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Helious market intelligence

Plan an integration

get_build_plan
Read-onlyIdempotent

What to build with Helious and how: for a stated goal, the tools to call in order, what each returns, what it costs in credits at your cadence, and which plan covers it. Use this when someone is building an integration, a bot, a dashboard or a strategy, before the first data call.

Input Schema

NameRequiredDescriptionDefault
goalNoWhat the person is building, in their words, like a bot that trades CPI, a macro regime dashboard or a CPI forecast. Empty returns the three things people build, in short form.
clientNoThe client or stack it runs in, like claude, chatgpt, cursor or a script.
cadenceNoHow often it will run, like every 15 minutes, hourly, daily, every trading day or weekly. Prices the month; daily when empty.
languageNopython, javascript or another language, to point at the right sample.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), so the description only needs added context. It does add real substance: the plan includes ordered tool calls, per-tool returns, credit cost priced at the caller's cadence, and plan coverage. It doesn't mention whether the plan is cached or how cost is estimated, but the output content is well disclosed for a tool with no output schema.

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?

One dense sentence, front-loaded with the deliverable and followed by the usage trigger. Every clause (ordered tools, returns, credit cost, plan coverage) earns its place, though the opening phrase "What to build with Helious and how" is slightly awkward and the sentence is long.

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

Completeness4/5

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

All four parameters are optional and there is no output schema, so the description must explain what comes back, which it does by enumerating the plan's contents. Combined with the rich schema descriptions, an agent has enough to invoke it correctly; only caching/estimation details are missing.

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 the schema already explains each parameter, including the non-obvious empty-value behaviors for goal and cadence. The description adds no parameter-level meaning beyond that, so baseline 3 applies.

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?

States a specific verb and deliverable: for a stated goal it returns the tools to call in order, what each returns, credit cost at your cadence, and plan coverage. This clearly separates it from the data-fetch siblings, and 'before the first data call' positions it as the planning step ahead of them.

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?

Explicit when-to-use: "Use this when someone is building an integration, a bot, a dashboard or a strategy, before the first data call." That gives both the trigger and the sequencing relative to data calls. It stops short of naming an alternative or a when-not condition, but for a unique meta-tool that gap is small.

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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