter, though the sentence structure is packed.

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?

For a complex, open-world verification tool with no output schema and no annotation depth on permissions, the description covers the procedure but omits meaning for half the parameters and any mention of failure/report behavior. Adequate but with clear gaps.

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

Parameters2/5

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

Schema coverage is 0% across 6 parameters, so the description carries the full burden. It documents `plan` (with its args/config/ids/skip/no_page2 shape) and `record` well, but leaves category, id, lang, and egress completely unexplained in both schema and description.

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 gives a specific verb (verify/run live) and resource (the entry/server), and spells out the exact procedure: start the entry over stdio, call every READ tool against the real platform, validate against the output schema. It does not explicitly differentiate itself from close siblings like test_server or try_tool, which keeps it out of 5 territory.

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

Usage Guidelines3/5

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

Usage is implied by the name and the procedural description, and one boundary is stated ('write tools are never called'). However there is no explicit guidance on when to choose this over test_server, try_tool, or doctor, leaving the agent to infer the correct sibling.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.